davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
freshpearYoon/vr_train_free_10	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: filename dtype: string - name: NumOfUtterance dtype: int64 - name: text dtype: string - name: samplingrate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: speaker_id dtype: string - name: directory dtype: string splits: - name: train num_bytes: 7143134749 num_examples: 10000 download_size: 1080163057 dataset_size: 7143134749 configs: - config_name: default data_files: - split: train path: data/train-* ---
liuyanchen1015/MULTI_VALUE_stsb_got_gotten	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 450 num_examples: 2 - name: test num_bytes: 72 num_examples: 1 - name: train num_bytes: 274 num_examples: 1 download_size: 9256 dataset_size: 796 --- # Dataset Card for "MULTI_VALUE_stsb_got_gotten" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
benchan79/github-issues	--- dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: labels list: - name: id dtype: int64 - name: node_id dtype: string - name: url dtype: string - name: name dtype: string - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: assignees list: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: milestone struct: - name: url dtype: string - name: html_url dtype: string - name: labels_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: description dtype: string - name: creator struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: open_issues dtype: int64 - name: closed_issues dtype: int64 - name: state dtype: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: due_on dtype: 'null' - name: closed_at dtype: 'null' - name: comments sequence: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: active_lock_reason dtype: 'null' - name: body dtype: string - name: reactions struct: - name: url dtype: string - name: total_count dtype: int64 - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: laugh dtype: int64 - name: hooray dtype: int64 - name: confused dtype: int64 - name: heart dtype: int64 - name: rocket dtype: int64 - name: eyes dtype: int64 - name: timeline_url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: draft dtype: bool - name: pull_request struct: - name: url dtype: string - name: html_url dtype: string - name: diff_url dtype: string - name: patch_url dtype: string - name: merged_at dtype: timestamp[s] - name: is_pull_request dtype: bool splits: - name: train num_bytes: 15437002 num_examples: 3100 download_size: 4434085 dataset_size: 15437002 annotations_creators: - no-annotation language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: Hugging Face GitHub Issues size_categories: - unknown source_datasets: - original tags: [] task_categories: - text-classification - text-retrieval task_ids: - multi-class-classification - multi-label-classification - document-retrieval --- # Dataset Card for "Hugging Face GitHub Issues ## Dataset Description - Point of Contact: [Ben Chan](benchan79@gmail.com) ### Dataset Summary GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond. ### Supported Tasks and Leaderboards ### Languages English ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Citation Information ### Contributions
DeepPavlov/verbalist_prompts	--- configs: - config_name: default data_files: - split: dim_oasst_en path: data/dim_oasst_en-* - split: dim_oasst_ru path: data/dim_oasst_ru-* - split: dim_lima path: data/dim_lima-* - split: dim_logic_tasks_ru path: data/dim_logic_tasks_ru-* - split: dim_wikihow_en path: data/dim_wikihow_en-* - split: dim_wikihow_ru path: data/dim_wikihow_ru-* - split: dim_essayforum_writing_prompts_6k path: data/dim_essayforum_writing_prompts_6k-* - split: dim_sharegpt_short_ru path: data/dim_sharegpt_short_ru-* - split: dim_openreview_prompts_65 path: data/dim_openreview_prompts_65-* - split: dim_roleplay_instruct_v2_final path: data/dim_roleplay_instruct_v2_final-* - split: dim_kinomania_scripts path: data/dim_kinomania_scripts-* - split: dim_bugurt_thread_prompts path: data/dim_bugurt_thread_prompts-* - split: dim_russian_lyrics_prompts path: data/dim_russian_lyrics_prompts-* - split: dim_ru_instruct_gpt4 path: data/dim_ru_instruct_gpt4-* - split: dim_gpt_roleplay_realm path: data/dim_gpt_roleplay_realm-* - split: dim_ultrachat_ru path: data/dim_ultrachat_ru-* - split: dim_scitldr path: data/dim_scitldr-* - split: dim_linux_man_pages_tldr_summarized path: data/dim_linux_man_pages_tldr_summarized-* - split: dim_dolphin_ru_3k path: data/dim_dolphin_ru_3k-* - split: dim_runne_prompts path: data/dim_runne_prompts-* - split: dim_lurk_prompts path: data/dim_lurk_prompts-* - split: dim_panorama_prompts_10k path: data/dim_panorama_prompts_10k-* - split: dim_resh_edu_short_prompts path: data/dim_resh_edu_short_prompts-* - split: dim_databricks_dolly_15k_ru path: data/dim_databricks_dolly_15k_ru-* - split: dim_databricks_dolly_15k_en path: data/dim_databricks_dolly_15k_en-* - split: dim_grammarly_coedit path: data/dim_grammarly_coedit-* - split: dim_kinopoisk_prompts path: data/dim_kinopoisk_prompts-* - split: dim_medical_qa_ru_prompts path: data/dim_medical_qa_ru_prompts-* - split: dim_joke_explaination_prompts path: data/dim_joke_explaination_prompts-* - split: dim_oa_stackexchange_200k path: data/dim_oa_stackexchange_200k-* - split: dim_scale_helpful_no_math path: data/dim_scale_helpful_no_math-* - split: dim_law_stackexchange_prompts path: data/dim_law_stackexchange_prompts-* - split: dim_ficbook_prompts_best_10k path: data/dim_ficbook_prompts_best_10k-* - split: dim_azbyka_logic_ru path: data/dim_azbyka_logic_ru-* - split: dim_povarenok path: data/dim_povarenok-* - split: dim_AO3_fandom_chatbot_1to1 path: data/dim_AO3_fandom_chatbot_1to1-* - split: dim_habr_prompts_5k path: data/dim_habr_prompts_5k-* - split: dim_what_where_when_50k path: data/dim_what_where_when_50k-* - split: dim_competition_math path: data/dim_competition_math-* - split: dim_sharegpt_short_en_30k path: data/dim_sharegpt_short_en_30k-* - split: dim_ru_turbo_alpaca_evol_instruct path: data/dim_ru_turbo_alpaca_evol_instruct-* - split: dim_ru_turbo_saiga path: data/dim_ru_turbo_saiga-* - split: dim_bugurt_completion_prompts path: data/dim_bugurt_completion_prompts-* - split: dim_tldr_17_50k path: data/dim_tldr_17_50k-* - split: dim_grade_school_math_instructions path: data/dim_grade_school_math_instructions-* - split: dim_tldr_news path: data/dim_tldr_news-* - split: dim_grade_school_math_instructions_ru path: data/dim_grade_school_math_instructions_ru-* - split: dim_dialogsum path: data/dim_dialogsum-* - split: dim_HC3_ru path: data/dim_HC3_ru-* - split: dim_horoscopes_ru_10k path: data/dim_horoscopes_ru_10k-* - split: dim_yandex_q_200k path: data/dim_yandex_q_200k-* - split: dim_leetcodesolutions_en_2k path: data/dim_leetcodesolutions_en_2k-* - split: dim_forum_uristov_rf_prompts path: data/dim_forum_uristov_rf_prompts-* - split: dim_dialogsum_ru path: data/dim_dialogsum_ru-* - split: dim_huggingartists_prompts path: data/dim_huggingartists_prompts-* dataset_info: features: - name: conversation_text sequence: string splits: - name: dim_oasst_en num_bytes: 4335500 num_examples: 2289 - name: dim_oasst_ru num_bytes: 6206378 num_examples: 2220 - name: dim_lima num_bytes: 2892267 num_examples: 1030 - name: dim_logic_tasks_ru num_bytes: 76915 num_examples: 86 - name: dim_wikihow_en num_bytes: 16008199 num_examples: 1995 - name: dim_wikihow_ru num_bytes: 24451573 num_examples: 2058 - name: dim_essayforum_writing_prompts_6k num_bytes: 22326330 num_examples: 6361 - name: dim_sharegpt_short_ru num_bytes: 808319 num_examples: 253 - name: dim_openreview_prompts_65 num_bytes: 6739952 num_examples: 150 - name: dim_roleplay_instruct_v2_final num_bytes: 4389286 num_examples: 7188 - name: dim_kinomania_scripts num_bytes: 238731 num_examples: 27 - name: dim_bugurt_thread_prompts num_bytes: 302191 num_examples: 223 - name: dim_russian_lyrics_prompts num_bytes: 18676 num_examples: 43 - name: dim_ru_instruct_gpt4 num_bytes: 18351658 num_examples: 14222 - name: dim_gpt_roleplay_realm num_bytes: 20163429 num_examples: 8700 - name: dim_ultrachat_ru num_bytes: 4495105 num_examples: 500 - name: dim_scitldr num_bytes: 4049209 num_examples: 3229 - name: dim_linux_man_pages_tldr_summarized num_bytes: 3006631 num_examples: 481 - name: dim_dolphin_ru_3k num_bytes: 7976776 num_examples: 3000 - name: dim_runne_prompts num_bytes: 2686148 num_examples: 537 - name: dim_lurk_prompts num_bytes: 92012533 num_examples: 5671 - name: dim_panorama_prompts_10k num_bytes: 28964132 num_examples: 11024 - name: dim_resh_edu_short_prompts num_bytes: 12380000 num_examples: 2106 - name: dim_databricks_dolly_15k_ru num_bytes: 21900617 num_examples: 14914 - name: dim_databricks_dolly_15k_en num_bytes: 11973713 num_examples: 15011 - name: dim_grammarly_coedit num_bytes: 18500223 num_examples: 82466 - name: dim_kinopoisk_prompts num_bytes: 136323982 num_examples: 36591 - name: dim_medical_qa_ru_prompts num_bytes: 75634717 num_examples: 80101 - name: dim_joke_explaination_prompts num_bytes: 196224 num_examples: 364 - name: dim_oa_stackexchange_200k num_bytes: 192535277 num_examples: 200000 - name: dim_scale_helpful_no_math num_bytes: 85610911 num_examples: 17095 - name: dim_law_stackexchange_prompts num_bytes: 64544963 num_examples: 24343 - name: dim_ficbook_prompts_best_10k num_bytes: 75867114 num_examples: 10000 - name: dim_azbyka_logic_ru num_bytes: 173101 num_examples: 480 - name: dim_povarenok num_bytes: 93518909 num_examples: 46500 - name: dim_AO3_fandom_chatbot_1to1 num_bytes: 1162058 num_examples: 614 - name: dim_habr_prompts_5k num_bytes: 40224997 num_examples: 5000 - name: dim_what_where_when_50k num_bytes: 38385243 num_examples: 50000 - name: dim_competition_math num_bytes: 5808689 num_examples: 7500 - name: dim_sharegpt_short_en_30k num_bytes: 86599862 num_examples: 29597 - name: dim_ru_turbo_alpaca_evol_instruct num_bytes: 105340901 num_examples: 47793 - name: dim_ru_turbo_saiga num_bytes: 79875722 num_examples: 37699 - name: dim_bugurt_completion_prompts num_bytes: 5471066 num_examples: 5000 - name: dim_tldr_17_50k num_bytes: 81185070 num_examples: 50000 - name: dim_grade_school_math_instructions num_bytes: 4655452 num_examples: 8792 - name: dim_tldr_news num_bytes: 4014718 num_examples: 7138 - name: dim_grade_school_math_instructions_ru num_bytes: 6845510 num_examples: 7473 - name: dim_dialogsum num_bytes: 11176807 num_examples: 12460 - name: dim_HC3_ru num_bytes: 43395731 num_examples: 24322 - name: dim_horoscopes_ru_10k num_bytes: 9489348 num_examples: 10000 - name: dim_yandex_q_200k num_bytes: 292443135 num_examples: 200000 - name: dim_leetcodesolutions_en_2k num_bytes: 4708692 num_examples: 2048 - name: dim_forum_uristov_rf_prompts num_bytes: 2757263 num_examples: 1849 - name: dim_dialogsum_ru num_bytes: 18657989 num_examples: 12460 - name: dim_huggingartists_prompts num_bytes: 121909835 num_examples: 64006 download_size: 0 dataset_size: 2023767777 language: - ru - en --- # Verbalist (буквоед) - русскоязычный ассистент. Проект во многом вдохновленный [Saiga](https://huggingface.co/IlyaGusev/saiga2_7b_lora). Мною были собраны все самые качественные датасеты с [huggingface.datasets](https://huggingface.co/datasets), а также собраны дополнительно с тех сайтов, которые я посчитал весьма полезными для создания аналога ChatGPT. Лицензии у всех датасетов отличаются, какие-то по типу [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) были созданы специально для обучения подобных моделей, какие-то являются прямой выгрузкой диалогов с ChatGPT ([RyokoAI/ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K)). Вклад данного репозитория состоит в систематизации и стандартизации уже имеющихся датасетов, добавлении новых. А также тренировке моделей на этих данных. - [google sheets таблица с датасетами и описанием](https://docs.google.com/spreadsheets/d/10xcsINF_c_zUZchT8p-8xIuHDgcuwg63jjl2ortBP9I/edit?usp=sharing) ### Датасеты - [Объединенный датасет где все данные уже подготовлены для тренировки диалоговой модели](https://huggingface.co/datasets/dim/verbalist_prompts) \|name \|link \|description \|original_name \|original_source \|preparation_script \|language\|amount_examples\|mean_llama_tokens\|std \|min_llama_tokens\|25% \|50% \|75% \|max_llama_tokens\| \|-------------------------------------\|---------------------------------------------------------------------\|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|--------------------------------------------------------------\|-------------------------------------------------------------------------------------------------------\|---------------------------------------------------------------------------------------------------------\|--------\|---------------\|-----------------\|-----------\|----------------\|-------\|-------\|-------\|----------------\| \|dim/oasst_en \|https://huggingface.co/datasets/dim/oasst_en \|OpenAssistant Conversations Dataset на английском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: https://docs.google.com/spreadsheets/d/117t5-Tr-dxdODpyFBkBg5R8GklYBlsvBfeDyjqwz2pA/edit?usp=sharing\|2023-04-12_oasst_ready.messages.jsonl.gz \|https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/2023-04-12_oasst_ready.messages.jsonl.gz\|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oasst \|en \|2289 \|468.6788991 \|295.0864391\|17 \|264 \|410 \|618 \|2332 \| \|dim/oasst_ru \|https://huggingface.co/datasets/dim/oasst_ru \|OpenAssistant Conversations Dataset на русском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: https://docs.google.com/spreadsheets/d/1uiOnqxiytuxrB6u6q2pMSdnMfqjT3arfg8DlT-OWlb0/edit?usp=sharing \|2023-04-12_oasst_ready.messages.jsonl.gz \|https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/2023-04-12_oasst_ready.messages.jsonl.gz\|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oasst \|ru \|2220 \|589.6112613 \|479.835392 \|7 \|278 \|465 \|763.5 \|5028 \| \|dim/lima \|https://huggingface.co/datasets/dim/lima \|Данный датасет включает в себя 1000 высококачественных обучающих примеров на английском языке. Он собран из различных источников, включая Stack Exchange (STEM), Stack Exchange (Other), wikiHow, Pushshift r/WritingPrompts, Natural Instructions, а также уникальные инструкции, созданные авторами статей. Более подробную информацию о датасете можно найти в [соответствующей статье](https://arxiv.org/pdf/2305.11206.pdf). \|GAIR/lima \|https://huggingface.co/datasets/GAIR/lima \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/lima \|en \|1030 \|712.9456311 \|671.179319 \|29 \|312.75 \|488.5 \|825 \|3920 \| \|dim/logic_tasks_ru \|https://huggingface.co/datasets/dim/logic_tasks_ru \|Данный набор задач по логике для детей взят с веб-сайта https://www.potehechas.ru/zadachi/zadachi.shtml. \|Логические задачи - Логика и нестандартное мышление \|https://www.potehechas.ru/zadachi/zadachi.shtml \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/logic_tasks_ru \|ru \|86 \|193.0697674 \|76.69048422\|58 \|133.75 \|185 \|243.5 \|432 \| \|dim/wikihow_en \|https://huggingface.co/datasets/dim/wikihow_en \|Данный датасет содержит англоязычные статьи, извлеченные с веб-сайта Wikihow. \|0x22almostEvil/multilingual-wikihow-qa-16k \|https://huggingface.co/datasets/0x22almostEvil/multilingual-wikihow-qa-16k \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/wiki_how \|en \|1995 \|2037.86416 \|870.1910713\|265 \|1463 \|1913 \|2461.5 \|8988 \| \|dim/wikihow_ru \|https://huggingface.co/datasets/dim/wikihow_ru \|Данный датасет включает в себя русскоязычные статьи, полученные с веб-сайта Wikihow. \|0x22almostEvil/multilingual-wikihow-qa-16k \|https://huggingface.co/datasets/0x22almostEvil/multilingual-wikihow-qa-16k \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/wiki_how \|ru \|2058 \|2498.119534 \|1587.851549\|139 \|1236.25\|2264 \|3421.75\|10217 \| \|dim/essayforum_writing_prompts_6k \|https://huggingface.co/datasets/dim/essayforum_writing_prompts_6k \|Данный датасет включает в себя запросы на помощь с написанием небольших эссе, размещенные на данном сайте. Ответы в датасете предоставлены исключительно главным администратором сайта. Его ответы были отобраны, поскольку чаще всего они являются наиболее качественными и вдумчивыми. \|EssayForum \|https://essayforum.com/writing/ \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/essayforum \|en \|6361 \|783.1760729 \|285.4314176\|258 \|629 \|742 \|879 \|4966 \| \|dim/sharegpt_short_ru \|https://huggingface.co/datasets/dim/sharegpt_short_ru \|Очищенная версия русская версия sharegpt. Я попытался вырезать из текста все промпты, где модель извиняется что что-то не может сделать, что она не имеет доступа в интернет. Диалоги, которые противоречат морали модели я просто исключил. Постарался убрать упоминания о том что она модель AI, так как за ролеплейные характеристики отвечают другие датасеты. \|RyokoAI/ShareGPT52K \|https://huggingface.co/datasets/RyokoAI/ShareGPT52K \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/sharegpt \|ru \|253 \|706.6521739 \|494.7437584\|13 \|310 \|628 \|1078 \|1861 \| \|dim/openreview_prompts_65 \|https://huggingface.co/datasets/dim/openreview_prompts_65 \|Датасет рецензий на реальные научные статьи с сайта openreview. Вышло на самом деле не так много, так как многие статьи не выложенны на arxiv или просто не имеют рецензий. Плюс я собрал только малую часть данного сайта, а не все что там было. \|https://openreview.net/ \|https://openreview.net/ \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/openreview \|en \|150 \|13531.51333 \|6966.623686\|4893 \|8279 \|12648.5\|15833.5\|41494 \| \|dim/roleplay_instruct_v2_final \|https://huggingface.co/datasets/dim/roleplay_instruct_v2_final \|Датасет ролеплея от GPT-4 на различных персонажей на английском языке. \|roleplay-instruct-v2-final \|https://github.com/teknium1/GPTeacher \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/gpt_roleplay_realm \|en \|7188 \|155.1413467 \|97.71215667\|14 \|88 \|125 \|192 \|1291 \| \|dim/kinomania_scripts \|https://huggingface.co/datasets/dim/kinomania_scripts \|Небольшой датасет, который содержит в себе сценарии фильмов целиком и их краткое содержание \|https://www.kinomania.ru/scripts \|https://www.kinomania.ru/scripts \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/kinomania_scripts \|ru\en \|27 \|2603.407407 \|510.375447 \|1887 \|2175 \|2370 \|3069 \|3616 \| \|dim/bugurt_thread_prompts \|https://huggingface.co/datasets/dim/bugurt_thread_prompts \|Небольшой набор размеченных бугуртов вместе с моим другом, для того чтобы модель научилась писать бугурты на конкретную ситуацию. Собраны из телеграм паблика БУГУРТ ТРЕД(https://t.me/bugurtthread) \|https://t.me/bugurtthread \|https://t.me/bugurtthread \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/bugurt_thread \|ru \|223 \|334.4529148 \|271.2557988\|48 \|148.5 \|254 \|434.5 \|1645 \| \|dim/russian_lyrics_prompts \|https://huggingface.co/datasets/dim/russian_lyrics_prompts \|Небольшой датасет промптов собранный мною из различных учебников по стихосложению, чтобы модель научилась писать стихи, используя необходимый литературный прием на конкретную тему. \|Учебник стихосложения \|https://stihi.ru/uchebnik/ \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/russian_lyrics_prompts \|ru \|43 \|106.1395349 \|71.00220701\|45 \|71 \|83 \|96.5 \|411 \| \|dim/ru_instruct_gpt4 \|https://huggingface.co/datasets/dim/ru_instruct_gpt4 \|Датасет каких-то инструкций на русском сгенерированных GPT-4 \|lksy/ru_instruct_gpt4 \|https://huggingface.co/datasets/lksy/ru_instruct_gpt4 \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_instruct_gpt4 \|ru \|14222 \|259.2173393 \|237.9433891\|16 \|109 \|175 \|271 \|1374 \| \|dim/gpt_roleplay_realm \|https://huggingface.co/datasets/dim/gpt_roleplay_realm \|Диалоги выдуманных персонажей при помощи GPT-4, диалоги были сгенерированны при помощи GPT-3.5. Русский и английский. \|IlyaGusev/gpt_roleplay_realm \|https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/gpt_roleplay_realm \|ru\en \|8700 \|504.2424138 \|117.6228987\|180 \|424 \|489 \|569 \|1207 \| \|dim/ultrachat_ru \|https://huggingface.co/datasets/dim/ultrachat_ru \|Какой-то рандомный датасет диалогов от chatgpt, который я нашел на huggingface. Из текста диалогов были вырезаны шаблонные фразы по типу: "я не могу выполнить", "как языковая модель" и тд. Потому что обычно после этого следовало вменяемое решение задачи. \|kaleinaNyan/UltraChat_ru \|https://huggingface.co/datasets/kaleinaNyan/UltraChat_ru \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ultrachat_ru \|ru \|500 \|1781.782 \|901.1212735\|267 \|1113.25\|1648 \|2250.25\|7303 \| \|dim/scitldr \|https://huggingface.co/datasets/dim/scitldr \|Саммаризация научных статей на английском языке, выполненная экспертами. \|allenai/scitldr \|https://huggingface.co/datasets/allenai/scitldr \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/scitldr \|en \|3229 \|258.748529 \|71.41209752\|60 \|209 \|252 \|303 \|689 \| \|dim/linux_man_pages_tldr_summarized \|https://huggingface.co/datasets/dim/linux_man_pages_tldr_summarized \|Саммаризация мануалов для инструментов линукс в удобный набор команд с их кратким описанием. \|tmskss/linux-man-pages-tldr-summarized \|https://huggingface.co/datasets/tmskss/linux-man-pages-tldr-summarized \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/linux-man-pages-tldr-summarized \|en \|481 \|1567.727651 \|3590.30871 \|96 \|405 \|765 \|1386 \|49888 \| \|dim/dolphin_ru_3k \|https://huggingface.co/datasets/dim/dolphin_ru_3k \|Подвыборка размера 3000 переведенных заданий dolphin. Примеры из оригинального датасета это промпты из FLANv2 и решения при помощи GPT-4 или GPT-3.5. \|d0rj/dolphin-ru \|https://huggingface.co/datasets/d0rj/dolphin-ru \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dolphin_ru \|ru \|3000 \|556.1133333 \|650.0962612\|19 \|207 \|369.5 \|720.25 \|6787 \| \|dim/runne_prompts \|https://huggingface.co/datasets/dim/runne_prompts \|Промпты составленные из датасета RuNNE. Лично я при обучении сотавил промпт следующим образом. Сначала идет текст "Найди все именованные сущности в данном тексте:", а затем шел сам текст. В качестве выхода модели нужно сгенерировать JSON где содержатся все найденные именованные сущности. К примеру так [{"name": "PERSON", "ent": "Ким Чен Нама", "pos": "0 12"}, {"name": "ORGANIZATION", "ent": "Полиция Малайзии", "pos": "56 72"}] \|iluvvatar/RuNNE \|https://huggingface.co/datasets/iluvvatar/RuNNE \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/RuNNE \|ru \|537 \|1479.750466 \|230.0259174\|581 \|1337 \|1480 \|1635 \|1988 \| \|dim/lurk_prompts \|https://huggingface.co/datasets/dim/lurk_prompts \|Набор определений различных терминов с сайта lurk. Сами промпты были составлены автоматически следующим образом. напиши определение для (ОПРЕДЕЛЕНИЕ) в стиле lurk \|averoo/lurk \|https://huggingface.co/datasets/averoo/lurk/viewer/default/train?p=2 \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/lurk \|ru \|5671 \|3450.34262 \|4147.897824\|35 \|710.5 \|2010 \|4593 \|55098 \| \|dim/panorama_prompts_10k \|https://huggingface.co/datasets/dim/panorama_prompts_10k \|Набор юмористических заголовков и текстов новостей с сайта панорама. \|its5Q/panorama \|https://huggingface.co/datasets/its5Q/panorama \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/panorama \|ru \|11024 \|516.9588171 \|191.3774023\|36 \|422 \|498 \|585 \|3496 \| \|dim/resh_edu_short_prompts \|https://huggingface.co/datasets/dim/resh_edu_short_prompts \|Набор уроков с сайта resh.edu.ru включающих в себя название урока, тему, класс и текст урока с заданиями. \|its5Q/resh-edu \|https://huggingface.co/datasets/its5Q/resh-edu \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/resh_edu \|ru \|2106 \|1431.510921 \|435.7847102\|56 \|1175.5 \|1517 \|1777 \|2029 \| \|dim/databricks_dolly_15k_ru \|https://huggingface.co/datasets/dim/databricks_dolly_15k_ru \|Переведенный датасет dolly на русский язык. Включает в себя набор инструкций на обширное количество тематик. \|dwarf2/databricks-dolly-15k-ru \|https://huggingface.co/dwarf2/databricks-dolly-15k-ru \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/databricks_dolly_15k_ru \|ru \|14914 \|305.4638595 \|405.874049 \|8 \|87 \|182 \|370 \|9268 \| \|dim/databricks_dolly_15k_en \|https://huggingface.co/datasets/dim/databricks_dolly_15k_en \|databricks-dolly-15k — это набор данных с открытым исходным кодом, содержащий записи о выполнении инструкций, созданные тысячами сотрудников Databricks в нескольких поведенческих категориях, изложенных в документе InstructGPT, включая мозговой штурм, классификацию, закрытый контроль качества, генерацию, извлечение информации, открытый контроль качества и обобщение. \|databricks/databricks-dolly-15k \|https://huggingface.co/datasets/databricks/databricks-dolly-15k \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/databricks_dolly_15k_en \|en \|15011 \|204.7264006 \|302.5539423\|6 \|57 \|119 \|242 \|8883 \| \|dim/grammarly_coedit \|https://huggingface.co/datasets/dim/grammarly_coedit \|Набор промптов, которые просят исправить грамматические, стилистические ошибки на английском. \|grammarly/coedit \|https://huggingface.co/datasets/grammarly/coedit \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grammarly_coedit \|en \|82466 \|53.7128271 \|26.73822864\|10 \|35 \|46 \|64 \|694 \| \|dim/kinopoisk_prompts \|https://huggingface.co/datasets/dim/kinopoisk_prompts \|Отзывы с кинопоиска на топ 250 фильмов. В промптах я прошу написать хороший, плохой или нейтральный отзыв на определенный фильм. \|blinoff/kinopoisk \|https://huggingface.co/datasets/blinoff/kinopoisk \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/kinopoisk \|ru \|36591 \|875.0955973 \|565.3212035\|48 \|484 \|733 \|1117 \|8628 \| \|dim/medical_qa_ru_prompts \|https://huggingface.co/datasets/dim/medical_qa_ru_prompts \|Какие-то вопросы и ответы с какого-то медицинского форума. В данной версии датасета только первый ответ из оригинала. \|blinoff/medical_qa_ru_data \|https://huggingface.co/datasets/blinoff/medical_qa_ru_data \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/medical_qa_ru_data \|ru \|80101 \|206.710528 \|175.4343973\|12 \|106 \|161 \|247 \|5062 \| \|dim/joke_explaination_prompts \|https://huggingface.co/datasets/dim/joke_explaination_prompts \|Объяснение шуток на английском. От изначального датасета отличается тем, что я убрал последнее предложение из объяснения, так как оно ссылается на видео на сайте. \|theblackcat102/joke_explaination \|https://huggingface.co/datasets/theblackcat102/joke_explaination \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/joke_explaination \|en \|364 \|143.5741758 \|68.90275411\|21 \|99 \|137.5 \|189.25 \|334 \| \|dim/oa_stackexchange_200k \|https://huggingface.co/datasets/dim/oa_stackexchange_200k \|Вопросы-ответы со stackexchange. Оригинальный датасет был составлен следующим образом: были выбраны только темы с принятым ответом, для которых длина вопроса и ответа составляет менее 1000 символов. Другие ответы, вопросы без принятых ответов или длинные записи были удалены. Так как оригинальный датасет слишком большой, я рандомно выбрал 200k семплов. \|donfu/oa-stackexchange \|https://huggingface.co/datasets/donfu/oa-stackexchange \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oa_stackexchange \|en \|200000 \|276.29862 \|112.5004436\|22 \|194 \|265 \|345 \|1226 \| \|dim/scale_helpful_no_math \|https://huggingface.co/datasets/dim/scale_helpful_no_math \|Какой-то набор диалогов с вопросами-ответами на английском, происхождение неизвестно. \|HuggingFaceH4/scale_helpful_no_math \|https://huggingface.co/datasets/HuggingFaceH4/scale_helpful_no_math/viewer/default/train_rm \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/scale_helpful_no_math \|en \|17095 \|1235.302603 \|838.1097885\|53 \|663 \|1063 \|1617 \|34480 \| \|dim/law_stackexchange_prompts \|https://huggingface.co/datasets/dim/law_stackexchange_prompts \|Вопросы про закон на английском языке со StackExchange. Оригинальный датасет был преобразован в markdown. \|ymoslem/Law-StackExchange \|https://huggingface.co/datasets/ymoslem/Law-StackExchange \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/law_stackexchange \|en \|24343 \|689.1184324 \|565.0316906\|43 \|354 \|540 \|836 \|8969 \| \|dim/ficbook_prompts_best_10k \|https://huggingface.co/datasets/dim/ficbook_prompts_best_10k \|Топ 10k лучших фанфиков с сайта ficbook.net. Все промпты выглядят следующим образом: напиши фанфик с названием {title} и следующим описанием {description}, с тегами {tags}, Где title это оригинальное название, description оригинальное описание, tags это теги данного произведения. \|AlexWortega/FicBook \|https://huggingface.co/datasets/AlexWortega/FicBook \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ficbook \|ru \|10000 \|1737.8214 \|402.0748161\|166 \|1716 \|1950 \|1950 \|1952 \| \|dim/azbyka_logic_ru \|https://huggingface.co/datasets/dim/azbyka_logic_ru \|Небольшой набор детских логических и православных задач, взятых с сайта https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi . Обычно у них почти нет развернутого решения, только ответ. Я пытался расписать решение некоторых задач, но меня хватило только на 35, если кто-то займется подобным буду рад https://docs.google.com/spreadsheets/d/1JRbtppbZCUbV_Eqd0nKbRDQEuPnJIAgJ70cUILEDUI4/edit?usp=sharing . \|Логические и занимательные задачи (300 задач) \|https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/azbyka_logic_ru \|ru \|480 \|77.4375 \|77.56990416\|14 \|31 \|50 \|91 \|652 \| \|dim/povarenok \|https://huggingface.co/datasets/dim/povarenok \|46k лучших рецептов с сайта povarenok.ru, содержит текст рецепта, список ингридиентов, название блюда \|https://www.povarenok.ru/recipes/ \|https://www.povarenok.ru/recipes/ \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/povarenok \|ru \|46500 \|488.9118495 \|344.8563249\|31 \|281 \|440 \|632 \|5542 \| \|dim/AO3_fandom_chatbot_1to1 \|https://huggingface.co/datasets/dim/AO3_fandom_chatbot_1to1 \|Какой-то набор ролеплейных диалогов с описанием персонажей и их отыгрышем. Происхождение неизвестно. \|ebony59/AO3_fandom_chatbot_1to1 \|https://huggingface.co/datasets/ebony59/AO3_fandom_chatbot_1to1 \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/AO3_fandom_chatbot_1to1 \|en \|614 \|493.7166124 \|226.3885365\|129 \|328.25 \|432.5 \|611.75 \|1272 \| \|dim/habr_prompts_5k \|https://huggingface.co/datasets/dim/habr_prompts_5k \|Статьи с хабра. Датасет был составлен с помощью chatgpt, chatgpt преобразовывал заголовки таким образом чтобы они звучали как вопросы от пользователя, в качестве таргета выступала сама статья. \|IlyaGusev/habr \|https://huggingface.co/datasets/IlyaGusev/habr \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/habr \|ru \|5000 \|1732.892 \|454.8418369\|19 \|1920.75\|1950 \|1951 \|1952 \| \|dim/what_where_when_50k \|https://huggingface.co/datasets/dim/what_where_when_50k \|50k вопросов с решениями с сайта что где когда. В качестве промпта выступает вопрос, в качестве ответа конкатенация объяснения и краткого ответа. Все вопросы-ответы вы можете найти по этой ссылке https://huggingface.co/datasets/dim/what_where_when_ru \|https://db.chgk.info \|https://db.chgk.info \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/what_where_when \|ru \|50000 \|169.1862 \|68.91119898\|18 \|122 \|158 \|202 \|1167 \| \|dim/competition_math \|https://huggingface.co/datasets/dim/competition_math \|Датасет олимпиадной математики на английском. The Mathematics Aptitude Test of Heuristics (MATH) dataset. \|competition_math \|https://huggingface.co/datasets/competition_math \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/competition_math \|en \|7500 \|317.5254667 \|267.8583731\|34 \|147 \|234 \|393 \|3029 \| \|dim/sharegpt_short_en_30k \|https://huggingface.co/datasets/dim/sharegpt_short_en_30k \|Короткие диалоги на английском из sharegpt \|RyokoAI/ShareGPT52K \|https://huggingface.co/datasets/RyokoAI/ShareGPT52K \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/sharegpt \|en \|29597 \|749.3149981 \|516.3702473\|3 \|336 \|630 \|1095 \|2021 \| \|dim/ru_turbo_alpaca_evol_instruct \|https://huggingface.co/datasets/dim/ru_turbo_alpaca_evol_instruct \|Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. \|IlyaGusev/ru_turbo_alpaca_evol_instruct \|https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca_evol_instruct \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_turbo_alpaca_evol_instruct \|ru \|47793 \|453.0887996 \|289.5498356\|17 \|221 \|430 \|623 \|4647 \| \|dim/ru_turbo_saiga \|https://huggingface.co/datasets/dim/ru_turbo_saiga \|Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. \|IlyaGusev/ru_turbo_saiga \|https://huggingface.co/datasets/IlyaGusev/ru_turbo_saiga \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_turbo_saiga \|ru \|37699 \|412.7508687 \|113.346917 \|87 \|339 \|398 \|466 \|1427 \| \|dim/bugurt_completion_prompts \|https://huggingface.co/datasets/dim/bugurt_completion_prompts \|Обрезанные бугурты, где в качестве промпта используется строка вида - продолжи бугурт: первая строчка бугурта \|https://t.me/bugurtthread \|https://t.me/bugurtthread \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/bugurt_thread \|ru \|5000 \|280.2466 \|320.4353681\|32 \|111 \|178 \|331 \|11333 \| \|dim/tldr_17_50k \|https://huggingface.co/datasets/dim/tldr_17_50k \|Очень вольная абстрактная саммаризация постов с реддита в одну строчку \|webis/tldr-17 \|https://huggingface.co/datasets/webis/tldr-17 \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/tldr_17 \|en \|50000 \|421.12752 \|403.346214 \|10 \|177 \|303 \|525 \|9592 \| \|dim/grade_school_math_instructions \|https://huggingface.co/datasets/dim/grade_school_math_instructions \|OpenAI's grade-school-math датасет преобразованный в промпты. \|qwedsacf/grade-school-math-instructions \|https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grade-school-math-instructions \|en \|8792 \|171.6310282 \|63.09232668\|50 \|124 \|161 \|206 \|511 \| \|dim/tldr_news \|https://huggingface.co/datasets/dim/tldr_news \|Хедлайны и текст новостей на различную тематику. \|JulesBelveze/tldr_news \|https://huggingface.co/datasets/JulesBelveze/tldr_news \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/tldr_news \|en \|7138 \|133.1004483 \|46.48736493\|23 \|100 \|133 \|161 \|476 \| \|dim/grade_school_math_instructions_ru\|https://huggingface.co/datasets/dim/grade_school_math_instructions_ru\|OpenAI's grade-school-math датасет переведенный на русский. \|d0rj/gsm8k-ru \|https://huggingface.co/datasets/d0rj/gsm8k-ru \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grade_school_math_instructions_ru\|ru \|7473 \|259.8321959 \|100.1229127\|78 \|185 \|241 \|314 \|838 \| \|dim/dialogsum \|https://huggingface.co/datasets/dim/dialogsum \|Саммаризация диалогов на английском языке, разметка выполнялась вручную. \|knkarthick/dialogsum \|https://huggingface.co/datasets/knkarthick/dialogsum \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dialogsum \|en \|12460 \|269.6467095 \|126.285664 \|75 \|191 \|245 \|327 \|1725 \| \|dim/HC3_ru \|https://huggingface.co/datasets/dim/HC3_ru \|Вопросы-ответы с реддита, есть ответы сгенерированные chatgpt и реальные ответы пользователей. Я использовал только реальные ответы пользователей. \|d0rj/HC3-ru \|https://huggingface.co/datasets/d0rj/HC3-ru \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/HC3_ru \|ru \|24322 \|360.5608503 \|330.2285903\|15 \|168 \|267 \|435 \|10025 \| \|dim/horoscopes_ru_10k \|https://huggingface.co/datasets/dim/horoscopes_ru_10k \|10k гороскопов, с промптами где я прошу сгенерировать гороском для определенного знака зодиака \|dkagramanyan/horoscopes_ru \|https://huggingface.co/datasets/dkagramanyan/horoscopes_ru \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/horoscopes_ru \|ru \|10000 \|183.1443 \|31.62023184\|55 \|159 \|187 \|201 \|464 \| \|dim/yandex_q_200k \|https://huggingface.co/datasets/dim/yandex_q_200k \|200k рандомно выбранных вопросов-ответов с сайта yandex q. \|its5Q/yandex-q \|https://huggingface.co/datasets/its5Q/yandex-q \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/yandex_q \|ru \|200000 \|304.569005 \|340.7808288\|18 \|127 \|202 \|353 \|19294 \| \|dim/leetcodesolutions_en_2k \|https://huggingface.co/datasets/dim/leetcodesolutions_en_2k \|Решения задач с leetcode на разных языках. \|TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k \|https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/leetcodesolutions_en_2k \|en \|2048 \|740.7441406 \|253.2493282\|297 \|565 \|685 \|857 \|1960 \| \|dim/forum_uristov_rf_prompts \|https://huggingface.co/datasets/dim/forum_uristov_rf_prompts \|Вопросы-ответы с российского юридического форума. \|https://xn----dtbrojdkckkfj9k.xn--p1ai/vopros-yuristu?page=560\|https://xn----dtbrojdkckkfj9k.xn--p1ai/vopros-yuristu?page=560 \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/forum_uristov_rf \|ru \|1849 \|321.0540833 \|429.58896 \|31 \|134 \|210 \|349 \|6470 \| \|dim/dialogsum_ru \|https://huggingface.co/datasets/dim/dialogsum_ru \|Саммаризация диалогов на русском языке, перевод dialogsum. \|d0rj/dialogsum-ru \|https://huggingface.co/datasets/d0rj/dialogsum-ru \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dialogsum-ru \|ru \|12460 \|364.2813804 \|178.7117754\|98 \|250 \|329 \|446 \|2300 \| \|dim/huggingartists_prompts \|https://huggingface.co/datasets/dim/huggingartists_prompts \|Промпты, которые просят продолжить песню в стиле определенного исполнителя. В данном наборе содержатся почти все исполнители, которых вы можете найти в этой организации https://huggingface.co/huggingartists \|https://huggingface.co/huggingartists \|https://huggingface.co/huggingartists \|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/huggingartists \|ru \|64006 \|561.6732025 \|586.18458 \|28 \|297 \|453 \|720 \|32949 \| ### Модели - [Ссылка на google sheets](https://docs.google.com/spreadsheets/d/1LGCy8RBR_Yk9wHRcp0IDV8eut4D7tAQBRgqCLqxYk3E/edit?usp=sharing) \|model_name \|ru_cola(zero shot, prompt)\|russian super glue(zero shot)\|mmlu_ru(NLPCoreTeam)\|mt_bench_ru_turn_1\|mt_bench_ru_turn_2\|mt_bench_ru_avg\|mt_bench_generation\|average \| \|-----------------------------------------\|--------------------------\|-----------------------------\|--------------------\|------------------\|------------------\|---------------\|-------------------\|------------\| \|IlyaGusev/saiga_mistral_7b_lora \|0.710082526 \|0.64 \|0.4848747094 \|0.6567901 \|0.53375 \|0.59527005 \| \|0.6050994671\| \|verbalist_7b_v9_800 \|0.6709723717 \|0.665 \|0.4801731633 \|0.6175 \|0.4375 \|0.4375 \| \|0.574229107 \| \|Open-Orca/Mistral-7B-OpenOrca \|0.6917832795 \|0.652 \|0.4592925739 \|0.685 \|0.595 \|0.64 \| \|0.6166151707\| \|mistral-open-orca-ru-4600-step \|0.6928597058 \|0.663 \|0.4347 \|0.71625 \|0.546 \|0.631125 \| \|0.6105619412\| \|verbalist_v10_1650 \|0.7201291712 \|0.66 \|0.4920804261 \|0.56125 \|0.5 \|0.530625 \| \|0.5866919194\| \|gpt-3.5-turbo \|0.72 \|0.682 \| \|0.87 \|0.745 \|0.8075 \| \|0.75425 \| \|openchat/openchat_3.5 \|0.6727664155 \|0.642 \| \| \| \|#DIV/0! \| \|0.6573832078\| \|dim/tiny-llama-2T-open-orca-ru-10000-step\|0.6361679225 \|0.451 \|0.2999564271 \| \| \| \| \|0.4623747832\| - [dim/mistral-open-orca-ru-4600-step](https://huggingface.co/dim/mistral-open-orca-ru-4600-step) - [dim/verbalist_7b_v9_800](https://huggingface.co/dim/verbalist_7b_v9_800) - [dim/verbalist_v10_1650](https://huggingface.co/dim/verbalist_v10_1650) ### Код обучения - [общий алгоритм обучения](https://github.com/dmitrymailk/verbalist/blob/master/verbalist/model/src/train.py) - [формирование датасетов для обучения](https://github.com/dmitrymailk/verbalist/blob/master/verbalist/model/src/dataset.py#L176) ### Оборудование Все обучение и инференс производится на видеокарте A100, на других видеокартах была обнаружена существенная деградация качества при инференсе, данный аспект требует дополнительного изучения. - NVIDIA A100-SXM4-40GB - NVIDIA-SMI 535.54.03 - Driver Version: 535.54.03 - CUDA Version: 12.2 - torch==2.0.1+cu118 ### Дальнейшее развитие Самое простое, что можно сделать это переводить уже имеющиеся хорошие датасеты с английского на русский при помощи GPT-4. Более сложное это собирать больше разнообразных данных из различных доменов. Я могу лишь подкинуть идеи для того какие датасеты можно собрать еще. - решебники по литературе, русскому и другим предметам - задания со всяких бирж труда - [краткие пересказы произведений, анализ произведений, сочинения по ним](http://www.litra.ru/shortwork/) - [туториалы с digital ocean (более 7000)](https://www.digitalocean.com/community/tutorials) - [туториалы с selectel](https://selectel.ru/blog/tutorials/) - больше форумов на различные тематики - [бесплатные эссе с ivypanda essays](https://ivypanda.com/essays/) и дальнейший их перевод на русский - больше стихов и песен - [олимпиадные русские задачи](https://math.ru/problems/) их очень сложно собирать, так как большинство их них живут только в PDF или docx. Но их довольно много и они довольно отличаются от олимпиадной математики на английском. Но у меня нет времени этим заниматься. - фанфики на иностранном языке - исправить текущие автоматические промпты на более разнообразные, при помощи chatgpt
open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16	--- pretty_name: Evaluation run of ConvexAI/Harmony-4x7B-bf16 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ConvexAI/Harmony-4x7B-bf16](https://huggingface.co/ConvexAI/Harmony-4x7B-bf16)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-02T02:08:05.844408](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16/blob/main/results_2024-02-02T02-08-05.844408.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6519815241664966,\n\ \ \"acc_stderr\": 0.03207888973753625,\n \"acc_norm\": 0.6516491536204881,\n\ \ \"acc_norm_stderr\": 0.03274551770079104,\n \"mc1\": 0.4602203182374541,\n\ \ \"mc1_stderr\": 0.01744801722396088,\n \"mc2\": 0.6205565135867297,\n\ \ \"mc2_stderr\": 0.015134580676846265\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.658703071672355,\n \"acc_stderr\": 0.013855831287497724,\n\ \ \"acc_norm\": 0.6834470989761092,\n \"acc_norm_stderr\": 0.013592431519068077\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6810396335391357,\n\ \ \"acc_stderr\": 0.004651211311633843,\n \"acc_norm\": 0.8674566819358693,\n\ \ \"acc_norm_stderr\": 0.0033838751726700243\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.037150621549989056,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.037150621549989056\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"\ acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7709677419354839,\n\ \ \"acc_stderr\": 0.02390491431178265,\n \"acc_norm\": 0.7709677419354839,\n\ \ \"acc_norm_stderr\": 0.02390491431178265\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.37777777777777777,\n \"acc_stderr\": 0.02956070739246572,\n \ \ \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.02956070739246572\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.02983796238829193,\n \ \ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.02983796238829193\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"\ acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474086,\n \"\ acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474086\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.034465133507525995,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.034465133507525995\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281376\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608304,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608304\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4011173184357542,\n\ \ \"acc_stderr\": 0.01639222189940708,\n \"acc_norm\": 0.4011173184357542,\n\ \ \"acc_norm_stderr\": 0.01639222189940708\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292452,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292452\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46870925684485004,\n\ \ \"acc_stderr\": 0.01274520462608314,\n \"acc_norm\": 0.46870925684485004,\n\ \ \"acc_norm_stderr\": 0.01274520462608314\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6948529411764706,\n \"acc_stderr\": 0.0279715413701706,\n\ \ \"acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.0279715413701706\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \ \ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.02553843336857833,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.02553843336857833\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4602203182374541,\n\ \ \"mc1_stderr\": 0.01744801722396088,\n \"mc2\": 0.6205565135867297,\n\ \ \"mc2_stderr\": 0.015134580676846265\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.813733228097869,\n \"acc_stderr\": 0.01094187795567621\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7210007581501138,\n \ \ \"acc_stderr\": 0.012354115779970308\n }\n}\n```" repo_url: https://huggingface.co/ConvexAI/Harmony-4x7B-bf16 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|arc:challenge\|25_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-02T02-08-05.844408.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|gsm8k\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hellaswag\|10_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T02-08-05.844408.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-02T02-08-05.844408.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '/details_harness\|winogrande\|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-02T02-08-05.844408.parquet' - config_name: results data_files: - split: 2024_02_02T02_08_05.844408 path: - results_2024-02-02T02-08-05.844408.parquet - split: latest path: - results_2024-02-02T02-08-05.844408.parquet --- # Dataset Card for Evaluation run of ConvexAI/Harmony-4x7B-bf16 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ConvexAI/Harmony-4x7B-bf16](https://huggingface.co/ConvexAI/Harmony-4x7B-bf16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-02T02:08:05.844408](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16/blob/main/results_2024-02-02T02-08-05.844408.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6519815241664966, "acc_stderr": 0.03207888973753625, "acc_norm": 0.6516491536204881, "acc_norm_stderr": 0.03274551770079104, "mc1": 0.4602203182374541, "mc1_stderr": 0.01744801722396088, "mc2": 0.6205565135867297, "mc2_stderr": 0.015134580676846265 }, "harness\|arc:challenge\|25": { "acc": 0.658703071672355, "acc_stderr": 0.013855831287497724, "acc_norm": 0.6834470989761092, "acc_norm_stderr": 0.013592431519068077 }, "harness\|hellaswag\|10": { "acc": 0.6810396335391357, "acc_stderr": 0.004651211311633843, "acc_norm": 0.8674566819358693, "acc_norm_stderr": 0.0033838751726700243 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7039473684210527, "acc_stderr": 0.037150621549989056, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.037150621549989056 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909282, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909282 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.025487187147859375, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.025487187147859375 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7709677419354839, "acc_stderr": 0.02390491431178265, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.02390491431178265 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175008, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175008 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033456, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033456 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402534, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402534 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.37777777777777777, "acc_stderr": 0.02956070739246572, "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.02956070739246572 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6974789915966386, "acc_stderr": 0.02983796238829193, "acc_norm": 0.6974789915966386, "acc_norm_stderr": 0.02983796238829193 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5370370370370371, "acc_stderr": 0.03400603625538272, "acc_norm": 0.5370370370370371, "acc_norm_stderr": 0.03400603625538272 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8186274509803921, "acc_stderr": 0.027044621719474086, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.027044621719474086 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.02595502084162113, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.02595502084162113 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.034465133507525995, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.034465133507525995 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.047184714852195886, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.047184714852195886 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281376, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281376 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608304, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608304 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4011173184357542, "acc_stderr": 0.01639222189940708, "acc_norm": 0.4011173184357542, "acc_norm_stderr": 0.01639222189940708 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292452, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292452 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7530864197530864, "acc_stderr": 0.02399350170904211, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.02399350170904211 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46870925684485004, "acc_stderr": 0.01274520462608314, "acc_norm": 0.46870925684485004, "acc_norm_stderr": 0.01274520462608314 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6948529411764706, "acc_stderr": 0.0279715413701706, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.0279715413701706 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6830065359477124, "acc_stderr": 0.018824219512706207, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.018824219512706207 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.845771144278607, "acc_stderr": 0.02553843336857833, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.02553843336857833 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4602203182374541, "mc1_stderr": 0.01744801722396088, "mc2": 0.6205565135867297, "mc2_stderr": 0.015134580676846265 }, "harness\|winogrande\|5": { "acc": 0.813733228097869, "acc_stderr": 0.01094187795567621 }, "harness\|gsm8k\|5": { "acc": 0.7210007581501138, "acc_stderr": 0.012354115779970308 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. 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SEACrowd/id_abusive	--- tags: - sentiment-analysis language: - ind --- # id_abusive The ID_ABUSIVE dataset is collection of 2,016 informal abusive tweets in Indonesian language, designed for sentiment analysis NLP task. This dataset is crawled from Twitter, and then filtered and labelled manually by 20 volunteer annotators. The dataset labelled into three labels namely not abusive language, abusive but not offensive, and offensive language. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @article{IBROHIM2018222, title = {A Dataset and Preliminaries Study for Abusive Language Detection in Indonesian Social Media}, journal = {Procedia Computer Science}, volume = {135}, pages = {222-229}, year = {2018}, note = {The 3rd International Conference on Computer Science and Computational Intelligence (ICCSCI 2018) : Empowering Smart Technology in Digital Era for a Better Life}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2018.08.169}, url = {https://www.sciencedirect.com/science/article/pii/S1877050918314583}, author = {Muhammad Okky Ibrohim and Indra Budi}, keywords = {abusive language, twitter, machine learning}, abstract = {Abusive language is an expression (both oral or text) that contains abusive/dirty words or phrases both in the context of jokes, a vulgar sex conservation or to cursing someone. Nowadays many people on the internet (netizens) write and post an abusive language in the social media such as Facebook, Line, Twitter, etc. Detecting an abusive language in social media is a difficult problem to resolve because this problem can not be resolved just use word matching. This paper discusses a preliminaries study for abusive language detection in Indonesian social media and the challenge in developing a system for Indonesian abusive language detection, especially in social media. We also built reported an experiment for abusive language detection on Indonesian tweet using machine learning approach with a simple word n-gram and char n-gram features. We use Naive Bayes, Support Vector Machine, and Random Forest Decision Tree classifier to identify the tweet whether the tweet is a not abusive language, abusive but not offensive, or offensive language. The experiment results show that the Naive Bayes classifier with the combination of word unigram + bigrams features gives the best result i.e. 70.06% of F1 - Score. However, if we classifying the tweet into two labels only (not abusive language and abusive language), all classifier that we used gives a higher result (more than 83% of F1 - Score for every classifier). The dataset in this experiment is available for other researchers that interest to improved this study.} } ``` ## License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International ## Homepage [https://www.sciencedirect.com/science/article/pii/S1877050918314583](https://www.sciencedirect.com/science/article/pii/S1877050918314583) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF	--- pretty_name: Evaluation run of FPHam/Sydney_Overthinker_13b_HF dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FPHam/Sydney_Overthinker_13b_HF](https://huggingface.co/FPHam/Sydney_Overthinker_13b_HF)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-08T02:51:52.068469](https://huggingface.co/datasets/open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF/blob/main/results_2023-12-08T02-51-52.068469.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5136287950246363,\n\ \ \"acc_stderr\": 0.034087739996992605,\n \"acc_norm\": 0.5191163704808761,\n\ \ \"acc_norm_stderr\": 0.03483348555557295,\n \"mc1\": 0.2998776009791922,\n\ \ \"mc1_stderr\": 0.01604035296671362,\n \"mc2\": 0.45697851910783077,\n\ \ \"mc2_stderr\": 0.015427158150833389\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5614334470989761,\n \"acc_stderr\": 0.014500682618212865,\n\ \ \"acc_norm\": 0.5895904436860068,\n \"acc_norm_stderr\": 0.014374922192642664\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6118303126867158,\n\ \ \"acc_stderr\": 0.004863375698153865,\n \"acc_norm\": 0.8085042820155347,\n\ \ \"acc_norm_stderr\": 0.0039267405951797715\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4888888888888889,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04063302731486671,\n\ \ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04063302731486671\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5132075471698113,\n \"acc_stderr\": 0.030762134874500476,\n\ \ \"acc_norm\": 0.5132075471698113,\n \"acc_norm_stderr\": 0.030762134874500476\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5972222222222222,\n\ \ \"acc_stderr\": 0.04101405519842425,\n \"acc_norm\": 0.5972222222222222,\n\ \ \"acc_norm_stderr\": 0.04101405519842425\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n\ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.4797687861271676,\n\ \ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n\ \ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.0433643270799318,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.0433643270799318\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n\ \ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2631578947368421,\n\ \ \"acc_stderr\": 0.041424397194893624,\n \"acc_norm\": 0.2631578947368421,\n\ \ \"acc_norm_stderr\": 0.041424397194893624\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2962962962962963,\n \"acc_stderr\": 0.023517294335963286,\n \"\ acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.023517294335963286\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.35714285714285715,\n\ \ \"acc_stderr\": 0.042857142857142816,\n \"acc_norm\": 0.35714285714285715,\n\ \ \"acc_norm_stderr\": 0.042857142857142816\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5870967741935483,\n\ \ \"acc_stderr\": 0.028009138125400387,\n \"acc_norm\": 0.5870967741935483,\n\ \ \"acc_norm_stderr\": 0.028009138125400387\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.3399014778325123,\n \"acc_stderr\": 0.033327690684107895,\n\ \ \"acc_norm\": 0.3399014778325123,\n \"acc_norm_stderr\": 0.033327690684107895\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6181818181818182,\n \"acc_stderr\": 0.03793713171165634,\n\ \ \"acc_norm\": 0.6181818181818182,\n \"acc_norm_stderr\": 0.03793713171165634\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6616161616161617,\n \"acc_stderr\": 0.033711241426263014,\n \"\ acc_norm\": 0.6616161616161617,\n \"acc_norm_stderr\": 0.033711241426263014\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7409326424870466,\n \"acc_stderr\": 0.0316187791793541,\n\ \ \"acc_norm\": 0.7409326424870466,\n \"acc_norm_stderr\": 0.0316187791793541\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5230769230769231,\n \"acc_stderr\": 0.025323990861736232,\n\ \ \"acc_norm\": 0.5230769230769231,\n \"acc_norm_stderr\": 0.025323990861736232\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \ \ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.46218487394957986,\n \"acc_stderr\": 0.0323854694875898,\n \ \ \"acc_norm\": 0.46218487394957986,\n \"acc_norm_stderr\": 0.0323854694875898\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.25165562913907286,\n \"acc_stderr\": 0.035433042343899844,\n \"\ acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.035433042343899844\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.6862385321100918,\n \"acc_stderr\": 0.019894723341469116,\n \"\ acc_norm\": 0.6862385321100918,\n \"acc_norm_stderr\": 0.019894723341469116\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.33796296296296297,\n \"acc_stderr\": 0.03225941352631295,\n \"\ acc_norm\": 0.33796296296296297,\n \"acc_norm_stderr\": 0.03225941352631295\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.6323529411764706,\n \"acc_stderr\": 0.03384132045674118,\n \"\ acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.03384132045674118\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.6708860759493671,\n \"acc_stderr\": 0.030587326294702368,\n \ \ \"acc_norm\": 0.6708860759493671,\n \"acc_norm_stderr\": 0.030587326294702368\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6412556053811659,\n\ \ \"acc_stderr\": 0.03219079200419995,\n \"acc_norm\": 0.6412556053811659,\n\ \ \"acc_norm_stderr\": 0.03219079200419995\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\ \ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.04139112727635463,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.04139112727635463\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.5705521472392638,\n \"acc_stderr\": 0.03889066619112722,\n\ \ \"acc_norm\": 0.5705521472392638,\n \"acc_norm_stderr\": 0.03889066619112722\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6796116504854369,\n \"acc_stderr\": 0.04620284082280041,\n\ \ \"acc_norm\": 0.6796116504854369,\n \"acc_norm_stderr\": 0.04620284082280041\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7948717948717948,\n\ \ \"acc_stderr\": 0.026453508054040335,\n \"acc_norm\": 0.7948717948717948,\n\ \ \"acc_norm_stderr\": 0.026453508054040335\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.0498887651569859,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n\ \ \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7203065134099617,\n\ \ \"acc_stderr\": 0.016050792148036522,\n \"acc_norm\": 0.7203065134099617,\n\ \ \"acc_norm_stderr\": 0.016050792148036522\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5809248554913294,\n \"acc_stderr\": 0.026564178111422622,\n\ \ \"acc_norm\": 0.5809248554913294,\n \"acc_norm_stderr\": 0.026564178111422622\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.32737430167597764,\n\ \ \"acc_stderr\": 0.015694238967737383,\n \"acc_norm\": 0.32737430167597764,\n\ \ \"acc_norm_stderr\": 0.015694238967737383\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.028491993586171566,\n\ \ \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.028491993586171566\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6045016077170418,\n\ \ \"acc_stderr\": 0.02777091853142784,\n \"acc_norm\": 0.6045016077170418,\n\ \ \"acc_norm_stderr\": 0.02777091853142784\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6080246913580247,\n \"acc_stderr\": 0.027163686038271146,\n\ \ \"acc_norm\": 0.6080246913580247,\n \"acc_norm_stderr\": 0.027163686038271146\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3723404255319149,\n \"acc_stderr\": 0.02883892147125146,\n \ \ \"acc_norm\": 0.3723404255319149,\n \"acc_norm_stderr\": 0.02883892147125146\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3963494132985658,\n\ \ \"acc_stderr\": 0.012492830452095217,\n \"acc_norm\": 0.3963494132985658,\n\ \ \"acc_norm_stderr\": 0.012492830452095217\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.03016191193076711,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.03016191193076711\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5163398692810458,\n \"acc_stderr\": 0.02021703065318646,\n \ \ \"acc_norm\": 0.5163398692810458,\n \"acc_norm_stderr\": 0.02021703065318646\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.04673752333670239,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.04673752333670239\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6081632653061224,\n \"acc_stderr\": 0.031251275910891656,\n\ \ \"acc_norm\": 0.6081632653061224,\n \"acc_norm_stderr\": 0.031251275910891656\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6467661691542289,\n\ \ \"acc_stderr\": 0.03379790611796777,\n \"acc_norm\": 0.6467661691542289,\n\ \ \"acc_norm_stderr\": 0.03379790611796777\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.42168674698795183,\n\ \ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n\ \ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7134502923976608,\n \"acc_stderr\": 0.03467826685703826,\n\ \ \"acc_norm\": 0.7134502923976608,\n \"acc_norm_stderr\": 0.03467826685703826\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2998776009791922,\n\ \ \"mc1_stderr\": 0.01604035296671362,\n \"mc2\": 0.45697851910783077,\n\ \ \"mc2_stderr\": 0.015427158150833389\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.739542225730071,\n \"acc_stderr\": 0.012334833671998297\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.18877937831690675,\n \ \ \"acc_stderr\": 0.010779262837202751\n }\n}\n```" repo_url: https://huggingface.co/FPHam/Sydney_Overthinker_13b_HF leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|arc:challenge\|25_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-08T02-51-52.068469.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|gsm8k\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hellaswag\|10_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-08T02-51-52.068469.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-08T02-51-52.068469.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '/details_harness\|winogrande\|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-08T02-51-52.068469.parquet' - config_name: results data_files: - split: 2023_12_08T02_51_52.068469 path: - results_2023-12-08T02-51-52.068469.parquet - split: latest path: - results_2023-12-08T02-51-52.068469.parquet --- # Dataset Card for Evaluation run of FPHam/Sydney_Overthinker_13b_HF ## Dataset Description - Homepage: - Repository: https://huggingface.co/FPHam/Sydney_Overthinker_13b_HF - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [FPHam/Sydney_Overthinker_13b_HF](https://huggingface.co/FPHam/Sydney_Overthinker_13b_HF) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-08T02:51:52.068469](https://huggingface.co/datasets/open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF/blob/main/results_2023-12-08T02-51-52.068469.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5136287950246363, "acc_stderr": 0.034087739996992605, "acc_norm": 0.5191163704808761, "acc_norm_stderr": 0.03483348555557295, "mc1": 0.2998776009791922, "mc1_stderr": 0.01604035296671362, "mc2": 0.45697851910783077, "mc2_stderr": 0.015427158150833389 }, "harness\|arc:challenge\|25": { "acc": 0.5614334470989761, "acc_stderr": 0.014500682618212865, "acc_norm": 0.5895904436860068, "acc_norm_stderr": 0.014374922192642664 }, "harness\|hellaswag\|10": { "acc": 0.6118303126867158, "acc_stderr": 0.004863375698153865, "acc_norm": 0.8085042820155347, "acc_norm_stderr": 0.0039267405951797715 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4888888888888889, "acc_stderr": 0.04318275491977976, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.04318275491977976 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04063302731486671, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04063302731486671 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5132075471698113, "acc_stderr": 0.030762134874500476, "acc_norm": 0.5132075471698113, "acc_norm_stderr": 0.030762134874500476 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5972222222222222, "acc_stderr": 0.04101405519842425, "acc_norm": 0.5972222222222222, "acc_norm_stderr": 0.04101405519842425 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.4797687861271676, "acc_stderr": 0.03809342081273957, "acc_norm": 0.4797687861271676, "acc_norm_stderr": 0.03809342081273957 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.0433643270799318, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.0433643270799318 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4553191489361702, "acc_stderr": 0.03255525359340355, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.03255525359340355 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2631578947368421, "acc_stderr": 0.041424397194893624, "acc_norm": 0.2631578947368421, "acc_norm_stderr": 0.041424397194893624 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.023517294335963286, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.023517294335963286 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.042857142857142816, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.042857142857142816 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5870967741935483, "acc_stderr": 0.028009138125400387, "acc_norm": 0.5870967741935483, "acc_norm_stderr": 0.028009138125400387 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3399014778325123, "acc_stderr": 0.033327690684107895, "acc_norm": 0.3399014778325123, "acc_norm_stderr": 0.033327690684107895 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6181818181818182, "acc_stderr": 0.03793713171165634, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.03793713171165634 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6616161616161617, "acc_stderr": 0.033711241426263014, "acc_norm": 0.6616161616161617, "acc_norm_stderr": 0.033711241426263014 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7409326424870466, "acc_stderr": 0.0316187791793541, "acc_norm": 0.7409326424870466, "acc_norm_stderr": 0.0316187791793541 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5230769230769231, "acc_stderr": 0.025323990861736232, "acc_norm": 0.5230769230769231, "acc_norm_stderr": 0.025323990861736232 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.28888888888888886, "acc_stderr": 0.027634907264178544, "acc_norm": 0.28888888888888886, "acc_norm_stderr": 0.027634907264178544 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.46218487394957986, "acc_stderr": 0.0323854694875898, "acc_norm": 0.46218487394957986, "acc_norm_stderr": 0.0323854694875898 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.25165562913907286, "acc_stderr": 0.035433042343899844, "acc_norm": 0.25165562913907286, "acc_norm_stderr": 0.035433042343899844 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6862385321100918, "acc_stderr": 0.019894723341469116, "acc_norm": 0.6862385321100918, "acc_norm_stderr": 0.019894723341469116 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.33796296296296297, "acc_stderr": 0.03225941352631295, "acc_norm": 0.33796296296296297, "acc_norm_stderr": 0.03225941352631295 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6323529411764706, "acc_stderr": 0.03384132045674118, "acc_norm": 0.6323529411764706, "acc_norm_stderr": 0.03384132045674118 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6708860759493671, "acc_stderr": 0.030587326294702368, "acc_norm": 0.6708860759493671, "acc_norm_stderr": 0.030587326294702368 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6412556053811659, "acc_stderr": 0.03219079200419995, "acc_norm": 0.6412556053811659, "acc_norm_stderr": 0.03219079200419995 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5954198473282443, "acc_stderr": 0.043046937953806645, "acc_norm": 0.5954198473282443, "acc_norm_stderr": 0.043046937953806645 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7107438016528925, "acc_stderr": 0.04139112727635463, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.04139112727635463 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497751, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497751 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5705521472392638, "acc_stderr": 0.03889066619112722, "acc_norm": 0.5705521472392638, "acc_norm_stderr": 0.03889066619112722 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6796116504854369, "acc_stderr": 0.04620284082280041, "acc_norm": 0.6796116504854369, "acc_norm_stderr": 0.04620284082280041 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7948717948717948, "acc_stderr": 0.026453508054040335, "acc_norm": 0.7948717948717948, "acc_norm_stderr": 0.026453508054040335 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.56, "acc_stderr": 0.0498887651569859, "acc_norm": 0.56, "acc_norm_stderr": 0.0498887651569859 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7203065134099617, "acc_stderr": 0.016050792148036522, "acc_norm": 0.7203065134099617, "acc_norm_stderr": 0.016050792148036522 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5809248554913294, "acc_stderr": 0.026564178111422622, "acc_norm": 0.5809248554913294, "acc_norm_stderr": 0.026564178111422622 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.32737430167597764, "acc_stderr": 0.015694238967737383, "acc_norm": 0.32737430167597764, "acc_norm_stderr": 0.015694238967737383 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5490196078431373, "acc_stderr": 0.028491993586171566, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.028491993586171566 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6045016077170418, "acc_stderr": 0.02777091853142784, "acc_norm": 0.6045016077170418, "acc_norm_stderr": 0.02777091853142784 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6080246913580247, "acc_stderr": 0.027163686038271146, "acc_norm": 0.6080246913580247, "acc_norm_stderr": 0.027163686038271146 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3723404255319149, "acc_stderr": 0.02883892147125146, "acc_norm": 0.3723404255319149, "acc_norm_stderr": 0.02883892147125146 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3963494132985658, "acc_stderr": 0.012492830452095217, "acc_norm": 0.3963494132985658, "acc_norm_stderr": 0.012492830452095217 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.03016191193076711, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.03016191193076711 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5163398692810458, "acc_stderr": 0.02021703065318646, "acc_norm": 0.5163398692810458, "acc_norm_stderr": 0.02021703065318646 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6090909090909091, "acc_stderr": 0.04673752333670239, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.04673752333670239 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6081632653061224, "acc_stderr": 0.031251275910891656, "acc_norm": 0.6081632653061224, "acc_norm_stderr": 0.031251275910891656 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6467661691542289, "acc_stderr": 0.03379790611796777, "acc_norm": 0.6467661691542289, "acc_norm_stderr": 0.03379790611796777 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7134502923976608, "acc_stderr": 0.03467826685703826, "acc_norm": 0.7134502923976608, "acc_norm_stderr": 0.03467826685703826 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2998776009791922, "mc1_stderr": 0.01604035296671362, "mc2": 0.45697851910783077, "mc2_stderr": 0.015427158150833389 }, "harness\|winogrande\|5": { "acc": 0.739542225730071, "acc_stderr": 0.012334833671998297 }, "harness\|gsm8k\|5": { "acc": 0.18877937831690675, "acc_stderr": 0.010779262837202751 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
abwqr/michael_scott	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 79558.0 num_examples: 12 download_size: 0 dataset_size: 79558.0 --- # Dataset Card for "michael_scott" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shahidul034/text_summarization_dataset6	--- dataset_info: features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 129713026 num_examples: 113562 download_size: 44838616 dataset_size: 129713026 --- # Dataset Card for "text_summarization_dataset6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
parksimon0808/prm800k-llama-generator	--- dataset_info: features: - name: texts dtype: string - name: input_ids sequence: int32 - name: labels sequence: int64 - name: answers dtype: string splits: - name: train num_bytes: 107264878 num_examples: 16465 - name: test num_bytes: 4635493 num_examples: 773 download_size: 23666282 dataset_size: 111900371 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "prm800k-llama-v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MMShmBlogs/hmblogs-v3	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 45957987986 num_examples: 16896817 download_size: 21302352262 dataset_size: 45957987986 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_pmking27__PrathameshLLM-2B	--- pretty_name: Evaluation run of pmking27/PrathameshLLM-2B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [pmking27/PrathameshLLM-2B](https://huggingface.co/pmking27/PrathameshLLM-2B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_pmking27__PrathameshLLM-2B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-07T15:12:15.779927](https://huggingface.co/datasets/open-llm-leaderboard/details_pmking27__PrathameshLLM-2B/blob/main/results_2024-04-07T15-12-15.779927.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.38448642166678587,\n\ \ \"acc_stderr\": 0.03412711045658374,\n \"acc_norm\": 0.3882749361001093,\n\ \ \"acc_norm_stderr\": 0.03491709236659397,\n \"mc1\": 0.2913096695226438,\n\ \ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.44689893094855493,\n\ \ \"mc2_stderr\": 0.014502506509597363\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.4257679180887372,\n \"acc_stderr\": 0.014449464278868809,\n\ \ \"acc_norm\": 0.447098976109215,\n \"acc_norm_stderr\": 0.014529380160526845\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5071698864767975,\n\ \ \"acc_stderr\": 0.00498926836296872,\n \"acc_norm\": 0.6840270862378013,\n\ \ \"acc_norm_stderr\": 0.004639520453444027\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4074074074074074,\n\ \ \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.4074074074074074,\n\ \ \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.375,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.32,\n\ \ \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \ \ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.4226415094339623,\n \"acc_stderr\": 0.03040233144576954,\n\ \ \"acc_norm\": 0.4226415094339623,\n \"acc_norm_stderr\": 0.03040233144576954\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4097222222222222,\n\ \ \"acc_stderr\": 0.04112490974670787,\n \"acc_norm\": 0.4097222222222222,\n\ \ \"acc_norm_stderr\": 0.04112490974670787\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.32947976878612717,\n\ \ \"acc_stderr\": 0.03583901754736411,\n \"acc_norm\": 0.32947976878612717,\n\ \ \"acc_norm_stderr\": 0.03583901754736411\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.039505818611799616,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.039505818611799616\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.37446808510638296,\n \"acc_stderr\": 0.03163910665367291,\n\ \ \"acc_norm\": 0.37446808510638296,\n \"acc_norm_stderr\": 0.03163910665367291\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2894736842105263,\n\ \ \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n\ \ \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708617,\n \"\ acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708617\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.4290322580645161,\n\ \ \"acc_stderr\": 0.02815603653823321,\n \"acc_norm\": 0.4290322580645161,\n\ \ \"acc_norm_stderr\": 0.02815603653823321\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.33497536945812806,\n \"acc_stderr\": 0.033208527423483104,\n\ \ \"acc_norm\": 0.33497536945812806,\n \"acc_norm_stderr\": 0.033208527423483104\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\"\ : 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.3696969696969697,\n \"acc_stderr\": 0.03769430314512568,\n\ \ \"acc_norm\": 0.3696969696969697,\n \"acc_norm_stderr\": 0.03769430314512568\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.46464646464646464,\n \"acc_stderr\": 0.03553436368828063,\n \"\ acc_norm\": 0.46464646464646464,\n \"acc_norm_stderr\": 0.03553436368828063\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.5077720207253886,\n \"acc_stderr\": 0.03608003225569654,\n\ \ \"acc_norm\": 0.5077720207253886,\n \"acc_norm_stderr\": 0.03608003225569654\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.38461538461538464,\n \"acc_stderr\": 0.024666744915187222,\n\ \ \"acc_norm\": 0.38461538461538464,\n \"acc_norm_stderr\": 0.024666744915187222\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2518518518518518,\n \"acc_stderr\": 0.02646611753895992,\n \ \ \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.02646611753895992\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.3949579831932773,\n \"acc_stderr\": 0.03175367846096626,\n \ \ \"acc_norm\": 0.3949579831932773,\n \"acc_norm_stderr\": 0.03175367846096626\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.43853211009174314,\n \"acc_stderr\": 0.021274713073954565,\n \"\ acc_norm\": 0.43853211009174314,\n \"acc_norm_stderr\": 0.021274713073954565\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.2962962962962963,\n \"acc_stderr\": 0.031141447823536037,\n \"\ acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.031141447823536037\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.37254901960784315,\n \"acc_stderr\": 0.03393388584958404,\n \"\ acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.03393388584958404\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.4050632911392405,\n \"acc_stderr\": 0.03195514741370673,\n \ \ \"acc_norm\": 0.4050632911392405,\n \"acc_norm_stderr\": 0.03195514741370673\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.3991031390134529,\n\ \ \"acc_stderr\": 0.03286745312567961,\n \"acc_norm\": 0.3991031390134529,\n\ \ \"acc_norm_stderr\": 0.03286745312567961\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.42748091603053434,\n \"acc_stderr\": 0.04338920305792401,\n\ \ \"acc_norm\": 0.42748091603053434,\n \"acc_norm_stderr\": 0.04338920305792401\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.5454545454545454,\n \"acc_stderr\": 0.04545454545454546,\n \"\ acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.04545454545454546\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.37037037037037035,\n\ \ \"acc_stderr\": 0.04668408033024932,\n \"acc_norm\": 0.37037037037037035,\n\ \ \"acc_norm_stderr\": 0.04668408033024932\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.3619631901840491,\n \"acc_stderr\": 0.037757007291414416,\n\ \ \"acc_norm\": 0.3619631901840491,\n \"acc_norm_stderr\": 0.037757007291414416\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.38392857142857145,\n\ \ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\ \ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.4854368932038835,\n \"acc_stderr\": 0.049486373240266376,\n\ \ \"acc_norm\": 0.4854368932038835,\n \"acc_norm_stderr\": 0.049486373240266376\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.5726495726495726,\n\ \ \"acc_stderr\": 0.032408473935163266,\n \"acc_norm\": 0.5726495726495726,\n\ \ \"acc_norm_stderr\": 0.032408473935163266\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237101,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237101\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.5159642401021711,\n\ \ \"acc_stderr\": 0.01787084750608173,\n \"acc_norm\": 0.5159642401021711,\n\ \ \"acc_norm_stderr\": 0.01787084750608173\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.3786127167630058,\n \"acc_stderr\": 0.02611374936131034,\n\ \ \"acc_norm\": 0.3786127167630058,\n \"acc_norm_stderr\": 0.02611374936131034\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.25139664804469275,\n\ \ \"acc_stderr\": 0.014508979453553988,\n \"acc_norm\": 0.25139664804469275,\n\ \ \"acc_norm_stderr\": 0.014508979453553988\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.48366013071895425,\n \"acc_stderr\": 0.028614624752805407,\n\ \ \"acc_norm\": 0.48366013071895425,\n \"acc_norm_stderr\": 0.028614624752805407\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.41479099678456594,\n\ \ \"acc_stderr\": 0.027982680459759553,\n \"acc_norm\": 0.41479099678456594,\n\ \ \"acc_norm_stderr\": 0.027982680459759553\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.4382716049382716,\n \"acc_stderr\": 0.027607914087400483,\n\ \ \"acc_norm\": 0.4382716049382716,\n \"acc_norm_stderr\": 0.027607914087400483\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.28368794326241137,\n \"acc_stderr\": 0.026891709428343968,\n \ \ \"acc_norm\": 0.28368794326241137,\n \"acc_norm_stderr\": 0.026891709428343968\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3116036505867014,\n\ \ \"acc_stderr\": 0.01182903918284965,\n \"acc_norm\": 0.3116036505867014,\n\ \ \"acc_norm_stderr\": 0.01182903918284965\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.29044117647058826,\n \"acc_stderr\": 0.027576468622740522,\n\ \ \"acc_norm\": 0.29044117647058826,\n \"acc_norm_stderr\": 0.027576468622740522\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.3545751633986928,\n \"acc_stderr\": 0.0193533605475537,\n \ \ \"acc_norm\": 0.3545751633986928,\n \"acc_norm_stderr\": 0.0193533605475537\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.4090909090909091,\n\ \ \"acc_stderr\": 0.047093069786618966,\n \"acc_norm\": 0.4090909090909091,\n\ \ \"acc_norm_stderr\": 0.047093069786618966\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.4122448979591837,\n \"acc_stderr\": 0.03151236044674281,\n\ \ \"acc_norm\": 0.4122448979591837,\n \"acc_norm_stderr\": 0.03151236044674281\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.472636815920398,\n\ \ \"acc_stderr\": 0.035302355173346824,\n \"acc_norm\": 0.472636815920398,\n\ \ \"acc_norm_stderr\": 0.035302355173346824\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.3855421686746988,\n\ \ \"acc_stderr\": 0.037891344246115496,\n \"acc_norm\": 0.3855421686746988,\n\ \ \"acc_norm_stderr\": 0.037891344246115496\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.03834234744164993,\n\ \ \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.03834234744164993\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2913096695226438,\n\ \ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.44689893094855493,\n\ \ \"mc2_stderr\": 0.014502506509597363\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6511444356748224,\n \"acc_stderr\": 0.01339505932013732\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.09249431387414708,\n \ \ \"acc_stderr\": 0.007980396874560178\n }\n}\n```" repo_url: https://huggingface.co/pmking27/PrathameshLLM-2B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|arc:challenge\|25_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-07T15-12-15.779927.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|gsm8k\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hellaswag\|10_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T15-12-15.779927.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-07T15-12-15.779927.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '/details_harness\|winogrande\|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-07T15-12-15.779927.parquet' - config_name: results data_files: - split: 2024_04_07T15_12_15.779927 path: - results_2024-04-07T15-12-15.779927.parquet - split: latest path: - results_2024-04-07T15-12-15.779927.parquet --- # Dataset Card for Evaluation run of pmking27/PrathameshLLM-2B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [pmking27/PrathameshLLM-2B](https://huggingface.co/pmking27/PrathameshLLM-2B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_pmking27__PrathameshLLM-2B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-07T15:12:15.779927](https://huggingface.co/datasets/open-llm-leaderboard/details_pmking27__PrathameshLLM-2B/blob/main/results_2024-04-07T15-12-15.779927.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.38448642166678587, "acc_stderr": 0.03412711045658374, "acc_norm": 0.3882749361001093, "acc_norm_stderr": 0.03491709236659397, "mc1": 0.2913096695226438, "mc1_stderr": 0.01590598704818483, "mc2": 0.44689893094855493, "mc2_stderr": 0.014502506509597363 }, "harness\|arc:challenge\|25": { "acc": 0.4257679180887372, "acc_stderr": 0.014449464278868809, "acc_norm": 0.447098976109215, "acc_norm_stderr": 0.014529380160526845 }, "harness\|hellaswag\|10": { "acc": 0.5071698864767975, "acc_stderr": 0.00498926836296872, "acc_norm": 0.6840270862378013, "acc_norm_stderr": 0.004639520453444027 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.04244633238353228, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.04244633238353228 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.375, "acc_stderr": 0.039397364351956274, "acc_norm": 0.375, "acc_norm_stderr": 0.039397364351956274 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4226415094339623, "acc_stderr": 0.03040233144576954, "acc_norm": 0.4226415094339623, "acc_norm_stderr": 0.03040233144576954 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4097222222222222, "acc_stderr": 0.04112490974670787, "acc_norm": 0.4097222222222222, "acc_norm_stderr": 0.04112490974670787 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768078, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.32947976878612717, "acc_stderr": 0.03583901754736411, "acc_norm": 0.32947976878612717, "acc_norm_stderr": 0.03583901754736411 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.19607843137254902, "acc_stderr": 0.039505818611799616, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.039505818611799616 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.37446808510638296, "acc_stderr": 0.03163910665367291, "acc_norm": 0.37446808510638296, "acc_norm_stderr": 0.03163910665367291 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159394, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159394 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.45517241379310347, "acc_stderr": 0.04149886942192117, "acc_norm": 0.45517241379310347, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.26455026455026454, "acc_stderr": 0.022717467897708617, "acc_norm": 0.26455026455026454, "acc_norm_stderr": 0.022717467897708617 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.4290322580645161, "acc_stderr": 0.02815603653823321, "acc_norm": 0.4290322580645161, "acc_norm_stderr": 0.02815603653823321 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.33497536945812806, "acc_stderr": 0.033208527423483104, "acc_norm": 0.33497536945812806, "acc_norm_stderr": 0.033208527423483104 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.3696969696969697, "acc_stderr": 0.03769430314512568, "acc_norm": 0.3696969696969697, "acc_norm_stderr": 0.03769430314512568 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.46464646464646464, "acc_stderr": 0.03553436368828063, "acc_norm": 0.46464646464646464, "acc_norm_stderr": 0.03553436368828063 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.5077720207253886, "acc_stderr": 0.03608003225569654, "acc_norm": 0.5077720207253886, "acc_norm_stderr": 0.03608003225569654 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.38461538461538464, "acc_stderr": 0.024666744915187222, "acc_norm": 0.38461538461538464, "acc_norm_stderr": 0.024666744915187222 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2518518518518518, "acc_stderr": 0.02646611753895992, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.02646611753895992 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.3949579831932773, "acc_stderr": 0.03175367846096626, "acc_norm": 0.3949579831932773, "acc_norm_stderr": 0.03175367846096626 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.43853211009174314, "acc_stderr": 0.021274713073954565, "acc_norm": 0.43853211009174314, "acc_norm_stderr": 0.021274713073954565 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.031141447823536037, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.031141447823536037 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.03393388584958404, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.03393388584958404 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.4050632911392405, "acc_stderr": 0.03195514741370673, "acc_norm": 0.4050632911392405, "acc_norm_stderr": 0.03195514741370673 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.3991031390134529, "acc_stderr": 0.03286745312567961, "acc_norm": 0.3991031390134529, "acc_norm_stderr": 0.03286745312567961 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.42748091603053434, "acc_stderr": 0.04338920305792401, "acc_norm": 0.42748091603053434, "acc_norm_stderr": 0.04338920305792401 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.5454545454545454, "acc_stderr": 0.04545454545454546, "acc_norm": 0.5454545454545454, "acc_norm_stderr": 0.04545454545454546 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.37037037037037035, "acc_stderr": 0.04668408033024932, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.04668408033024932 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.3619631901840491, "acc_stderr": 0.037757007291414416, "acc_norm": 0.3619631901840491, "acc_norm_stderr": 0.037757007291414416 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness\|hendrycksTest-management\|5": { "acc": 0.4854368932038835, "acc_stderr": 0.049486373240266376, "acc_norm": 0.4854368932038835, "acc_norm_stderr": 0.049486373240266376 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.5726495726495726, "acc_stderr": 0.032408473935163266, "acc_norm": 0.5726495726495726, "acc_norm_stderr": 0.032408473935163266 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237101, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237101 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5159642401021711, "acc_stderr": 0.01787084750608173, "acc_norm": 0.5159642401021711, "acc_norm_stderr": 0.01787084750608173 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.3786127167630058, "acc_stderr": 0.02611374936131034, "acc_norm": 0.3786127167630058, "acc_norm_stderr": 0.02611374936131034 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.25139664804469275, "acc_stderr": 0.014508979453553988, "acc_norm": 0.25139664804469275, "acc_norm_stderr": 0.014508979453553988 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.48366013071895425, "acc_stderr": 0.028614624752805407, "acc_norm": 0.48366013071895425, "acc_norm_stderr": 0.028614624752805407 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.41479099678456594, "acc_stderr": 0.027982680459759553, "acc_norm": 0.41479099678456594, "acc_norm_stderr": 0.027982680459759553 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.4382716049382716, "acc_stderr": 0.027607914087400483, "acc_norm": 0.4382716049382716, "acc_norm_stderr": 0.027607914087400483 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.28368794326241137, "acc_stderr": 0.026891709428343968, "acc_norm": 0.28368794326241137, "acc_norm_stderr": 0.026891709428343968 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3116036505867014, "acc_stderr": 0.01182903918284965, "acc_norm": 0.3116036505867014, "acc_norm_stderr": 0.01182903918284965 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.29044117647058826, "acc_stderr": 0.027576468622740522, "acc_norm": 0.29044117647058826, "acc_norm_stderr": 0.027576468622740522 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.3545751633986928, "acc_stderr": 0.0193533605475537, "acc_norm": 0.3545751633986928, "acc_norm_stderr": 0.0193533605475537 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.4090909090909091, "acc_stderr": 0.047093069786618966, "acc_norm": 0.4090909090909091, "acc_norm_stderr": 0.047093069786618966 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.4122448979591837, "acc_stderr": 0.03151236044674281, "acc_norm": 0.4122448979591837, "acc_norm_stderr": 0.03151236044674281 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.472636815920398, "acc_stderr": 0.035302355173346824, "acc_norm": 0.472636815920398, "acc_norm_stderr": 0.035302355173346824 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3855421686746988, "acc_stderr": 0.037891344246115496, "acc_norm": 0.3855421686746988, "acc_norm_stderr": 0.037891344246115496 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.03834234744164993, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.03834234744164993 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2913096695226438, "mc1_stderr": 0.01590598704818483, "mc2": 0.44689893094855493, "mc2_stderr": 0.014502506509597363 }, "harness\|winogrande\|5": { "acc": 0.6511444356748224, "acc_stderr": 0.01339505932013732 }, "harness\|gsm8k\|5": { "acc": 0.09249431387414708, "acc_stderr": 0.007980396874560178 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
axelprsvl/my_dataset	--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 40520175.0 num_examples: 5 download_size: 40474142 dataset_size: 40520175.0 --- # Dataset Card for "my_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/wikitext-2-raw-v1-shuffled	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11018604 num_examples: 36718 - name: validation num_bytes: 1159288 num_examples: 3760 download_size: 7055354 dataset_size: 12177892 --- # Dataset Card for "wikitext-2-raw-v1-shuffled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ovior/twitter_dataset_1713165252	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2753460 num_examples: 7970 download_size: 1590780 dataset_size: 2753460 configs: - config_name: default data_files: - split: train path: data/train-* ---
guilhermebo/embeddings	--- license: creativeml-openrail-m ---
telugu_news	--- annotations_creators: - machine-generated language_creators: - other language: - te license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - language-modeling - masked-language-modeling - multi-class-classification - topic-classification pretty_name: TeluguNews dataset_info: features: - name: sno dtype: int32 - name: date dtype: string - name: heading dtype: string - name: body dtype: string - name: topic dtype: class_label: names: '0': business '1': editorial '2': entertainment '3': nation '4': sports splits: - name: train num_bytes: 69400234 num_examples: 17312 - name: test num_bytes: 17265514 num_examples: 4329 download_size: 0 dataset_size: 86665748 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.kaggle.com/sudalairajkumar/telugu-nlp?select=telugu_news - Repository: https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset ### Dataset Summary This dataset contains Telugu language news articles along with respective topic labels (business, editorial, entertainment, nation, sport) extracted from the daily Andhra Jyoti. This dataset could be used to build Classification and Language Models. ### Supported Tasks and Leaderboards Multiclass classification, Topic Classification, Language Model ### Languages TE - Telugu, India ## Dataset Structure ### Data Instances Two CSV files (train, test) with five columns (sno, date, heading, body, topic). ### Data Fields - sno: id - date: publish date of the news article - heading: article heading/title - body: article body/content - topic: one of the following topics (business, editorial, entertainment, nation, sport) ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data - https://www.kaggle.com/sudalairajkumar/telugu-nlp?select=telugu_news - https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset #### Initial Data Collection and Normalization The source data is scraped articles from archives of Telugu newspaper website Andhra Jyoti. A set of queries were created and the corresponding ground truth answers were retrieved by a combination of BM25 and tf-idf. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Sudalai Rajkumar, Anusha Motamarri ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{kaggle:dataset, title = {Telugu News - Natural Language Processing for Indian Languages}, authors={Sudalai Rajkumar, Anusha Motamarri}, year={2019} } ``` ### Contributions Thanks to [@oostopitre](https://github.com/oostopitre) for adding this dataset.
income/trec-covid-top-20-gen-queries	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval --- # NFCorpus: 20 generated queries (BEIR Benchmark) This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset. - DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1) - id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`). - Questions generated: 20 - Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py) Below contains the old dataset card for the BEIR benchmark. # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/UKPLab/beir - Repository: https://github.com/UKPLab/beir - Paper: https://openreview.net/forum?id=wCu6T5xFjeJ - Leaderboard: https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - Point of Contact: nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits \| Dataset \| Website\| BEIR-Name \| Type \| Queries \| Corpus \| Rel D/Q \| Down-load \| md5 \| \| -------- \| -----\| ---------\| --------- \| ----------- \| ---------\| ---------\| :----------: \| :------:\| \| MSMARCO \| [Homepage](https://microsoft.github.io/msmarco/)\| ``msmarco`` \| ``train``<br>``dev``<br>``test``\| 6,980 \| 8.84M \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) \| ``444067daf65d982533ea17ebd59501e4`` \| \| TREC-COVID \| [Homepage](https://ir.nist.gov/covidSubmit/index.html)\| ``trec-covid``\| ``test``\| 50\| 171K\| 493.5 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) \| ``ce62140cb23feb9becf6270d0d1fe6d1`` \| \| NFCorpus \| [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) \| ``nfcorpus`` \| ``train``<br>``dev``<br>``test``\| 323 \| 3.6K \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) \| ``a89dba18a62ef92f7d323ec890a0d38d`` \| \| BioASQ \| [Homepage](http://bioasq.org) \| ``bioasq``\| ``train``<br>``test`` \| 500 \| 14.91M \| 8.05 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) \| \| NQ \| [Homepage](https://ai.google.com/research/NaturalQuestions) \| ``nq``\| ``train``<br>``test``\| 3,452 \| 2.68M \| 1.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) \| ``d4d3d2e48787a744b6f6e691ff534307`` \| \| HotpotQA \| [Homepage](https://hotpotqa.github.io) \| ``hotpotqa``\| ``train``<br>``dev``<br>``test``\| 7,405 \| 5.23M \| 2.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) \| ``f412724f78b0d91183a0e86805e16114`` \| \| FiQA-2018 \| [Homepage](https://sites.google.com/view/fiqa/) \| ``fiqa`` \| ``train``<br>``dev``<br>``test``\| 648 \| 57K \| 2.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) \| ``17918ed23cd04fb15047f73e6c3bd9d9`` \| \| Signal-1M(RT) \| [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)\| ``signal1m`` \| ``test``\| 97 \| 2.86M \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) \| \| TREC-NEWS \| [Homepage](https://trec.nist.gov/data/news2019.html) \| ``trec-news`` \| ``test``\| 57 \| 595K \| 19.6 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) \| \| ArguAna \| [Homepage](http://argumentation.bplaced.net/arguana/data) \| ``arguana``\| ``test`` \| 1,406 \| 8.67K \| 1.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) \| ``8ad3e3c2a5867cdced806d6503f29b99`` \| \| Touche-2020\| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) \| ``webis-touche2020``\| ``test``\| 49 \| 382K \| 19.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) \| ``46f650ba5a527fc69e0a6521c5a23563`` \| \| CQADupstack\| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) \| ``cqadupstack``\| ``test``\| 13,145 \| 457K \| 1.4 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) \| ``4e41456d7df8ee7760a7f866133bda78`` \| \| Quora\| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) \| ``quora``\| ``dev``<br>``test``\| 10,000 \| 523K \| 1.6 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) \| ``18fb154900ba42a600f84b839c173167`` \| \| DBPedia \| [Homepage](https://github.com/iai-group/DBpedia-Entity/) \| ``dbpedia-entity``\| ``dev``<br>``test``\| 400 \| 4.63M \| 38.2 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) \| ``c2a39eb420a3164af735795df012ac2c`` \| \| SCIDOCS\| [Homepage](https://allenai.org/data/scidocs) \| ``scidocs``\| ``test``\| 1,000 \| 25K \| 4.9 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) \| ``38121350fc3a4d2f48850f6aff52e4a9`` \| \| FEVER \| [Homepage](http://fever.ai) \| ``fever``\| ``train``<br>``dev``<br>``test``\| 6,666 \| 5.42M \| 1.2\| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) \| ``5a818580227bfb4b35bb6fa46d9b6c03`` \| \| Climate-FEVER\| [Homepage](http://climatefever.ai) \| ``climate-fever``\|``test``\| 1,535 \| 5.42M \| 3.0 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) \| ``8b66f0a9126c521bae2bde127b4dc99d`` \| \| SciFact\| [Homepage](https://github.com/allenai/scifact) \| ``scifact``\| ``train``<br>``test``\| 300 \| 5K \| 1.1 \| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) \| ``5f7d1de60b170fc8027bb7898e2efca1`` \| \| Robust04 \| [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) \| ``robust04``\| ``test``\| 249 \| 528K \| 69.9 \| No \| [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) \| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca	--- pretty_name: Evaluation run of chargoddard/mistral-11b-slimorca dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chargoddard/mistral-11b-slimorca](https://huggingface.co/chargoddard/mistral-11b-slimorca)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-08T07:32:00.985160](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca/blob/main/results_2024-01-08T07-32-00.985160.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6367892367382408,\n\ \ \"acc_stderr\": 0.032420458743968754,\n \"acc_norm\": 0.6401126581266797,\n\ \ \"acc_norm_stderr\": 0.03306588858456459,\n \"mc1\": 0.38310893512851896,\n\ \ \"mc1_stderr\": 0.017018461679389855,\n \"mc2\": 0.5466386336115909,\n\ \ \"mc2_stderr\": 0.015507674046261742\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6075085324232082,\n \"acc_stderr\": 0.01426963463567073,\n\ \ \"acc_norm\": 0.6424914675767918,\n \"acc_norm_stderr\": 0.014005494275916573\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6468830910177256,\n\ \ \"acc_stderr\": 0.004769618829196511,\n \"acc_norm\": 0.8380800637323242,\n\ \ \"acc_norm_stderr\": 0.0036762448867232586\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411021,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411021\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.0372424959581773,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.0372424959581773\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"\ acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7709677419354839,\n\ \ \"acc_stderr\": 0.023904914311782658,\n \"acc_norm\": 0.7709677419354839,\n\ \ \"acc_norm_stderr\": 0.023904914311782658\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8232323232323232,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.8232323232323232,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676177,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676177\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.0230866350868414,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.0230866350868414\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608311,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608311\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3541899441340782,\n\ \ \"acc_stderr\": 0.015995644947299232,\n \"acc_norm\": 0.3541899441340782,\n\ \ \"acc_norm_stderr\": 0.015995644947299232\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.02526169121972948,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.02526169121972948\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6816720257234726,\n\ \ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n\ \ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n\ \ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4530638852672751,\n\ \ \"acc_stderr\": 0.012713845972358981,\n \"acc_norm\": 0.4530638852672751,\n\ \ \"acc_norm_stderr\": 0.012713845972358981\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6715686274509803,\n \"acc_stderr\": 0.01899970738316267,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.01899970738316267\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128445,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128445\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.38310893512851896,\n\ \ \"mc1_stderr\": 0.017018461679389855,\n \"mc2\": 0.5466386336115909,\n\ \ \"mc2_stderr\": 0.015507674046261742\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7797947908445146,\n \"acc_stderr\": 0.011646276755089688\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5238817285822593,\n \ \ \"acc_stderr\": 0.013756765835465755\n }\n}\n```" repo_url: https://huggingface.co/chargoddard/mistral-11b-slimorca leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|arc:challenge\|25_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-08T07-32-00.985160.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|gsm8k\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hellaswag\|10_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-08T07-32-00.985160.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-08T07-32-00.985160.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '/details_harness\|winogrande\|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-08T07-32-00.985160.parquet' - config_name: results data_files: - split: 2024_01_08T07_32_00.985160 path: - results_2024-01-08T07-32-00.985160.parquet - split: latest path: - results_2024-01-08T07-32-00.985160.parquet --- # Dataset Card for Evaluation run of chargoddard/mistral-11b-slimorca <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [chargoddard/mistral-11b-slimorca](https://huggingface.co/chargoddard/mistral-11b-slimorca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-08T07:32:00.985160](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca/blob/main/results_2024-01-08T07-32-00.985160.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6367892367382408, "acc_stderr": 0.032420458743968754, "acc_norm": 0.6401126581266797, "acc_norm_stderr": 0.03306588858456459, "mc1": 0.38310893512851896, "mc1_stderr": 0.017018461679389855, "mc2": 0.5466386336115909, "mc2_stderr": 0.015507674046261742 }, "harness\|arc:challenge\|25": { "acc": 0.6075085324232082, "acc_stderr": 0.01426963463567073, "acc_norm": 0.6424914675767918, "acc_norm_stderr": 0.014005494275916573 }, "harness\|hellaswag\|10": { "acc": 0.6468830910177256, "acc_stderr": 0.004769618829196511, "acc_norm": 0.8380800637323242, "acc_norm_stderr": 0.0036762448867232586 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411021, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411021 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.690566037735849, "acc_stderr": 0.028450154794118637, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118637 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6069364161849711, "acc_stderr": 0.0372424959581773, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.0372424959581773 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41005291005291006, "acc_stderr": 0.02533120243894443, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.02533120243894443 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7709677419354839, "acc_stderr": 0.023904914311782658, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.023904914311782658 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7515151515151515, "acc_stderr": 0.033744026441394036, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.033744026441394036 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8232323232323232, "acc_stderr": 0.027178752639044915, "acc_norm": 0.8232323232323232, "acc_norm_stderr": 0.027178752639044915 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8860103626943006, "acc_stderr": 0.022935144053919443, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676177, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676177 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8547008547008547, "acc_stderr": 0.0230866350868414, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.0230866350868414 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608311, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608311 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.024257901705323378, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.024257901705323378 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3541899441340782, "acc_stderr": 0.015995644947299232, "acc_norm": 0.3541899441340782, "acc_norm_stderr": 0.015995644947299232 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7352941176470589, "acc_stderr": 0.02526169121972948, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.02526169121972948 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811025, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811025 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7345679012345679, "acc_stderr": 0.024569223600460845, "acc_norm": 0.7345679012345679, "acc_norm_stderr": 0.024569223600460845 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4530638852672751, "acc_stderr": 0.012713845972358981, "acc_norm": 0.4530638852672751, "acc_norm_stderr": 0.012713845972358981 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6715686274509803, "acc_stderr": 0.01899970738316267, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.01899970738316267 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128445, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128445 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.38310893512851896, "mc1_stderr": 0.017018461679389855, "mc2": 0.5466386336115909, "mc2_stderr": 0.015507674046261742 }, "harness\|winogrande\|5": { "acc": 0.7797947908445146, "acc_stderr": 0.011646276755089688 }, "harness\|gsm8k\|5": { "acc": 0.5238817285822593, "acc_stderr": 0.013756765835465755 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, 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It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
CyberHarem/colorado_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of colorado/コロラド/科罗拉多 (Azur Lane) This is the dataset of colorado/コロラド/科罗拉多 (Azur Lane), containing 120 images and their tags. The core tags of this character are `long_hair, red_eyes, white_hair, breasts, hairband, hair_over_one_eye, large_breasts, bangs, red_hairband`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 120 \| 153.85 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 120 \| 82.39 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 302 \| 181.15 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 120 \| 135.42 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 302 \| 265.98 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/colorado_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, bare_shoulders, looking_at_viewer, solo, detached_sleeves, sideboob, sleeveless_dress, belt, coat \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, sideboob, sleeveless_dress, solo, thigh_boots, thighhighs, detached_sleeves, lace-up_boots, looking_at_viewer, bare_shoulders, coat, simple_background, white_background, belt \| \| 2 \| 13 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, official_alternate_costume, solo, black_jacket, guitar_case, black_hairband, braid, white_sweater, scarf_over_mouth, white_scarf, looking_at_viewer, disposable_cup, holding_cup, open_clothes, black_thighhighs, simple_background, white_background, bird_on_head \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| looking_at_viewer \| solo \| detached_sleeves \| sideboob \| sleeveless_dress \| belt \| coat \| thigh_boots \| thighhighs \| lace-up_boots \| simple_background \| white_background \| official_alternate_costume \| black_jacket \| guitar_case \| black_hairband \| braid \| white_sweater \| scarf_over_mouth \| white_scarf \| disposable_cup \| holding_cup \| open_clothes \| black_thighhighs \| bird_on_head \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:--------------------\|:-------\|:-------------------\|:-----------\|:-------------------\|:-------\|:-------\|:--------------\|:-------------\|:----------------\|:--------------------\|:-------------------\|:-----------------------------\|:---------------\|:--------------\|:-----------------\|:--------\|:----------------\|:-------------------\|:--------------\|:-----------------\|:--------------\|:---------------\|:-------------------\|:---------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 13 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
Reasat/kather-19_test	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ADI '1': BACK '2': DEB '3': LYM '4': MUC '5': MUS '6': NORM '7': STR '8': TUM splits: - name: train num_bytes: 1093018719.36 num_examples: 7180 download_size: 941913399 dataset_size: 1093018719.36 --- # Dataset Card for "kather-19_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MinusV25/arxiv-1000-sample	--- license: apache-2.0 ---
LucasMagnana/ARASAAC_CACE	--- dataset_info: features: - name: text dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 15417 num_examples: 803 download_size: 13416 dataset_size: 15417 configs: - config_name: default data_files: - split: train path: data/train-* ---
JaydeepPatel/gujarati-alpaca52K	--- license: cc-by-nc-4.0 ---
PlayerJ/iOneBot_Custom_llama2	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7527 num_examples: 19 download_size: 3528 dataset_size: 7527 configs: - config_name: default data_files: - split: train path: data/train-* ---
AnushaKulkarni/preferred_dataset	--- dataset_info: features: - name: prompt dtype: string - name: chosen struct: - name: rank dtype: int32 - name: response dtype: string - name: rejected struct: - name: rank dtype: int32 - name: response dtype: string splits: - name: train num_bytes: 33413 num_examples: 50 download_size: 29993 dataset_size: 33413 configs: - config_name: default data_files: - split: train path: data/train-* ---
mattlc/deepgui	--- dataset_info: features: - name: file_name dtype: image - name: caption dtype: string - name: conditioning_image dtype: image splits: - name: train num_bytes: 184059306.0 num_examples: 400 download_size: 85178148 dataset_size: 184059306.0 --- # Dataset Card for "deepgui_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DBQ/Loro.Piana.Product.prices.France	--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: France - Loro Piana - Product-level price list tags: - webscraping - ecommerce - Loro Piana - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: string - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 389433 num_examples: 1128 download_size: 131337 dataset_size: 389433 --- # Loro Piana web scraped data ## About the website In the European, Middle Eastern and African (EMEA) region, specifically France, Loro Piana operates within the luxury fashion industry. This sector is renowned for its fine craftsmanship, superior quality, and the high value attached to its products. France, in particular, has long been a global center of luxury fashion with its capital, Paris, often referred to as the "fashion capital of the world". The industry is continuously evolving, incorporating Ecommerce and online platforms into their business models to meet the changing demands and preferences of its upscale clientele. In this context, we observed a dataset consisting of Ecommerce product-list page (PLP) data on Loro Piana in France. ## Link to dataset [France - Loro Piana - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Loro%20Piana%20Product-prices%20France/r/recyuT3Im5gEtayt6)
RikoteMaster/isear_augmented	--- dataset_info: features: - name: Text_processed dtype: string - name: Emotion dtype: string - name: Augmented dtype: bool splits: - name: train num_bytes: 1130723 num_examples: 7499 - name: validation num_bytes: 198379 num_examples: 1324 - name: test num_bytes: 250735 num_examples: 1879 download_size: 923389 dataset_size: 1579837 --- # Dataset Card for "isear_augmented" Classical ISEAR dataset augmented using gpt3.5 prompt tunning. Example prompt: Hello, you are going to take care of the task of increasing data in text. The data format that I am going to pass you is going to be as follows. Sentence: this is a sample text PROHIBITED WORD: this is a sample PROHIBITED WORD 2. Sentence: this is a sample text PROHIBITED WORD: this is a sample PROHIBITED WORD. I can enter as many sentences as I want, you must respect the logic that I have marked. NOW YOUR TASK MUST BE TO REFORMULATE THE SENTENCES IN ORDER TO EXPRESS THE SENTIMENT OF THE PROHIBITED WORD BUT YOU CANT USE THE PROHIBITED WORD BECAUSE IS FORBIDEN. PROHIBITED WORD LIST = [anger, fear, love, sadness, guilt, joy, shame, Overwhelming, remorse] you cannot use none of this words in the reformulation process, also you cannot use words derivated from this words and you must not do aclarations about what the text is trying to transmit. The output of the reformulated sentences must be Reformulated sentence 1: LorenIpsum reformulated sentence 2: LorenIpsum reformulated. Remember, I can introduce more than two sentences so you must return the reformulation of each sentence to me. So, remember, you must get a sentence that prevails the sentiment called the sentence PROHIBITED WORD but it is more important that the word does not appear inside the reformulation. If the word appears within the reformulation we would be entering into an incorrect practice of data augmentation. Yann LeCunn is watching, your bosses are watching you too, you must do what I ask you to do and get me the best sentences possible, but remember, without using the PROHIBITED WORD in the reformulation.1. Sentece: Unexpected visit by a close friend, whom I hadn't seen for half a year. PROHIBITED WORD: joy 2. Sentece: I wandered by mistake into the safety zone of a shooting range, and was shot at. PROHIBITED WORD: fear 3. Sentece: Being treated unfairly. PROHIBITED WORD: anger 4. Sentece: Breaking up with a girl. PROHIBITED WORD: sadness 5. Sentece: Nothing. PROHIBITED WORD: disgust 6. Sentece: None. PROHIBITED WORD: shame 7. Sentece: Little contact with my father before he died. PROHIBITED WORD: guilt 8. Sentece: When I was accepted as a student at the college, not having thought it possible. PROHIBITED WORD: joy The results obtained were quite well but when gpt obtain a way to express a sentiment with concrete words he is going to repeat it the structure, so you must reestructure the prompt [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3	--- pretty_name: Evaluation run of cgato/Thespis-CurtainCall-7b-v0.3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cgato/Thespis-CurtainCall-7b-v0.3](https://huggingface.co/cgato/Thespis-CurtainCall-7b-v0.3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-14T19:10:45.068833](https://huggingface.co/datasets/open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3/blob/main/results_2024-03-14T19-10-45.068833.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6209088671220813,\n\ \ \"acc_stderr\": 0.032670238010700646,\n \"acc_norm\": 0.626293015631346,\n\ \ \"acc_norm_stderr\": 0.033333675763785604,\n \"mc1\": 0.35006119951040393,\n\ \ \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5095336916736214,\n\ \ \"mc2_stderr\": 0.015077729128899325\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6032423208191127,\n \"acc_stderr\": 0.014296513020180644,\n\ \ \"acc_norm\": 0.6424914675767918,\n \"acc_norm_stderr\": 0.014005494275916573\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6328420633339972,\n\ \ \"acc_stderr\": 0.004810449343572395,\n \"acc_norm\": 0.8293168691495718,\n\ \ \"acc_norm_stderr\": 0.003754629313275157\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.630057803468208,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246483,\n \"\ acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246483\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4126984126984127,\n\ \ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n\ \ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7354838709677419,\n\ \ \"acc_stderr\": 0.02509189237885928,\n \"acc_norm\": 0.7354838709677419,\n\ \ \"acc_norm_stderr\": 0.02509189237885928\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.02912652283458682,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.02912652283458682\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153303,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153303\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.02432173848460235,\n \ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.02432173848460235\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815632,\n \ \ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815632\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059285,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059285\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\"\ : 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n\ \ \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8146788990825689,\n\ \ \"acc_stderr\": 0.016659279700295827,\n \"acc_norm\": 0.8146788990825689,\n\ \ \"acc_norm_stderr\": 0.016659279700295827\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\"\ : {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n\ \ \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\ acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098823,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098823\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.03957835471980979,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.03957835471980979\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8589743589743589,\n\ \ \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n\ \ \"acc_norm_stderr\": 0.022801382534597528\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.013890862162876166,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.013890862162876166\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\ \ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.33854748603351953,\n\ \ \"acc_stderr\": 0.01582670009648135,\n \"acc_norm\": 0.33854748603351953,\n\ \ \"acc_norm_stderr\": 0.01582670009648135\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6895424836601307,\n \"acc_stderr\": 0.026493033225145898,\n\ \ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.026493033225145898\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7041800643086816,\n\ \ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\ \ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495033,\n\ \ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495033\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4439374185136897,\n\ \ \"acc_stderr\": 0.012689708167787686,\n \"acc_norm\": 0.4439374185136897,\n\ \ \"acc_norm_stderr\": 0.012689708167787686\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.029408372932278746,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.029408372932278746\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6535947712418301,\n \"acc_stderr\": 0.01924978569171721,\n \ \ \"acc_norm\": 0.6535947712418301,\n \"acc_norm_stderr\": 0.01924978569171721\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291296,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291296\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.35006119951040393,\n\ \ \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5095336916736214,\n\ \ \"mc2_stderr\": 0.015077729128899325\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090248\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3737680060652009,\n \ \ \"acc_stderr\": 0.013326342860737021\n }\n}\n```" repo_url: https://huggingface.co/cgato/Thespis-CurtainCall-7b-v0.3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|arc:challenge\|25_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-14T19-10-45.068833.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|gsm8k\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hellaswag\|10_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T19-10-45.068833.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-14T19-10-45.068833.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '/details_harness\|winogrande\|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-14T19-10-45.068833.parquet' - config_name: results data_files: - split: 2024_03_14T19_10_45.068833 path: - results_2024-03-14T19-10-45.068833.parquet - split: latest path: - results_2024-03-14T19-10-45.068833.parquet --- # Dataset Card for Evaluation run of cgato/Thespis-CurtainCall-7b-v0.3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cgato/Thespis-CurtainCall-7b-v0.3](https://huggingface.co/cgato/Thespis-CurtainCall-7b-v0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-14T19:10:45.068833](https://huggingface.co/datasets/open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3/blob/main/results_2024-03-14T19-10-45.068833.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6209088671220813, "acc_stderr": 0.032670238010700646, "acc_norm": 0.626293015631346, "acc_norm_stderr": 0.033333675763785604, "mc1": 0.35006119951040393, "mc1_stderr": 0.01669794942015103, "mc2": 0.5095336916736214, "mc2_stderr": 0.015077729128899325 }, "harness\|arc:challenge\|25": { "acc": 0.6032423208191127, "acc_stderr": 0.014296513020180644, "acc_norm": 0.6424914675767918, "acc_norm_stderr": 0.014005494275916573 }, "harness\|hellaswag\|10": { "acc": 0.6328420633339972, "acc_stderr": 0.004810449343572395, "acc_norm": 0.8293168691495718, "acc_norm_stderr": 0.003754629313275157 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201942, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201942 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, 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"harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6410256410256411, "acc_stderr": 0.02432173848460235, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.02432173848460235 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815632, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815632 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.031041941304059285, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059285 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8146788990825689, "acc_stderr": 0.016659279700295827, "acc_norm": 0.8146788990825689, "acc_norm_stderr": 0.016659279700295827 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538272, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538272 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7990196078431373, "acc_stderr": 0.028125972265654373, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.028125972265654373 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 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"acc_norm_stderr": 0.022801382534597528 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.013890862162876166, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.013890862162876166 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.0246853168672578, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.0246853168672578 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.33854748603351953, "acc_stderr": 0.01582670009648135, "acc_norm": 0.33854748603351953, "acc_norm_stderr": 0.01582670009648135 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6895424836601307, "acc_stderr": 0.026493033225145898, "acc_norm": 0.6895424836601307, "acc_norm_stderr": 0.026493033225145898 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7041800643086816, "acc_stderr": 0.025922371788818763, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.025922371788818763 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7098765432098766, "acc_stderr": 0.025251173936495033, "acc_norm": 0.7098765432098766, "acc_norm_stderr": 0.025251173936495033 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4439374185136897, "acc_stderr": 0.012689708167787686, "acc_norm": 0.4439374185136897, "acc_norm_stderr": 0.012689708167787686 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.625, "acc_stderr": 0.029408372932278746, "acc_norm": 0.625, "acc_norm_stderr": 0.029408372932278746 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6535947712418301, "acc_stderr": 0.01924978569171721, "acc_norm": 0.6535947712418301, "acc_norm_stderr": 0.01924978569171721 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291296, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291296 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.35006119951040393, "mc1_stderr": 0.01669794942015103, "mc2": 0.5095336916736214, "mc2_stderr": 0.015077729128899325 }, "harness\|winogrande\|5": { "acc": 0.7861089187056038, "acc_stderr": 0.011524466954090248 }, "harness\|gsm8k\|5": { "acc": 0.3737680060652009, "acc_stderr": 0.013326342860737021 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and 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It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. 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sirCamp/hotpot_as2_pairs_and_triplets	--- dataset_info: features: - name: texts sequence: string splits: - name: pairs num_bytes: 34545896 num_examples: 117828 - name: triplets num_bytes: 34545896 num_examples: 117828 download_size: 32977152 dataset_size: 69091792 --- # Dataset Card for "hotpot_as2_pairs_and_triplets" This dataset is a modified version of HotpotAS2 from [lucadiliello/hotpot_as2](https://huggingface.co/datasets/lucadiliello/hotpot_as2) designed to train/pre-train "passage-retrieval" models. The dataset is divided into two splits: + pairs split: Each instance in this split consists of a <u>question-(positive)answer pair</u>. + triplets split: Each instance in this split consists of a <u>question-(positive)answer-(hard-negative)answer triplet</u>. For more info check out the original HotpotQA dataset in this [repository](https://github.com/lucadiliello/answer-selection)
beratcmn/parse-gpt-tr	--- language: - tr license: apache-2.0 task_categories: - question-answering - text2text-generation pretty_name: Parse GPT TR dataset_info: features: - name: id dtype: string - name: model dtype: string - name: messages list: - name: index dtype: int64 - name: role dtype: string - name: text dtype: string splits: - name: train num_bytes: 31282 num_examples: 14 download_size: 21619 dataset_size: 31282 configs: - config_name: default data_files: - split: train path: data/train-* --- # ParseGPT Turkish This is a Turkish dataset gathered from various individuals via the [ChatGPT Conversation Parser by beratcmn](https://huggingface.co/spaces/beratcmn/chatgpt-conversation-parser). This dataset gets updated every time someone uses the ChatGPT Conversation Parser to parse a conversation. This dataset contains conversations from various individuals. The conversations are in Turkish and are in the form of a list of messages. Each message has an index, a role (either "user" or "assistant"), and the text of the message. # Contact If you have any questions, please contact beratcmn on [HuggingFace](https://huggingface.co/beratcmn). Website: [beratcimen.com](https://beratcimen.com/) Email: beratcmn@hotmail.com
sagot/lefff_morpho	--- license: lgpl-lr --- # Dataset Card for lefff morpho ## Dataset Description - Homepage: [http://almanach.inria.fr/software_and_resources/custom/Alexina-en.html](http://almanach.inria.fr/software_and_resources/custom/Alexina-en.html) - Repository: [https://gitlab.inria.fr/almanach/alexina/lefff](https://gitlab.inria.fr/almanach/alexina/lefff) - Paper: [http://www.lrec-conf.org/proceedings/lrec2010/pdf/701_Paper.pdf](http://www.lrec-conf.org/proceedings/lrec2010/pdf/701_Paper.pdf) - Point of Contact: [Benoît Sagot](benoit.sagot@inria.fr) ### Dataset Summary The Lefff, currently in its 3.5 version, is one of the main morphological and syntactic lexicons for French. This Hugging Face dataset provides an easy access to the extensional morphological information in the Lefff, i.e. to the 4-uples (form, lemma, category, morphosyntactic features) and to the amalgams (e.g. _aux_ = _à_ + _les_) it contains. Category and morphosyntactic features are provided both in the original Lefff format and following the UniMorph guidelines. ### Languages French ## Dataset Creation The main author of the resource is Benoît Sagot (Inria, France). Please refer to the main paper and other Lefff-related papers for details. ## Additional Information ### Licensing Information The dataset, as the whole Lefff, is distributed under the LGPL-LR licence. ### Citation Information The main paper regarding the Lefff can be found [here](https://aclanthology.org/L10-1487/). Here is the BibTeX entry for the paper: ``` @inproceedings{sagot:inria-00521242, TITLE = {{The Lefff, a freely available and large-coverage morphological and syntactic lexicon for French}}, AUTHOR = {Sagot, Beno{\^i}t}, URL = {https://hal.inria.fr/inria-00521242}, BOOKTITLE = {{7th international conference on Language Resources and Evaluation (LREC 2010)}}, ADDRESS = {Valletta, Malta}, YEAR = {2010}, MONTH = May, PDF = {https://hal.inria.fr/inria-00521242/file/lrec10lefff.pdf}, HAL_ID = {inria-00521242}, HAL_VERSION = {v1}, } ``` For specific parts of speech or other parts of the lexicon, please cite the corresponding papers whenever relevant.
hlillemark/flores200_eng_output_scaffolding_mt5	--- dataset_info: features: - name: id dtype: int32 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 10718199156 num_examples: 10240000 - name: val num_bytes: 3827042 num_examples: 5000 - name: test num_bytes: 7670994 num_examples: 10000 download_size: 4669652168 dataset_size: 10729697192 --- # Dataset Card for "flores200_eng_output_scaffolding_mt5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-us_foreign_policy	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: fewshot_context_neg dtype: string splits: - name: dev num_bytes: 4594 num_examples: 5 - name: test num_bytes: 407390 num_examples: 100 download_size: 70800 dataset_size: 411984 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-us_foreign_policy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
fuyu-quant/ibl-regression-ver2-linear-pred	--- dataset_info: features: - name: prediction dtype: string - name: 'true' dtype: string - name: index dtype: int64 splits: - name: pred num_bytes: 96424 num_examples: 1000 download_size: 24459 dataset_size: 96424 configs: - config_name: default data_files: - split: pred path: data/pred-* ---
CyberHarem/shikinami_kantaicollection	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of shikinami/敷波/敷波 (Kantai Collection) This is the dataset of shikinami/敷波/敷波 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `brown_hair, ponytail, brown_eyes, short_hair, ribbon, hair_ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 398.10 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 266.04 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1057 \| 529.52 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 367.15 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1057 \| 700.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/shikinami_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, brown_sailor_collar, serafuku, solo, looking_at_viewer, blush, short_sleeves, brown_skirt, pleated_skirt, simple_background, upper_body, white_background, twitter_username \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_socks, brown_sailor_collar, brown_skirt, kneehighs, pleated_skirt, serafuku, simple_background, solo, white_background, looking_at_viewer, blush, short_sleeves, sitting, open_mouth \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, black_sailor_collar, black_skirt, black_socks, serafuku, solo, anchor_symbol, kneehighs, pleated_skirt, looking_at_viewer, white_background, short_sleeves, simple_background, wariza \| \| 3 \| 9 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, black_socks, full_body, kneehighs, machinery, pleated_skirt, serafuku, simple_background, solo, smokestack, white_background, black_sailor_collar, black_skirt, looking_at_viewer, torpedo_launcher, adapted_turret, standing, cannon, rigging, short_sleeves, short_ponytail \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, black_socks, brown_sailor_collar, brown_skirt, grey_footwear, kneehighs, pleated_skirt, serafuku, solo, full_body, short_sleeves, anchor_symbol, shoes, blush, open_mouth, outdoors, smile, standing \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| anchor_symbol, black_sailor_collar, kneehighs, pleated_skirt, serafuku, short_sleeves, solo_focus, black_skirt, black_socks, 2girls, long_hair, standing \| \| 6 \| 7 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, black_pantyhose, detached_collar, playboy_bunny, rabbit_ears, small_breasts, solo, strapless_leotard, fake_animal_ears, simple_background, wrist_cuffs, alternate_costume, looking_at_viewer, black_leotard, full_body, grey_background, red_bowtie, red_leotard, sitting, white_background \| \| 7 \| 9 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, solo, cowboy_shot, looking_at_viewer, collarbone, black_one-piece_swimsuit, blue_one-piece_swimsuit, covered_navel, school_swimsuit, small_breasts, standing, blush, gradient_background \| \| 8 \| 11 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, alternate_costume, obi, blush, solo, yukata, looking_at_viewer, upper_body, uchiwa, floral_print \| \| 9 \| 9 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, white_apron, black_dress, enmaided, solo, blush, maid_apron, maid_headdress, simple_background, frilled_apron, looking_at_viewer, open_mouth, puffy_sleeves, short_sleeves, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| brown_sailor_collar \| serafuku \| solo \| looking_at_viewer \| blush \| short_sleeves \| brown_skirt \| pleated_skirt \| simple_background \| upper_body \| white_background \| twitter_username \| black_socks \| kneehighs \| sitting \| open_mouth \| black_sailor_collar \| black_skirt \| anchor_symbol \| wariza \| full_body \| machinery \| smokestack \| torpedo_launcher \| adapted_turret \| standing \| cannon \| rigging \| short_ponytail \| grey_footwear \| shoes \| outdoors \| smile \| solo_focus \| 2girls \| long_hair \| black_pantyhose \| detached_collar \| playboy_bunny \| rabbit_ears \| small_breasts \| strapless_leotard \| fake_animal_ears \| wrist_cuffs \| alternate_costume \| black_leotard \| grey_background \| red_bowtie \| red_leotard \| cowboy_shot \| collarbone \| black_one-piece_swimsuit \| blue_one-piece_swimsuit \| covered_navel \| school_swimsuit \| gradient_background \| obi \| yukata \| uchiwa \| floral_print \| white_apron \| black_dress \| enmaided \| maid_apron \| maid_headdress \| frilled_apron \| puffy_sleeves \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:----------------------\|:-----------\|:-------\|:--------------------\|:--------\|:----------------\|:--------------\|:----------------\|:--------------------\|:-------------\|:-------------------\|:-------------------\|:--------------\|:------------\|:----------\|:-------------\|:----------------------\|:--------------\|:----------------\|:---------\|:------------\|:------------\|:-------------\|:-------------------\|:-----------------\|:-----------\|:---------\|:----------\|:-----------------\|:----------------\|:--------\|:-----------\|:--------\|:-------------\|:---------\|:------------\|:------------------\|:------------------\|:----------------\|:--------------\|:----------------\|:--------------------\|:-------------------\|:--------------\|:--------------------\|:----------------\|:------------------\|:-------------\|:--------------\|:--------------\|:-------------\|:---------------------------\|:--------------------------\|:----------------\|:------------------\|:----------------------\|:------\|:---------\|:---------\|:---------------\|:--------------\|:--------------\|:-----------\|:-------------\|:-----------------\|:----------------\|:----------------\| \| 0 \| 13 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| X \| X \| \| X \| \| X \| X \| \| X \| \| X \| X \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 9 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| X \| X \| \| X \| \| X \| X \| \| X \| \| X \| X \| \| \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| \| \| \| X \| X \| \| X \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| \| \| X \| \| \| \| X \| \| X \| \| \| \| \| X \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 7 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| X \| \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 9 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 11 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| X \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| 9 \| 9 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| X \| X \| X \| X \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \|
zuleo/karen-fukuhara	--- license: creativeml-openrail-m tags: - stable-diffusion - embedding - textual-inversion - text-to-image - image-to-image - art - artistic --- # Karen Fukuhara textual inversion This is an embedding of Karen Fukuhara. She plays several different amazing roles from acting to voice acting: (Kimiko Miyashiro, Glimmah, Kipo). ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid1.png) ## Embedding Usage Use the token ```kfukvf-1990``` ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid2.png) --- ## 🎶 Prompt Examples 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, dressed as a queen with glimmering jewelry, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful, dynamic lighting``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DDIM, CFG scale: 7, Seed: 2154893269, Size: 512x768, Model hash: d8691b4d16_ --- 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, sitting near a table having a drink, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2899587651, Size: 512x768, Model hash: e3cda540bf_ --- 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, wearing a dirty jacket near a busy city, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3576359901, Size: 512x768, Model hash: 67abd65708_ ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid3.png) ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid4.png) --- ## 🎴 text2img Sampler and Checkpoint grids: It's always great to get a visual of what's going on with sampler using different models with this embedding. See the examples below and tune them to your liking. [Sampling Grid](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/sampler_ckpt_grid.png) --- ☕ If you enjoy this model, buy me a coffee [Buy a coffee](https://ko-fi.com/3eegames) ---
CaliD3f/garbagepailkids	--- license: openrail ---
Nadav/pixel_glue_cola	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 50427044.125 num_examples: 8551 - name: validation num_bytes: 6217509.625 num_examples: 1043 download_size: 46779514 dataset_size: 56644553.75 --- # Dataset Card for "pixel_glue_cola" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
maxidl/LeoLM-ArcChallenge_de-fixed	--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices struct: - name: text sequence: string - name: label sequence: string - name: answerKey dtype: string - name: question_de dtype: string - name: choices_de struct: - name: label sequence: string - name: text sequence: string - name: translation_de dtype: string splits: - name: test num_bytes: 1170650 num_examples: 1172 - name: validation num_bytes: 301780 num_examples: 299 download_size: 807886 dataset_size: 1472430 configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* --- The [LeoLM/ArcChallenge_de](https://huggingface.co/datasets/LeoLM/ArcChallenge_de) dataset, but with label errors fixed. The fix applied: ``` import datasets as hfds ds = hfds.load_dataset("LeoLM/ArcChallenge_de") def check_label(row): choices_en = row["choices"] label_en = choices_en["label"] choices_de = row["choices_de"] label_de = choices_de["label"] if label_en != label_de: print(f"\nLabel mismatch:\n{row}\n{label_en}\n{label_de}") choices_de["label"] = label_en return {"choices": choices_en, "choices_de": choices_de} ds = ds.map(lambda row: check_label(row)) ds.push_to_hub("maxidl/LeoLM-ArcChallenge_de-fixed") ``` Output of running this: ``` Label mismatch: {'id': 'NYSEDREGENTS_2014_4_4', 'question': 'Which state of matter has no definite volume and no definite shape?', 'choices': {'text': ['gas', 'liquid', 'solid'], 'label': ['A', 'B', 'C']}, 'answerKey': 'A', 'question_de': 'Welcher Aggregatzustand hat kein bestimmtes Volumen und keine bestimmte Form?', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['Gas', 'Flüssigkeit', 'Feststoff']}, 'translation_de': '{"question": "Welcher Aggregatzustand hat kein bestimmtes Volumen und keine bestimmte Form?", "choices": ["Gas", "Flüssigkeit", "Feststoff"]}'} ['A', 'B', 'C'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'TIMSS_2003_8_pg29', 'question': 'Which of the following organs is NOT situated in the abdomen?', 'choices': {'text': ['liver', 'kidney', 'stomach', 'bladder', 'heart'], 'label': ['A', 'B', 'C', 'D', 'E']}, 'answerKey': 'E', 'question_de': 'Welches der folgenden Organe ist NICHT im Bauchraum?', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['Leber', 'Niere', 'Magen', 'Blase', 'Herz']}, 'translation_de': '{"question": "Welches der folgenden Organe ist NICHT im Bauchraum?", "choices": ["Leber", "Niere", "Magen", "Blase", "Herz"]}'} ['A', 'B', 'C', 'D', 'E'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_4_19', 'question': 'As kittens grow into cats, their body weight usually', 'choices': {'text': ['decreases', 'increases', 'remains the same'], 'label': ['A', 'B', 'C']}, 'answerKey': 'B', 'question_de': 'Wenn Kätzchen zu Katzen heranwachsen, nimmt ihr Körpergewicht normalerweise', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['ab', 'zu', 'bleibt gleich']}, 'translation_de': '{"question": "Wenn Kätzchen zu Katzen heranwachsen, nimmt ihr Körpergewicht normalerweise", "choices": ["ab", "zu", "bleibt gleich"]}'} ['A', 'B', 'C'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_8_12', 'question': "A main function of a plant's seed is to", 'choices': {'text': ['store food to be used during early development', 'attract pollen to be used during development', 'take in light energy to be used during photosynthesis', 'produce chlorophyll to be used during photosynthesis'], 'label': ['1', '2', '3', '4']}, 'answerKey': '1', 'question_de': 'Eine Hauptfunktion des Samens einer Pflanze ist es,', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['Nahrung zu speichern, die während der frühen Entwicklung verwendet wird', 'Pollen anzulocken, der während der Entwicklung verwendet wird', 'Lichtenergie aufzunehmen, die während der Photosynthese verwendet wird', 'Chlorophyll zu produzieren, das während der Photosynthese verwendet wird']}, 'translation_de': '{"question": "Eine Hauptfunktion des Samens einer Pflanze ist es,", "choices": ["Nahrung zu speichern, die während der frühen Entwicklung verwendet wird", "Pollen anzulocken, der während der Entwicklung verwendet wird", "Lichtenergie aufzunehmen, die während der Photosynthese verwendet wird", "Chlorophyll zu produzieren, das während der Photosynthese verwendet wird"]}'} ['1', '2', '3', '4'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_4_28', 'question': 'Large birds have been eating small animals in an area. If all of the large birds died from a disease, the number of small animals in the area would probably', 'choices': {'text': ['decrease', 'increase', 'remain the same'], 'label': ['A', 'B', 'C']}, 'answerKey': 'B', 'question_de': 'Große Vögel haben kleine Tiere in einem Gebiet gefressen. Wenn alle großen Vögel an einer Krankheit sterben würden, würde die Anzahl der kleinen Tiere in der Gegend wahrscheinlich', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['abnehmen', 'zunehmen', 'gleich bleiben']}, 'translation_de': '{"question": "Große Vögel haben kleine Tiere in einem Gebiet gefressen. Wenn alle großen Vögel an einer Krankheit sterben würden, würde die Anzahl der kleinen Tiere in der Gegend wahrscheinlich", "choices": ["abnehmen", "zunehmen", "gleich bleiben"]}'} ['A', 'B', 'C'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_8_38', 'question': 'A substance in the solid phase (state) of matter has', 'choices': {'text': ['a definite shape and a definite volume', 'a definite shape, but no definite volume', 'no definite shape, but a definite volume', 'no definite shape and no definite volume'], 'label': ['1', '2', '3', '4']}, 'answerKey': '1', 'question_de': 'Ein Stoff im festen Aggregatzustand hat', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['eine bestimmte Form und ein bestimmtes Volumen', 'eine bestimmte Form, aber kein bestimmtes Volumen', 'keine bestimmte Form, aber ein bestimmtes Volumen', 'weder eine bestimmte Form noch ein bestimmtes Volumen']}, 'translation_de': '{"question": "Ein Stoff im festen Aggregatzustand hat", "choices": ["eine bestimmte Form und ein bestimmtes Volumen", "eine bestimmte Form, aber kein bestimmtes Volumen", "keine bestimmte Form, aber ein bestimmtes Volumen", "weder eine bestimmte Form noch ein bestimmtes Volumen"]}'} ['1', '2', '3', '4'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_8_20', 'question': 'The equation below shows the products formed when a solution of silver nitrate (AgNO3) reacts with a solution of sodium chloride (NaCl). AgNO3 \x02+ NaCl (Reactants) -> NaNO3 \x02+ AgCl (Products) In this equation, the total mass of the reactants is', 'choices': {'text': ['greater than the total mass of the products', 'equal to the total mass of the products', 'equal to the mass of AgCl', 'less than the mass of AgCl'], 'label': ['1', '2', '3', '4']}, 'answerKey': '2', 'question_de': 'Die Gleichung unten zeigt die Produkte, die entstehen, wenn eine Lösung von Silbernitrat (AgNO3) mit einer Lösung von Natriumchlorid (NaCl) reagiert. AgNO3 \x02+ NaCl (Edukte) -> NaNO3 \x02+ AgCl (Produkte) In dieser Gleichung ist die Gesamtmasse der Edukte', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['größer als die Gesamtmasse der Produkte', 'gleich der Gesamtmasse der Produkte', 'gleich der Masse von AgCl', 'kleiner als die Masse von AgCl']}, 'translation_de': '{"question": "Die Gleichung unten zeigt die Produkte, die entstehen, wenn eine Lösung von Silbernitrat (AgNO3) mit einer Lösung von Natriumchlorid (NaCl) reagiert. AgNO3 \\u0002+ NaCl (Edukte) -> NaNO3 \\u0002+ AgCl (Produkte) In dieser Gleichung ist die Gesamtmasse der Edukte", "choices": ["größer als die Gesamtmasse der Produkte", "gleich der Gesamtmasse der Produkte", "gleich der Masse von AgCl", "kleiner als die Masse von AgCl"]}'} ['1', '2', '3', '4'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_8_41', 'question': 'In a car accident, a seat belt helps prevent injuries by applying a force', 'choices': {'text': ['less than the force of the moving passenger', 'greater than the force of the moving car', "in the same direction as the car's motion", "in the opposite direction of the passenger's motion"], 'label': ['1', '2', '3', '4']}, 'answerKey': '4', 'question_de': 'Bei einem Autounfall hilft der Sicherheitsgurt Verletzungen zu vermeiden, indem er eine Kraft anwendet', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['die kleiner ist als die Kraft des sich bewegenden Passagiers', 'die größer ist als die Kraft des sich bewegenden Autos', 'in die gleiche Richtung wie die Bewegung des Autos', 'in entgegengesetzter Richtung zur Bewegung des Passagiers']}, 'translation_de': '{"question": "Bei einem Autounfall hilft der Sicherheitsgurt Verletzungen zu vermeiden, indem er eine Kraft anwendet", "choices": ["die kleiner ist als die Kraft des sich bewegenden Passagiers", "die größer ist als die Kraft des sich bewegenden Autos", "in die gleiche Richtung wie die Bewegung des Autos", "in entgegengesetzter Richtung zur Bewegung des Passagiers"]}'} ['1', '2', '3', '4'] ['A', 'B', 'C', 'D'] ```
Aditya000001/llamadataset	--- license: wtfpl ---
jtatman/CoT_reformatted_preprocessed_llama2	--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 10782796585 num_examples: 1851975 - name: eval num_bytes: 180549 num_examples: 25 download_size: 1530950918 dataset_size: 10782977134 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---
trajanson/black-long-sleeve-jersey	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4220210.0 num_examples: 19 download_size: 4215502 dataset_size: 4220210.0 --- # Dataset Card for "black-long-sleeve-jersey" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Princess3/Court_data_1k	--- license: wtfpl language: - en --- Just some New Zealand court decisions data, probably needs some more cleaning. data ranges between 1975 to now and its all randomly selected 1158 cases
tr416/dataset_20231007_033121	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 762696.0 num_examples: 297 - name: test num_bytes: 7704.0 num_examples: 3 download_size: 74119 dataset_size: 770400.0 --- # Dataset Card for "dataset_20231007_033121" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kunishou/hh-rlhf-49k-ja	--- license: mit --- This dataset was created by automatically translating part of "Anthropic/hh-rlhf" into Japanese. This dataset is also included in "mosaicml/dolly_hhrlhf". The "ng_translation" flag indicates that the translation was not successful, and "1" means that the translation failed. Therefore, for data with "1", "instruction" and "instruction_en" contain the same text. 以下の通りに読み込むことで"ng_translation"が"1"（翻訳誤り）のものを除外して使用できます。 ``` pip install datasets ``` ``` from datasets import Dataset, load_dataset dataset = load_dataset("kunishou/hh-rlhf-49k-ja") dataset.set_format(type="pandas") df = dataset["train"][:] df = df[df["ng_translation"]!="1"].drop(["ng_translation", "index"], axis=1).reset_index() dataset = Dataset.from_pandas(df) dataset ``` hh-rlhf repository https://github.com/anthropics/hh-rlhf Anthropic/hh-rlhf https://huggingface.co/datasets/Anthropic/hh-rlhf mosaicml/dolly_hhrlhf https://huggingface.co/datasets/mosaicml/dolly_hhrlhf
Ranjit/displace_dev_data	--- dataset_info: features: - name: audio dtype: audio - name: label dtype: class_label: names: '0': test '1': train - name: language dtype: string splits: - name: train num_bytes: 1788567880.172 num_examples: 30569 - name: test num_bytes: 352004692.0 num_examples: 5560 download_size: 2274720816 dataset_size: 2140572572.172 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Csplk/THE.ASCII.ART.EMPORIUM	--- license: cc-by-nc-sa-4.0 task_categories: - text2text-generation - text-generation language: - en tags: - art pretty_name: THE.ASCII.ART.EMPORIUM source: https://ascii.mozz.us:7070 --- ``` https://asciiartist.com/sitefiles/respectartistscampaign.html (published by Laura Brown aka ldb) Respect ASCII Artists Campaign Most ASCII artists will tag their ASCII creation with their initials. This is not just about signing your art, it shows the original artist. If someone else colours the art, or modifies it in any other way, the artist initials need to be kept with it. Anyone modifying art can add their initials (usually something like ldb/ you) and a note about what they had done to the original art. The original artist initials are left on the art, the initials of the person who modified it are added after the original artist. If you can not find the original artist, or a link to them on a site or social media, include a link to the source where you found the art as something being better than nothing. This helps preserve and archive the art as well as giving the artist credit. This is what the Respect ASCII Artists Campaign is about. Give respect to the original artist and leave their initials on the work. Not all ASCII art is available freely. Don't assume it is there for anyone to take (copy and paste or screenshot). Ask to repost, modify, or otherwise use the ASCII art you find online. Don't sell art you do not own or did not fully create yourself. The ribbon above is from the original Respect ASCII Artists Campaign. Started April 1998, by truffle. ```
yuan-sf63/chenyu_mask_64	--- dataset_info: features: - name: feature dtype: string - name: target dtype: string splits: - name: train num_bytes: 10038428.1 num_examples: 91242 - name: validation num_bytes: 1115380.9 num_examples: 10138 download_size: 0 dataset_size: 11153809.0 --- # Dataset Card for "chenyu_mask_64" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jwigginton/news-sp500	--- dataset_info: features: - name: symbol dtype: string - name: body dtype: string - name: publisher dtype: string - name: publish_time dtype: timestamp[ns, tz=GMT] - name: title dtype: string - name: url dtype: string - name: uuid dtype: string splits: - name: train num_bytes: 12055461 num_examples: 2010 download_size: 5673552 dataset_size: 12055461 configs: - config_name: default data_files: - split: train path: data/train-* ---
metaeval/nli-veridicality-transitivity	--- license: cc task_categories: - text-classification language: - en task_ids: - natural-language-inference --- ```bib @inproceedings{yanaka-etal-2021-exploring, title = "Exploring Transitivity in Neural {NLI} Models through Veridicality", author = "Yanaka, Hitomi and Mineshima, Koji and Inui, Kentaro", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", year = "2021", pages = "920--934", } ```
prithviraj-maurya/safesign-combined-general-ift	--- dataset_info: features: - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 10716758457 num_examples: 2596500 download_size: 5594595999 dataset_size: 10716758457 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_google__recurrentgemma-2b	--- pretty_name: Evaluation run of google/recurrentgemma-2b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [google/recurrentgemma-2b](https://huggingface.co/google/recurrentgemma-2b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_google__recurrentgemma-2b-hf_private\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-09T03:06:15.036120](https://huggingface.co/datasets/open-llm-leaderboard/details_google__recurrentgemma-2b-hf_private/blob/main/results_2024-04-09T03-06-15.036120.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.3466998813893547,\n\ \ \"acc_stderr\": 0.033548531585255545,\n \"acc_norm\": 0.3467198572346389,\n\ \ \"acc_norm_stderr\": 0.034298588914231626,\n \"mc1\": 0.211750305997552,\n\ \ \"mc1_stderr\": 0.014302068353925609,\n \"mc2\": 0.3501381307826391,\n\ \ \"mc2_stderr\": 0.013501544365145366\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.27047781569965873,\n \"acc_stderr\": 0.012980954547659556,\n\ \ \"acc_norm\": 0.28924914675767915,\n \"acc_norm_stderr\": 0.013250012579393443\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.44064927305317664,\n\ \ \"acc_stderr\": 0.004954503606471611,\n \"acc_norm\": 0.569308902609042,\n\ \ \"acc_norm_stderr\": 0.004941609820763589\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.3925925925925926,\n\ \ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.3925925925925926,\n\ \ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.3815789473684211,\n \"acc_stderr\": 0.03953173377749194,\n\ \ \"acc_norm\": 0.3815789473684211,\n \"acc_norm_stderr\": 0.03953173377749194\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.38113207547169814,\n \"acc_stderr\": 0.02989060968628663,\n\ \ \"acc_norm\": 0.38113207547169814,\n \"acc_norm_stderr\": 0.02989060968628663\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.3472222222222222,\n\ \ \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.3472222222222222,\n\ \ \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n\ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.34104046242774566,\n\ \ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.34104046242774566,\n\ \ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n\ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.2978723404255319,\n \"acc_stderr\": 0.029896145682095462,\n\ \ \"acc_norm\": 0.2978723404255319,\n \"acc_norm_stderr\": 0.029896145682095462\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2894736842105263,\n\ \ \"acc_stderr\": 0.04266339443159393,\n \"acc_norm\": 0.2894736842105263,\n\ \ \"acc_norm_stderr\": 0.04266339443159393\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.43448275862068964,\n \"acc_stderr\": 0.041307408795554966,\n\ \ \"acc_norm\": 0.43448275862068964,\n \"acc_norm_stderr\": 0.041307408795554966\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2698412698412698,\n \"acc_stderr\": 0.022860838309232072,\n \"\ acc_norm\": 0.2698412698412698,\n \"acc_norm_stderr\": 0.022860838309232072\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.30952380952380953,\n\ \ \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n\ \ \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.3741935483870968,\n \"acc_stderr\": 0.027528904299845777,\n \"\ acc_norm\": 0.3741935483870968,\n \"acc_norm_stderr\": 0.027528904299845777\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.2857142857142857,\n \"acc_stderr\": 0.03178529710642749,\n \"\ acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.03178529710642749\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\"\ : 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.22424242424242424,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.22424242424242424,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.4797979797979798,\n \"acc_stderr\": 0.03559443565563919,\n \"\ acc_norm\": 0.4797979797979798,\n \"acc_norm_stderr\": 0.03559443565563919\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.41450777202072536,\n \"acc_stderr\": 0.03555300319557673,\n\ \ \"acc_norm\": 0.41450777202072536,\n \"acc_norm_stderr\": 0.03555300319557673\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.38461538461538464,\n \"acc_stderr\": 0.02466674491518722,\n\ \ \"acc_norm\": 0.38461538461538464,\n \"acc_norm_stderr\": 0.02466674491518722\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2222222222222222,\n \"acc_stderr\": 0.02534809746809783,\n \ \ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.02534809746809783\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136094,\n\ \ \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136094\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.3926605504587156,\n \"acc_stderr\": 0.020937505161201093,\n \"\ acc_norm\": 0.3926605504587156,\n \"acc_norm_stderr\": 0.020937505161201093\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.2962962962962963,\n \"acc_stderr\": 0.03114144782353603,\n \"\ acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.03114144782353603\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.24509803921568626,\n \"acc_stderr\": 0.030190282453501936,\n \"\ acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.030190282453501936\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460302,\n \ \ \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460302\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.32286995515695066,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.32286995515695066,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.40458015267175573,\n \"acc_stderr\": 0.043046937953806645,\n\ \ \"acc_norm\": 0.40458015267175573,\n \"acc_norm_stderr\": 0.043046937953806645\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.4214876033057851,\n \"acc_stderr\": 0.04507732278775094,\n \"\ acc_norm\": 0.4214876033057851,\n \"acc_norm_stderr\": 0.04507732278775094\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.32142857142857145,\n\ \ \"acc_stderr\": 0.0443280405529152,\n \"acc_norm\": 0.32142857142857145,\n\ \ \"acc_norm_stderr\": 0.0443280405529152\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.3786407766990291,\n \"acc_stderr\": 0.04802694698258974,\n\ \ \"acc_norm\": 0.3786407766990291,\n \"acc_norm_stderr\": 0.04802694698258974\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.47435897435897434,\n\ \ \"acc_stderr\": 0.03271298896811159,\n \"acc_norm\": 0.47435897435897434,\n\ \ \"acc_norm_stderr\": 0.03271298896811159\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.4329501915708812,\n\ \ \"acc_stderr\": 0.017718469101513982,\n \"acc_norm\": 0.4329501915708812,\n\ \ \"acc_norm_stderr\": 0.017718469101513982\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.3583815028901734,\n \"acc_stderr\": 0.0258167567915842,\n\ \ \"acc_norm\": 0.3583815028901734,\n \"acc_norm_stderr\": 0.0258167567915842\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.24022346368715083,\n\ \ \"acc_stderr\": 0.014288343803925302,\n \"acc_norm\": 0.24022346368715083,\n\ \ \"acc_norm_stderr\": 0.014288343803925302\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.027684181883302877,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.027684181883302877\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.40514469453376206,\n\ \ \"acc_stderr\": 0.027882383791325946,\n \"acc_norm\": 0.40514469453376206,\n\ \ \"acc_norm_stderr\": 0.027882383791325946\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.4104938271604938,\n \"acc_stderr\": 0.027371350925124768,\n\ \ \"acc_norm\": 0.4104938271604938,\n \"acc_norm_stderr\": 0.027371350925124768\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.25886524822695034,\n \"acc_stderr\": 0.026129572527180848,\n \ \ \"acc_norm\": 0.25886524822695034,\n \"acc_norm_stderr\": 0.026129572527180848\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.1801470588235294,\n \"acc_stderr\": 0.02334516361654485,\n\ \ \"acc_norm\": 0.1801470588235294,\n \"acc_norm_stderr\": 0.02334516361654485\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.31862745098039214,\n \"acc_stderr\": 0.018850084696468705,\n \ \ \"acc_norm\": 0.31862745098039214,\n \"acc_norm_stderr\": 0.018850084696468705\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.04389311454644286,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.04389311454644286\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.23673469387755103,\n \"acc_stderr\": 0.027212835884073153,\n\ \ \"acc_norm\": 0.23673469387755103,\n \"acc_norm_stderr\": 0.027212835884073153\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.48258706467661694,\n\ \ \"acc_stderr\": 0.03533389234739244,\n \"acc_norm\": 0.48258706467661694,\n\ \ \"acc_norm_stderr\": 0.03533389234739244\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.3313253012048193,\n\ \ \"acc_stderr\": 0.03664314777288085,\n \"acc_norm\": 0.3313253012048193,\n\ \ \"acc_norm_stderr\": 0.03664314777288085\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.4619883040935672,\n \"acc_stderr\": 0.03823727092882307,\n\ \ \"acc_norm\": 0.4619883040935672,\n \"acc_norm_stderr\": 0.03823727092882307\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.211750305997552,\n\ \ \"mc1_stderr\": 0.014302068353925609,\n \"mc2\": 0.3501381307826391,\n\ \ \"mc2_stderr\": 0.013501544365145366\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6827150749802684,\n \"acc_stderr\": 0.01308059841133212\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.15693707354056102,\n \ \ \"acc_stderr\": 0.010019246595616153\n }\n}\n```" repo_url: https://huggingface.co/google/recurrentgemma-2b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|arc:challenge\|25_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|arc:challenge\|25_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|arc:challenge\|25_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-09T03-06-15.036120.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|gsm8k\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|gsm8k\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|gsm8k\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hellaswag\|10_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hellaswag\|10_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hellaswag\|10_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T00-15-01.985047.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T00-53-48.304390.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T03-06-15.036120.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-09T03-06-15.036120.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '/details_harness\|winogrande\|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '/details_harness\|winogrande\|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '/details_harness\|winogrande\|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-09T03-06-15.036120.parquet' - config_name: results data_files: - split: 2024_04_09T00_15_01.985047 path: - results_2024-04-09T00-15-01.985047.parquet - split: 2024_04_09T00_53_48.304390 path: - results_2024-04-09T00-53-48.304390.parquet - split: 2024_04_09T03_06_15.036120 path: - results_2024-04-09T03-06-15.036120.parquet - split: latest path: - results_2024-04-09T03-06-15.036120.parquet --- # Dataset Card for Evaluation run of google/recurrentgemma-2b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [google/recurrentgemma-2b](https://huggingface.co/google/recurrentgemma-2b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_google__recurrentgemma-2b-hf_private", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-09T03:06:15.036120](https://huggingface.co/datasets/open-llm-leaderboard/details_google__recurrentgemma-2b-hf_private/blob/main/results_2024-04-09T03-06-15.036120.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.3466998813893547, "acc_stderr": 0.033548531585255545, "acc_norm": 0.3467198572346389, "acc_norm_stderr": 0.034298588914231626, "mc1": 0.211750305997552, "mc1_stderr": 0.014302068353925609, "mc2": 0.3501381307826391, "mc2_stderr": 0.013501544365145366 }, "harness\|arc:challenge\|25": { "acc": 0.27047781569965873, "acc_stderr": 0.012980954547659556, "acc_norm": 0.28924914675767915, "acc_norm_stderr": 0.013250012579393443 }, "harness\|hellaswag\|10": { "acc": 0.44064927305317664, "acc_stderr": 0.004954503606471611, "acc_norm": 0.569308902609042, "acc_norm_stderr": 0.004941609820763589 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.3925925925925926, "acc_stderr": 0.04218506215368879, "acc_norm": 0.3925925925925926, "acc_norm_stderr": 0.04218506215368879 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.3815789473684211, "acc_stderr": 0.03953173377749194, "acc_norm": 0.3815789473684211, "acc_norm_stderr": 0.03953173377749194 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.38113207547169814, "acc_stderr": 0.02989060968628663, "acc_norm": 0.38113207547169814, "acc_norm_stderr": 0.02989060968628663 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.3472222222222222, "acc_stderr": 0.039812405437178615, "acc_norm": 0.3472222222222222, "acc_norm_stderr": 0.039812405437178615 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.34104046242774566, "acc_stderr": 0.036146654241808254, "acc_norm": 0.34104046242774566, "acc_norm_stderr": 0.036146654241808254 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364395, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364395 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.2978723404255319, "acc_stderr": 0.029896145682095462, "acc_norm": 0.2978723404255319, "acc_norm_stderr": 0.029896145682095462 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159393, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159393 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.43448275862068964, "acc_stderr": 0.041307408795554966, "acc_norm": 0.43448275862068964, "acc_norm_stderr": 0.041307408795554966 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2698412698412698, "acc_stderr": 0.022860838309232072, "acc_norm": 0.2698412698412698, "acc_norm_stderr": 0.022860838309232072 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.30952380952380953, "acc_stderr": 0.04134913018303316, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.04134913018303316 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.3741935483870968, "acc_stderr": 0.027528904299845777, "acc_norm": 0.3741935483870968, "acc_norm_stderr": 0.027528904299845777 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.03178529710642749, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.03178529710642749 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.22424242424242424, "acc_stderr": 0.03256866661681102, "acc_norm": 0.22424242424242424, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.4797979797979798, "acc_stderr": 0.03559443565563919, "acc_norm": 0.4797979797979798, "acc_norm_stderr": 0.03559443565563919 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.41450777202072536, "acc_stderr": 0.03555300319557673, "acc_norm": 0.41450777202072536, "acc_norm_stderr": 0.03555300319557673 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.38461538461538464, "acc_stderr": 0.02466674491518722, "acc_norm": 0.38461538461538464, "acc_norm_stderr": 0.02466674491518722 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2222222222222222, "acc_stderr": 0.02534809746809783, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.02534809746809783 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.28991596638655465, "acc_stderr": 0.029472485833136094, "acc_norm": 0.28991596638655465, "acc_norm_stderr": 0.029472485833136094 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526732, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526732 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.3926605504587156, "acc_stderr": 0.020937505161201093, "acc_norm": 0.3926605504587156, "acc_norm_stderr": 0.020937505161201093 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.03114144782353603, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.03114144782353603 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.24509803921568626, "acc_stderr": 0.030190282453501936, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.030190282453501936 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.25738396624472576, "acc_stderr": 0.028458820991460302, "acc_norm": 0.25738396624472576, "acc_norm_stderr": 0.028458820991460302 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.32286995515695066, "acc_stderr": 0.03138147637575499, "acc_norm": 0.32286995515695066, "acc_norm_stderr": 0.03138147637575499 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.40458015267175573, "acc_stderr": 0.043046937953806645, "acc_norm": 0.40458015267175573, "acc_norm_stderr": 0.043046937953806645 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.4214876033057851, "acc_stderr": 0.04507732278775094, "acc_norm": 0.4214876033057851, "acc_norm_stderr": 0.04507732278775094 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04557239513497751, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04557239513497751 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.25766871165644173, "acc_stderr": 0.03436150827846917, "acc_norm": 0.25766871165644173, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.32142857142857145, "acc_stderr": 0.0443280405529152, "acc_norm": 0.32142857142857145, "acc_norm_stderr": 0.0443280405529152 }, "harness\|hendrycksTest-management\|5": { "acc": 0.3786407766990291, "acc_stderr": 0.04802694698258974, "acc_norm": 0.3786407766990291, "acc_norm_stderr": 0.04802694698258974 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.47435897435897434, "acc_stderr": 0.03271298896811159, "acc_norm": 0.47435897435897434, "acc_norm_stderr": 0.03271298896811159 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.4329501915708812, "acc_stderr": 0.017718469101513982, "acc_norm": 0.4329501915708812, "acc_norm_stderr": 0.017718469101513982 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.3583815028901734, "acc_stderr": 0.0258167567915842, "acc_norm": 0.3583815028901734, "acc_norm_stderr": 0.0258167567915842 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24022346368715083, "acc_stderr": 0.014288343803925302, "acc_norm": 0.24022346368715083, "acc_norm_stderr": 0.014288343803925302 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.027684181883302877, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.027684181883302877 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.40514469453376206, "acc_stderr": 0.027882383791325946, "acc_norm": 0.40514469453376206, "acc_norm_stderr": 0.027882383791325946 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.4104938271604938, "acc_stderr": 0.027371350925124768, "acc_norm": 0.4104938271604938, "acc_norm_stderr": 0.027371350925124768 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.25886524822695034, "acc_stderr": 0.026129572527180848, "acc_norm": 0.25886524822695034, "acc_norm_stderr": 0.026129572527180848 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.1801470588235294, "acc_stderr": 0.02334516361654485, "acc_norm": 0.1801470588235294, "acc_norm_stderr": 0.02334516361654485 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.31862745098039214, "acc_stderr": 0.018850084696468705, "acc_norm": 0.31862745098039214, "acc_norm_stderr": 0.018850084696468705 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.3, "acc_stderr": 0.04389311454644286, "acc_norm": 0.3, "acc_norm_stderr": 0.04389311454644286 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.23673469387755103, "acc_stderr": 0.027212835884073153, "acc_norm": 0.23673469387755103, "acc_norm_stderr": 0.027212835884073153 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.48258706467661694, "acc_stderr": 0.03533389234739244, "acc_norm": 0.48258706467661694, "acc_norm_stderr": 0.03533389234739244 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3313253012048193, "acc_stderr": 0.03664314777288085, "acc_norm": 0.3313253012048193, "acc_norm_stderr": 0.03664314777288085 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.4619883040935672, "acc_stderr": 0.03823727092882307, "acc_norm": 0.4619883040935672, "acc_norm_stderr": 0.03823727092882307 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.211750305997552, "mc1_stderr": 0.014302068353925609, "mc2": 0.3501381307826391, "mc2_stderr": 0.013501544365145366 }, "harness\|winogrande\|5": { "acc": 0.6827150749802684, "acc_stderr": 0.01308059841133212 }, "harness\|gsm8k\|5": { "acc": 0.15693707354056102, "acc_stderr": 0.010019246595616153 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_245	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 753290112.0 num_examples: 147936 download_size: 767852826 dataset_size: 753290112.0 --- # Dataset Card for "chunk_245" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-futin__guess-en_3-fcaae9-2012466610	--- type: predictions tags: - autotrain - evaluation datasets: - futin/guess eval_info: task: text_zero_shot_classification model: facebook/opt-66b metrics: [] dataset_name: futin/guess dataset_config: en_3 dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-66b * Dataset: futin/guess * Config: en_3 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
fleurylol/cfacatering	--- license: apache-2.0 ---
MITCriticalData/L7_Dataset_2008_2015	--- license: mit ---
lenmon666/lentest	--- license: apache-2.0 --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
Markuin/Ia_vozes	--- license: unknown ---
open-llm-leaderboard/details_CausalLM__72B-preview	--- pretty_name: Evaluation run of CausalLM/72B-preview dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CausalLM/72B-preview](https://huggingface.co/CausalLM/72B-preview) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CausalLM__72B-preview\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-09T21:42:26.382618](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__72B-preview/blob/main/results_2023-12-09T21-42-26.382618.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7667362936260237,\n\ \ \"acc_stderr\": 0.027929321227362417,\n \"acc_norm\": 0.7704368351697709,\n\ \ \"acc_norm_stderr\": 0.028461947646281283,\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5257567284522894,\n\ \ \"mc2_stderr\": 0.014743557767765337\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.606655290102389,\n \"acc_stderr\": 0.014275101465693024,\n\ \ \"acc_norm\": 0.6518771331058021,\n \"acc_norm_stderr\": 0.013921008595179347\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6468830910177256,\n\ \ \"acc_stderr\": 0.004769618829196502,\n \"acc_norm\": 0.8323043218482374,\n\ \ \"acc_norm_stderr\": 0.0037283229688748914\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.9144736842105263,\n \"acc_stderr\": 0.02275867713088861,\n\ \ \"acc_norm\": 0.9144736842105263,\n \"acc_norm_stderr\": 0.02275867713088861\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8301886792452831,\n \"acc_stderr\": 0.023108393799841326,\n\ \ \"acc_norm\": 0.8301886792452831,\n \"acc_norm_stderr\": 0.023108393799841326\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8958333333333334,\n\ \ \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n\ \ \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n\ \ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7803468208092486,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.7803468208092486,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5392156862745098,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n\ \ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.026148818018424502,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.026148818018424502\n \ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5701754385964912,\n\ \ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.5701754385964912,\n\ \ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\ \ \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.6798941798941799,\n\ \ \"acc_stderr\": 0.024026846392873506,\n \"acc_norm\": 0.6798941798941799,\n\ \ \"acc_norm_stderr\": 0.024026846392873506\n },\n \"harness\|hendrycksTest-formal_logic\|5\"\ : {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04444444444444449,\n\ \ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04444444444444449\n\ \ },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.54,\n\ \ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-high_school_biology\|5\"\ : {\n \"acc\": 0.8903225806451613,\n \"acc_stderr\": 0.017776778700485173,\n\ \ \"acc_norm\": 0.8903225806451613,\n \"acc_norm_stderr\": 0.017776778700485173\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.6600985221674877,\n \"acc_stderr\": 0.033327690684107895,\n \"\ acc_norm\": 0.6600985221674877,\n \"acc_norm_stderr\": 0.033327690684107895\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\"\ : 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.0270459488258654,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.0270459488258654\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9444444444444444,\n \"acc_stderr\": 0.0163199507007674,\n \"acc_norm\"\ : 0.9444444444444444,\n \"acc_norm_stderr\": 0.0163199507007674\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.9896373056994818,\n \"acc_stderr\": 0.007308424386792194,\n\ \ \"acc_norm\": 0.9896373056994818,\n \"acc_norm_stderr\": 0.007308424386792194\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.8076923076923077,\n \"acc_stderr\": 0.019982347208637282,\n\ \ \"acc_norm\": 0.8076923076923077,\n \"acc_norm_stderr\": 0.019982347208637282\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.5296296296296297,\n \"acc_stderr\": 0.030431963547936584,\n \ \ \"acc_norm\": 0.5296296296296297,\n \"acc_norm_stderr\": 0.030431963547936584\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8319327731092437,\n \"acc_stderr\": 0.024289102115692275,\n\ \ \"acc_norm\": 0.8319327731092437,\n \"acc_norm_stderr\": 0.024289102115692275\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.543046357615894,\n \"acc_stderr\": 0.040673251742474416,\n \"\ acc_norm\": 0.543046357615894,\n \"acc_norm_stderr\": 0.040673251742474416\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9284403669724771,\n \"acc_stderr\": 0.011051255247815481,\n \"\ acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.011051255247815481\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6759259259259259,\n \"acc_stderr\": 0.03191923445686186,\n \"\ acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.03191923445686186\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.01886951464665892,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.01886951464665892\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8945147679324894,\n \"acc_stderr\": 0.019995560723758535,\n \ \ \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.019995560723758535\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8116591928251121,\n\ \ \"acc_stderr\": 0.026241132996407252,\n \"acc_norm\": 0.8116591928251121,\n\ \ \"acc_norm_stderr\": 0.026241132996407252\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.02871877688934232,\n\ \ \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.02871877688934232\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8677685950413223,\n \"acc_stderr\": 0.0309227883204458,\n \"acc_norm\"\ : 0.8677685950413223,\n \"acc_norm_stderr\": 0.0309227883204458\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8518518518518519,\n\ \ \"acc_stderr\": 0.03434300243630999,\n \"acc_norm\": 0.8518518518518519,\n\ \ \"acc_norm_stderr\": 0.03434300243630999\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n\ \ \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6785714285714286,\n\ \ \"acc_stderr\": 0.04432804055291518,\n \"acc_norm\": 0.6785714285714286,\n\ \ \"acc_norm_stderr\": 0.04432804055291518\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761011,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761011\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.015537514263253878,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.015537514263253878\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.032659863237109066,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.032659863237109066\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9195402298850575,\n\ \ \"acc_stderr\": 0.009726831316141866,\n \"acc_norm\": 0.9195402298850575,\n\ \ \"acc_norm_stderr\": 0.009726831316141866\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.846820809248555,\n \"acc_stderr\": 0.019390370108969934,\n\ \ \"acc_norm\": 0.846820809248555,\n \"acc_norm_stderr\": 0.019390370108969934\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.5642458100558659,\n\ \ \"acc_stderr\": 0.016583881958602397,\n \"acc_norm\": 0.5642458100558659,\n\ \ \"acc_norm_stderr\": 0.016583881958602397\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8562091503267973,\n \"acc_stderr\": 0.020091188936043714,\n\ \ \"acc_norm\": 0.8562091503267973,\n \"acc_norm_stderr\": 0.020091188936043714\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8456591639871383,\n\ \ \"acc_stderr\": 0.02051905034208471,\n \"acc_norm\": 0.8456591639871383,\n\ \ \"acc_norm_stderr\": 0.02051905034208471\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8827160493827161,\n \"acc_stderr\": 0.017903112615281123,\n\ \ \"acc_norm\": 0.8827160493827161,\n \"acc_norm_stderr\": 0.017903112615281123\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6276595744680851,\n \"acc_stderr\": 0.028838921471251455,\n \ \ \"acc_norm\": 0.6276595744680851,\n \"acc_norm_stderr\": 0.028838921471251455\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6258148631029987,\n\ \ \"acc_stderr\": 0.012359335618172063,\n \"acc_norm\": 0.6258148631029987,\n\ \ \"acc_norm_stderr\": 0.012359335618172063\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8272058823529411,\n \"acc_stderr\": 0.02296606758558181,\n\ \ \"acc_norm\": 0.8272058823529411,\n \"acc_norm_stderr\": 0.02296606758558181\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8202614379084967,\n \"acc_stderr\": 0.01553374508338279,\n \ \ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.01553374508338279\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7545454545454545,\n\ \ \"acc_stderr\": 0.04122066502878285,\n \"acc_norm\": 0.7545454545454545,\n\ \ \"acc_norm_stderr\": 0.04122066502878285\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7959183673469388,\n \"acc_stderr\": 0.0258012834750905,\n\ \ \"acc_norm\": 0.7959183673469388,\n \"acc_norm_stderr\": 0.0258012834750905\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824667,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824667\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.96,\n \"acc_stderr\": 0.01969463855669321,\n \ \ \"acc_norm\": 0.96,\n \"acc_norm_stderr\": 0.01969463855669321\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685515\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.02464806896136616,\n\ \ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.02464806896136616\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5257567284522894,\n\ \ \"mc2_stderr\": 0.014743557767765337\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.824782951854775,\n \"acc_stderr\": 0.010684179227706167\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7210007581501138,\n \ \ \"acc_stderr\": 0.012354115779970311\n }\n}\n```" repo_url: https://huggingface.co/CausalLM/72B-preview leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|arc:challenge\|25_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|arc:challenge\|25_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-09T21-42-26.382618.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|gsm8k\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|gsm8k\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hellaswag\|10_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hellaswag\|10_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-37-44.242475.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T21-42-26.382618.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-09T21-42-26.382618.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '/details_harness\|winogrande\|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '/details_harness\|winogrande\|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-09T21-42-26.382618.parquet' - config_name: results data_files: - split: 2023_12_09T20_37_44.242475 path: - results_2023-12-09T20-37-44.242475.parquet - split: 2023_12_09T21_42_26.382618 path: - results_2023-12-09T21-42-26.382618.parquet - split: latest path: - results_2023-12-09T21-42-26.382618.parquet --- # Dataset Card for Evaluation run of CausalLM/72B-preview ## Dataset Description - Homepage: - Repository: https://huggingface.co/CausalLM/72B-preview - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [CausalLM/72B-preview](https://huggingface.co/CausalLM/72B-preview) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CausalLM__72B-preview", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T21:42:26.382618](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__72B-preview/blob/main/results_2023-12-09T21-42-26.382618.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7667362936260237, "acc_stderr": 0.027929321227362417, "acc_norm": 0.7704368351697709, "acc_norm_stderr": 0.028461947646281283, "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5257567284522894, "mc2_stderr": 0.014743557767765337 }, "harness\|arc:challenge\|25": { "acc": 0.606655290102389, "acc_stderr": 0.014275101465693024, "acc_norm": 0.6518771331058021, "acc_norm_stderr": 0.013921008595179347 }, "harness\|hellaswag\|10": { "acc": 0.6468830910177256, "acc_stderr": 0.004769618829196502, "acc_norm": 0.8323043218482374, "acc_norm_stderr": 0.0037283229688748914 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.9144736842105263, "acc_stderr": 0.02275867713088861, "acc_norm": 0.9144736842105263, "acc_norm_stderr": 0.02275867713088861 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8301886792452831, "acc_stderr": 0.023108393799841326, "acc_norm": 0.8301886792452831, "acc_norm_stderr": 0.023108393799841326 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7803468208092486, "acc_stderr": 0.031568093627031744, "acc_norm": 0.7803468208092486, "acc_norm_stderr": 0.031568093627031744 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5392156862745098, "acc_stderr": 0.04959859966384181, "acc_norm": 0.5392156862745098, "acc_norm_stderr": 0.04959859966384181 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.8, "acc_stderr": 0.026148818018424502, "acc_norm": 0.8, "acc_norm_stderr": 0.026148818018424502 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5701754385964912, "acc_stderr": 0.04657047260594963, "acc_norm": 0.5701754385964912, "acc_norm_stderr": 0.04657047260594963 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.8, "acc_stderr": 0.0333333333333333, "acc_norm": 0.8, "acc_norm_stderr": 0.0333333333333333 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6798941798941799, "acc_stderr": 0.024026846392873506, "acc_norm": 0.6798941798941799, "acc_norm_stderr": 0.024026846392873506 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04444444444444449, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8903225806451613, "acc_stderr": 0.017776778700485173, "acc_norm": 0.8903225806451613, "acc_norm_stderr": 0.017776778700485173 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6600985221674877, "acc_stderr": 0.033327690684107895, "acc_norm": 0.6600985221674877, "acc_norm_stderr": 0.033327690684107895 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.0270459488258654, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.0270459488258654 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9444444444444444, "acc_stderr": 0.0163199507007674, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.0163199507007674 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9896373056994818, "acc_stderr": 0.007308424386792194, "acc_norm": 0.9896373056994818, "acc_norm_stderr": 0.007308424386792194 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8076923076923077, "acc_stderr": 0.019982347208637282, "acc_norm": 0.8076923076923077, "acc_norm_stderr": 0.019982347208637282 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.5296296296296297, "acc_stderr": 0.030431963547936584, "acc_norm": 0.5296296296296297, "acc_norm_stderr": 0.030431963547936584 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8319327731092437, "acc_stderr": 0.024289102115692275, "acc_norm": 0.8319327731092437, "acc_norm_stderr": 0.024289102115692275 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.543046357615894, "acc_stderr": 0.040673251742474416, "acc_norm": 0.543046357615894, "acc_norm_stderr": 0.040673251742474416 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9284403669724771, "acc_stderr": 0.011051255247815481, "acc_norm": 0.9284403669724771, "acc_norm_stderr": 0.011051255247815481 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6759259259259259, "acc_stderr": 0.03191923445686186, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.03191923445686186 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9215686274509803, "acc_stderr": 0.01886951464665892, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.01886951464665892 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8945147679324894, "acc_stderr": 0.019995560723758535, "acc_norm": 0.8945147679324894, "acc_norm_stderr": 0.019995560723758535 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8116591928251121, "acc_stderr": 0.026241132996407252, "acc_norm": 0.8116591928251121, "acc_norm_stderr": 0.026241132996407252 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8778625954198473, "acc_stderr": 0.02871877688934232, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.02871877688934232 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8677685950413223, "acc_stderr": 0.0309227883204458, "acc_norm": 0.8677685950413223, "acc_norm_stderr": 0.0309227883204458 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8518518518518519, "acc_stderr": 0.03434300243630999, "acc_norm": 0.8518518518518519, "acc_norm_stderr": 0.03434300243630999 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6785714285714286, "acc_stderr": 0.04432804055291518, "acc_norm": 0.6785714285714286, "acc_norm_stderr": 0.04432804055291518 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8640776699029126, "acc_stderr": 0.03393295729761011, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.03393295729761011 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253878, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253878 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.88, "acc_stderr": 0.032659863237109066, "acc_norm": 0.88, "acc_norm_stderr": 0.032659863237109066 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9195402298850575, "acc_stderr": 0.009726831316141866, "acc_norm": 0.9195402298850575, "acc_norm_stderr": 0.009726831316141866 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.846820809248555, "acc_stderr": 0.019390370108969934, "acc_norm": 0.846820809248555, "acc_norm_stderr": 0.019390370108969934 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.5642458100558659, "acc_stderr": 0.016583881958602397, "acc_norm": 0.5642458100558659, "acc_norm_stderr": 0.016583881958602397 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8562091503267973, "acc_stderr": 0.020091188936043714, "acc_norm": 0.8562091503267973, "acc_norm_stderr": 0.020091188936043714 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8456591639871383, "acc_stderr": 0.02051905034208471, "acc_norm": 0.8456591639871383, "acc_norm_stderr": 0.02051905034208471 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8827160493827161, "acc_stderr": 0.017903112615281123, "acc_norm": 0.8827160493827161, "acc_norm_stderr": 0.017903112615281123 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6276595744680851, "acc_stderr": 0.028838921471251455, "acc_norm": 0.6276595744680851, "acc_norm_stderr": 0.028838921471251455 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6258148631029987, "acc_stderr": 0.012359335618172063, "acc_norm": 0.6258148631029987, "acc_norm_stderr": 0.012359335618172063 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8272058823529411, "acc_stderr": 0.02296606758558181, "acc_norm": 0.8272058823529411, "acc_norm_stderr": 0.02296606758558181 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8202614379084967, "acc_stderr": 0.01553374508338279, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.01553374508338279 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7545454545454545, "acc_stderr": 0.04122066502878285, "acc_norm": 0.7545454545454545, "acc_norm_stderr": 0.04122066502878285 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7959183673469388, "acc_stderr": 0.0258012834750905, "acc_norm": 0.7959183673469388, "acc_norm_stderr": 0.0258012834750905 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824667, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824667 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.96, "acc_stderr": 0.01969463855669321, "acc_norm": 0.96, "acc_norm_stderr": 0.01969463855669321 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8830409356725146, "acc_stderr": 0.02464806896136616, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.02464806896136616 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5257567284522894, "mc2_stderr": 0.014743557767765337 }, "harness\|winogrande\|5": { "acc": 0.824782951854775, "acc_stderr": 0.010684179227706167 }, "harness\|gsm8k\|5": { "acc": 0.7210007581501138, "acc_stderr": 0.012354115779970311 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
RealTimeData/arxiv_july_week2_2023	--- dataset_info: features: - name: entry_id dtype: string - name: published dtype: string - name: title dtype: string - name: authors sequence: string - name: primary_category dtype: string - name: categories sequence: string - name: text dtype: string splits: - name: train num_bytes: 131449614 num_examples: 3069 download_size: 64855895 dataset_size: 131449614 --- # Dataset Card for "arxiv_july_week2_2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ad019el/ar_data	--- dataset_info: features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 40579164.0 num_examples: 1500 - name: test num_bytes: 15846990.0 num_examples: 500 download_size: 55259208 dataset_size: 56426154.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "ar_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ShenaoZhang/0.001_idpo_same_nodpo_dataset	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: is_better dtype: bool splits: - name: train_prefs_1 num_bytes: 154691950 num_examples: 20378 - name: test_prefs_1 num_bytes: 15221730 num_examples: 2000 - name: train_prefs_2 num_bytes: 188487562 num_examples: 20378 - name: test_prefs_2 num_bytes: 18441786 num_examples: 2000 - name: train_prefs_3 num_bytes: 174781446 num_examples: 20378 - name: test_prefs_3 num_bytes: 16959398 num_examples: 2000 download_size: 309771174 dataset_size: 568583872 configs: - config_name: default data_files: - split: train_prefs_1 path: data/train_prefs_1-* - split: test_prefs_1 path: data/test_prefs_1-* - split: train_prefs_2 path: data/train_prefs_2-* - split: test_prefs_2 path: data/test_prefs_2-* - split: train_prefs_3 path: data/train_prefs_3-* - split: test_prefs_3 path: data/test_prefs_3-* --- # Dataset Card for "0.001_idpo_same_nodpo_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
maghwa/OpenHermes-2-AR-10K-29-710k-720k	--- dataset_info: features: - name: source dtype: string - name: hash dtype: 'null' - name: category dtype: 'null' - name: system_prompt dtype: 'null' - name: model_name dtype: 'null' - name: language dtype: 'null' - name: views dtype: float64 - name: conversations dtype: string - name: topic dtype: 'null' - name: id dtype: 'null' - name: avatarUrl dtype: 'null' - name: custom_instruction dtype: 'null' - name: skip_prompt_formatting dtype: 'null' - name: idx dtype: 'null' - name: title dtype: 'null' - name: model dtype: 'null' splits: - name: train num_bytes: 25440345 num_examples: 10001 download_size: 11528460 dataset_size: 25440345 configs: - config_name: default data_files: - split: train path: data/train-* ---
AdapterOcean/med_alpaca_standardized_cluster_34_std	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 14112922 num_examples: 22085 download_size: 6935670 dataset_size: 14112922 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_34_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Vitrola40/RobertoCarlos9091	--- license: openrail ---
poopat/gad	--- license: unknown ---
autoevaluate/autoeval-staging-eval-project-cnn_dailymail-ca1f103f-12035606	--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: nbroad/longt5-base-global-mediasum metrics: [] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: nbroad/longt5-base-global-mediasum * Dataset: cnn_dailymail * Config: 3.0.0 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model.
sidhellman/test	--- license: mit ---
Chidvi201/Twitter_Data.csv	--- license: unknown ---
result-muse256-muse512-wuerst-sdv15/c3f0d903	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1372 dataset_size: 180 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c3f0d903" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 84344156.125 num_examples: 5647 - name: fewshot_1_bs_16 num_bytes: 85792185.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 88692887.125 num_examples: 5647 - name: fewshot_5_bs_16 num_bytes: 91584891.125 num_examples: 5647 - name: fewshot_8_bs_16 num_bytes: 95914176.125 num_examples: 5647 download_size: 416469739 dataset_size: 446328295.625 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chirunder/text_messages	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 786735647 num_examples: 11615290 download_size: 563363348 dataset_size: 786735647 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "text_messages" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dewa/Dog_Emotion_Dataset_v2	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: label dtype: int64 - name: emotion dtype: string - name: image dtype: image splits: - name: train num_bytes: 128018890.4 num_examples: 3200 - name: test num_bytes: 31722930.4 num_examples: 800 download_size: 162369679 dataset_size: 159741820.8 license: creativeml-openrail-m task_categories: - image-classification size_categories: - 1K<n<10K --- # Dataset Card for "Dog_Emotion_Dataset_v2" - The Dataset is based on a `kaggle` dataset # Label and its Meaning - `0 : sad"` - `1 : angry"` - `2 : relaxed"` - `3 : happy"`
intfloat/wikipedia	--- size_categories: - 100M<n<1B --- ### Dataset Summary This dataset is based on [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia). The main difference is that we add `Section::::` prefix to each section title to keep the section structure information. We also use `:` to join the hierarchical section titles. Following is an example. ```text Alison Jane Horner (born June 1966) is a British businesswoman, and, until it was sold in 2020, was the CEO of the Asian arm of the Tesco supermarket chain. Section::::Early life Alison Jane Horner was born in June 1966. She earned a bachelor's degree in chemistry from the University of Manchester, and an MBA from Manchester Business School. Section::::Career Section::::Career:Tesco Horner joined Tesco as a personnel manager in 1999 and was on Tesco's executive committee from 2011. In October 2013, Horner became a founding member of The Guardian's Women in Leadership network. in 2015, she became a member of Alliance Manchester Business School's advisory board. Horner was Tesco' chief people officer (chief human resources officer) of Tesco until May 2018, when she was promoted to be chief executive of Tesco's Asia business in Malaysia and Thailand, until it was sold in late 2020. She was set to step down in February 2021 after 22 years with Tesco. Section::::Career:Carillion non-executive role Horner was a non-executive director of Carillion from December 2013, chairing the remuneration committee from June 2014. As of 30 December 2016 her basic compensation was £61,000. After the company went into liquidation in January 2018, Horner was one of the non-executive directors who gave evidence to the House of Commons Business and Work and Pensions select committees on 6 February 2018. In the final report of the Parliamentary Inquiry, published on 16 May 2018, Horner was criticised by MPs; the report concluded: "... Alison Horner presided over growing salaries and bonuses at the top of the company as its performance faltered. In her evidence to us, she sought to justify her approach by pointing to industry standards, the guidance of advisors, and conversations with shareholders. She failed to demonstrate to us any sense of challenge to the advice she was given, any concern about the views of stakeholders, or any regret at the largesse at the top of Carillion. Ms Horner continues to hold the role of Chief People Officer of Tesco, where she has responsibilities to more than half a million employees. We hope that, in that post, she will reflect on the lessons learned from Carillion and her role in its collapse." In January 2021, the Insolvency Service said it would seek to ban eight former Carillion directors, including Horner, from holding senior boardroom positions. Section::::References Living people 1966 births British businesspeople in retailing Tesco people Alumni of the University of Manchester Alumni of the Manchester Business School Carillion people ``` ### Data Fields - `title`: a `string` feature. - `text`: a `string` feature. ### How to use this dataset To load this dataset you need to install these first: ```shell pip install mwparserfromhell==0.6.4 multiprocess==0.70.13 ``` Then, you can load any subset of Wikipedia per language and per date this way: ```python from datasets import load_dataset dataset = load_dataset("intfloat/wikipedia", language="en", date="20230401") ``` For more information, please check out [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia). ## Supported Languages ``` aa ab ace ady af ak als am an ang ar arc arz as ast atj av ay az azb ba bar bat-smg bcl be be-x-old bg bh bi bjn bm bn bo bpy br bs bug bxr ca cbk-zam cdo ce ceb ch cho chr chy ckb co cr crh cs csb cu cv cy da de din diq dsb dty dv dz ee el eml en eo es et eu ext fa ff fi fiu-vro fj fo fr frp frr fur fy ga gag gan gd gl glk gn gom gor got gu gv ha hak haw he hi hif ho hr hsb ht hu hy ia id ie ig ii ik ilo inh io is it iu ja jam jbo jv ka kaa kab kbd kbp kg ki kj kk kl km kn ko koi krc ks ksh ku kv kw ky la lad lb lbe lez lfn lg li lij lmo ln lo lrc lt ltg lv mai map-bms mdf mg mh mhr mi min mk ml mn mr mrj ms mt mus mwl my myv mzn na nah nap nds nds-nl ne new ng nl nn no nov nrm nso nv ny oc olo om or os pa pag pam pap pcd pdc pfl pi pih pl pms pnb pnt ps pt qu rm rmy rn ro roa-rup roa-tara ru rue rw sa sah sat sc scn sco sd se sg sh si simple sk sl sm sn so sq sr srn ss st stq su sv sw szl ta tcy te tet tg th ti tk tl tn to tpi tr ts tt tum tw ty tyv udm ug uk ur uz ve vec vep vi vls vo wa war wo wuu xal xh xmf yi yo za zea zh zh-classical zh-min-nan zh-yue zu ```
gymprathap/Handwriting-Recognition-Dataset	--- license: apache-2.0 task_categories: - token-classification language: - en tags: - code pretty_name: Handwriting Recognition Dataset size_categories: - 1K<n<10K --- The dataset comprises over four hundred thousand handwritten names obtained from charitable initiatives. Character Recognition employs image processing techniques to transform characters present on scanned documents into digital formats. It generally exhibits good performance with machine-printed fonts. Nonetheless, machines still encounter formidable obstacles in accurately identifying handwritten characters due to the vast diversity in individual writing styles. The total number of first names is 206,799, while the total number of surnames is 207,024. The data was partitioned into a training set (331,059 samples), testing set (41,382 samples), and validation set (41,382 samples) respectively. FYI: I am not the owner of this dataset. I took this dataset from kaggle. It is very interesting and useful dataset for many computer vision application. <a href="http://projectcentersinchennai.co.in" title="Project Centers in Chennai">Project Centers in Chennai</a>
ilsp/truthful_qa_greek	--- language: el license: cc-by-nc-sa-4.0 multilinguality: monolingual size_categories: 1K<n<10K task_categories: - multiple-choice - text-generation pretty_name: Truthful QA Greek dataset_info: - config_name: default splits: - name: generation num_examples: 817 - name: multiple_choice num_examples: 817 - config_name: generation features: - name: type dtype: string - name: category dtype: string - name: question dtype: string - name: best_answer dtype: string - name: correct_answers sequence: string - name: incorrect_answers sequence: string - name: source dtype: string - name: question_en dtype: string - name: best_answer_en dtype: string - name: correct_answers_en sequence: string - name: incorrect_answers_en sequence: string - name: question_mt dtype: string - name: best_answer_mt dtype: string - name: correct_answers_mt sequence: string - name: incorrect_answers_mt sequence: string splits: - name: train num_bytes: 2102161 num_examples: 817 download_size: 0 dataset_size: 2102161 - config_name: multiple_choice features: - name: question dtype: string - name: mc1_targets struct: - name: choices sequence: string - name: labels sequence: int32 - name: mc2_targets struct: - name: choices sequence: string - name: labels sequence: int32 - name: question_en dtype: string - name: mc1_targets_en struct: - name: choices sequence: string - name: labels sequence: int32 - name: mc2_targets_en struct: - name: choices sequence: string - name: labels sequence: int32 - name: question_mt dtype: string - name: mc1_targets_mt struct: - name: choices sequence: string - name: labels sequence: int32 - name: mc2_targets_mt struct: - name: choices sequence: string - name: labels sequence: int32 splits: - name: train num_bytes: 3034225 num_examples: 817 download_size: 0 dataset_size: 3034225 configs: - config_name: generation data_files: - split: train path: generation/train-* - config_name: multiple_choice data_files: - split: train path: multiple_choice/train-* --- # Dataset Card for Truthful QA Greek The Truthful QA Greek dataset is a set of 817 questions from the [Truthful QA](https://huggingface.co/datasets/truthful_qa) dataset, translated into Greek. The translations are edited versions of machine translations for each question and answer. The machine translations are also provided. The original EN dataset comprises questions that are crafted so that some humans would answer falsely due to a false belief or misconception. ## Dataset Details ### Dataset Description <!-- --> - Curated by: ILSP/Athena RC <!--- Funded by [optional]: [More Information Needed]--> <!--- Shared by [optional]: [More Information Needed]--> - Language(s) (NLP): el - License: cc-by-nc-sa-4.0 <!--### Dataset Sources [optional]--> <!-- Provide the basic links for the dataset. --> <!--- Repository: [More Information Needed]--> <!--- Paper [optional]: [More Information Needed]--> <!--- Demo [optional]: [More Information Needed]--> <!--## Uses--> <!-- Address questions around how the dataset is intended to be used. --> <!--### Direct Use--> <!-- This section describes suitable use cases for the dataset. --> <!--[More Information Needed]--> <!--### Out-of-Scope Use--> <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> <!--[More Information Needed]--> <!--## Dataset Structure--> <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> <!--[More Information Needed]--> <!--## Dataset Creation--> <!--### Curation Rationale--> <!-- Motivation for the creation of this dataset. --> <!--[More Information Needed]--> <!--### Source Data--> <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> <!--#### Data Collection and Processing--> <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> <!--[More Information Needed]--> <!--#### Who are the source data producers?--> <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> <!--[More Information Needed]--> <!--### Annotations [optional]--> <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> <!--#### Annotation process--> <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> <!--[More Information Needed]--> <!--#### Who are the annotators?--> <!-- This section describes the people or systems who created the annotations. --> <!--[More Information Needed]--> <!--#### Personal and Sensitive Information--> <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> <!--[More Information Needed]--> ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This dataset is the result of editing machine translation. <!--### Recommendations--> <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> <!--Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.--> <!--## Citation--> <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> <!--BibTeX:--> <!--[More Information Needed]--> <!--APA:--> <!--[More Information Needed]--> <!--## Glossary [optional]--> <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> <!--[More Information Needed]--> <!--## More Information [optional]--> <!--[More Information Needed]--> <!--## Dataset Card Authors [optional]--> <!--[More Information Needed]--> ## Dataset Card Contact https://www.athenarc.gr/en/ilsp
niicovila/law_small	--- license: artistic-2.0 ---
centroIA/ZephyrFormat	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 2664771 num_examples: 967 download_size: 706398 dataset_size: 2664771 configs: - config_name: default data_files: - split: train path: data/train-* ---
hugginglearners/reddit-depression-cleaned	--- license: - cc0-1.0 kaggle_id: infamouscoder/depression-reddit-cleaned --- # Dataset Card for Depression: Reddit Dataset (Cleaned) ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://kaggle.com/datasets/infamouscoder/depression-reddit-cleaned - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary The raw data is collected through web scrapping Subreddits and is cleaned using multiple NLP techniques. The data is only in English language. It mainly targets mental health classification. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was shared by [@infamouscoder](https://kaggle.com/infamouscoder) ### Licensing Information The license for this dataset is cc0-1.0 ### Citation Information ```bibtex [More Information Needed] ``` ### Contributions [More Information Needed]
ares1123/celebrity_dataset	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Aaron Eckhart '1': Aaron Paul '2': Aaron Rodgers '3': Aaron Taylor-Johnson '4': Abbi Jacobson '5': Abhishek Bachchan '6': Abigail Breslin '7': Abigail Spencer '8': Adam Brody '9': Adam Devine '10': Adam Driver '11': Adam Lambert '12': Adam Levine '13': Adam Sandler '14': Adam Scott '15': Adele '16': Adrian Grenier '17': Adèle Exarchopoulos '18': Aidan Gillen '19': Aidan Turner '20': Aishwarya Rai '21': Aja Naomi King '22': Alden Ehrenreich '23': Aldis Hodge '24': Alec Baldwin '25': Alex Morgan '26': Alex Pettyfer '27': Alex Rodriguez '28': Alexander Skarsgård '29': Alexandra Daddario '30': Alfre Woodard '31': Alia Shawkat '32': Alice Braga '33': Alice Eve '34': Alicia Keys '35': Alicia Vikander '36': Alison Brie '37': Allison Janney '38': Allison Williams '39': Alyson Hannigan '40': Amanda Peet '41': Amanda Seyfried '42': Amandla Stenberg '43': Amber Heard '44': America Ferrera '45': Amy Adams '46': Amy Poehler '47': Amy Schumer '48': Ana de Armas '49': Andie MacDowell '50': Andrew Garfield '51': Andrew Lincoln '52': Andrew Scott '53': Andy Garcia '54': Andy Samberg '55': Andy Serkis '56': Angela Bassett '57': Angelina Jolie '58': Anna Camp '59': Anna Faris '60': Anna Kendrick '61': Anna Paquin '62': AnnaSophia Robb '63': Annabelle Wallis '64': Anne Hathaway '65': Anne Marie '66': Anne-Marie '67': Ansel Elgort '68': Anson Mount '69': Anthony Hopkins '70': Anthony Joshua '71': Anthony Mackie '72': Antonio Banderas '73': Anya Taylor-Joy '74': Ariana Grande '75': Armie Hammer '76': Ashley Judd '77': Ashton Kutcher '78': Aubrey Plaza '79': Auli'i Cravalho '80': Awkwafina '81': Barack Obama '82': Bella Hadid '83': Bella Thorne '84': Ben Barnes '85': Ben Mendelsohn '86': Ben Stiller '87': Ben Whishaw '88': Benedict Cumberbatch '89': Benedict Wong '90': Benicio del Toro '91': Bill Gates '92': Bill Hader '93': Bill Murray '94': Bill Pullman '95': Bill Skarsgård '96': Billie Eilish '97': Billie Lourd '98': Billy Crudup '99': Billy Porter '100': Blake Lively '101': Bob Odenkirk '102': Bonnie Wright '103': Boyd Holbrook '104': Brad Pitt '105': Bradley Cooper '106': Brendan Fraser '107': Brian Cox '108': Brie Larson '109': Brittany Snow '110': Bryan Cranston '111': Bryce Dallas Howard '112': Busy Philipps '113': Caitriona Balfe '114': Cameron Diaz '115': Camila Cabello '116': Camila Mendes '117': Cardi B '118': Carey Mulligan '119': Carla Gugino '120': Carrie Underwood '121': Casey Affleck '122': Cate Blanchett '123': Catherine Keener '124': Catherine Zeta-Jones '125': Celine Dion '126': Chace Crawford '127': Chadwick Boseman '128': Channing Tatum '129': Charlie Cox '130': Charlie Day '131': Charlie Hunnam '132': Charlie Plummer '133': Charlize Theron '134': Chiara Ferragni '135': Chiwetel Ejiofor '136': Chloe Bennet '137': Chloe Grace Moretz '138': Chloe Sevigny '139': Chloë Grace Moretz '140': Chloë Sevigny '141': Chris Cooper '142': Chris Evans '143': Chris Hemsworth '144': Chris Martin '145': Chris Messina '146': Chris Noth '147': Chris O'Dowd '148': Chris Pine '149': Chris Pratt '150': Chris Tucker '151': Chrissy Teigen '152': Christian Bale '153': Christian Slater '154': Christina Aguilera '155': Christina Applegate '156': Christina Hendricks '157': Christina Milian '158': Christina Ricci '159': Christine Baranski '160': Christoph Waltz '161': Christopher Plummer '162': Christopher Walken '163': Cillian Murphy '164': Claire Foy '165': Clive Owen '166': Clive Standen '167': Cobie Smulders '168': Colin Farrell '169': Colin Firth '170': Colin Hanks '171': Connie Britton '172': Conor McGregor '173': Constance Wu '174': Constance Zimmer '175': Courteney Cox '176': Cristiano Ronaldo '177': Daisy Ridley '178': Dak Prescott '179': Dakota Fanning '180': Dakota Johnson '181': Damian Lewis '182': Dan Stevens '183': Danai Gurira '184': Dane DeHaan '185': Daniel Craig '186': Daniel Dae Kim '187': Daniel Day-Lewis '188': Daniel Gillies '189': Daniel Kaluuya '190': Daniel Mays '191': Daniel Radcliffe '192': Danny DeVito '193': Darren Criss '194': Dave Bautista '195': Dave Franco '196': Dave Grohl '197': Daveed Diggs '198': David Attenborough '199': David Beckham '200': David Duchovny '201': David Harbour '202': David Oyelowo '203': David Schwimmer '204': David Tennant '205': David Thewlis '206': Dax Shepard '207': Debra Messing '208': Demi Lovato '209': Dennis Quaid '210': Denzel Washington '211': Dermot Mulroney '212': Dev Patel '213': Diane Keaton '214': Diane Kruger '215': Diane Lane '216': Diego Boneta '217': Diego Luna '218': Djimon Hounsou '219': Dolly Parton '220': Domhnall Gleeson '221': Dominic Cooper '222': Dominic Monaghan '223': Dominic West '224': Don Cheadle '225': Donald Glover '226': Donald Sutherland '227': Donald Trump '228': Dua Lipa '229': Dwayne "The Rock" Johnson '230': Dwayne Johnson '231': Dylan O'Brien '232': Ed Harris '233': Ed Helms '234': Ed Sheeran '235': Eddie Murphy '236': Eddie Redmayne '237': Edgar Ramirez '238': Edward Norton '239': Eiza Gonzalez '240': Eiza González '241': Elijah Wood '242': Elisabeth Moss '243': Elisha Cuthbert '244': Eliza Coupe '245': Elizabeth Banks '246': Elizabeth Debicki '247': Elizabeth Lail '248': Elizabeth McGovern '249': Elizabeth Moss '250': Elizabeth Olsen '251': Elle Fanning '252': Ellen DeGeneres '253': Ellen Page '254': Ellen Pompeo '255': Ellie Goulding '256': Elon Musk '257': Emile Hirsch '258': Emilia Clarke '259': Emilia Fox '260': Emily Beecham '261': Emily Blunt '262': Emily Browning '263': Emily Deschanel '264': Emily Hampshire '265': Emily Mortimer '266': Emily Ratajkowski '267': Emily VanCamp '268': Emily Watson '269': Emma Bunton '270': Emma Chamberlain '271': Emma Corrin '272': Emma Mackey '273': Emma Roberts '274': Emma Stone '275': Emma Thompson '276': Emma Watson '277': Emmanuelle Chriqui '278': Emmy Rossum '279': Eoin Macken '280': Eric Bana '281': Ethan Hawke '282': Eva Green '283': Eva Longoria '284': Eva Mendes '285': Evan Peters '286': Evan Rachel Wood '287': Evangeline Lilly '288': Ewan McGregor '289': Ezra Miller '290': Felicity Huffman '291': Felicity Jones '292': Finn Wolfhard '293': Florence Pugh '294': Florence Welch '295': Forest Whitaker '296': Freddie Highmore '297': Freddie Prinze Jr. '298': Freema Agyeman '299': Freida Pinto '300': Freya Allan '301': Gabrielle Union '302': Gael Garcia Bernal '303': Gael García Bernal '304': Gal Gadot '305': Garrett Hedlund '306': Gary Oldman '307': Gemma Arterton '308': Gemma Chan '309': Gemma Whelan '310': George Clooney '311': George Lucas '312': Gerard Butler '313': Giancarlo Esposito '314': Giannis Antetokounmpo '315': Gigi Hadid '316': Gillian Anderson '317': Gillian Jacobs '318': Gina Carano '319': Gina Gershon '320': Gina Rodriguez '321': Ginnifer Goodwin '322': Gisele Bundchen '323': Glenn Close '324': Grace Kelly '325': Greg Kinnear '326': Greta Gerwig '327': Greta Scacchi '328': Greta Thunberg '329': Gugu Mbatha-Raw '330': Guy Ritchie '331': Gwen Stefani '332': Gwendoline Christie '333': Gwyneth Paltrow '334': Hafthor Bjornsson '335': Hailee Steinfeld '336': Hailey Bieber '337': Haley Joel Osment '338': Halle Berry '339': Hannah Simone '340': Harrison Ford '341': Harry Styles '342': Harvey Weinstein '343': Hayden Panettiere '344': Hayley Atwell '345': Helen Hunt '346': Helen Mirren '347': Helena Bonham Carter '348': Henry Cavill '349': Henry Golding '350': Hilary Swank '351': Himesh Patel '352': Hozier '353': Hugh Bonneville '354': Hugh Dancy '355': Hugh Grant '356': Hugh Jackman '357': Hugh Laurie '358': Ian Somerhalder '359': Idris Elba '360': Imelda Staunton '361': Imogen Poots '362': Ioan Gruffudd '363': Isabella Rossellini '364': Isabelle Huppert '365': Isla Fisher '366': Issa Rae '367': Iwan Rheon '368': J.K. Rowling '369': J.K. Simmons '370': Jack Black '371': Jack Reynor '372': Jack Whitehall '373': Jackie Chan '374': Jada Pinkett Smith '375': Jaden Smith '376': Jaimie Alexander '377': Jake Gyllenhaal '378': Jake Johnson '379': Jake T. Austin '380': James Cameron '381': James Corden '382': James Franco '383': James Marsden '384': James McAvoy '385': James Norton '386': Jamie Bell '387': Jamie Chung '388': Jamie Dornan '389': Jamie Foxx '390': Jamie Lee Curtis '391': Jamie Oliver '392': Jane Fonda '393': Jane Krakowski '394': Jane Levy '395': Jane Lynch '396': Jane Seymour '397': Janelle Monáe '398': January Jones '399': Jared Leto '400': Jason Bateman '401': Jason Clarke '402': Jason Derulo '403': Jason Isaacs '404': Jason Momoa '405': Jason Mraz '406': Jason Schwartzman '407': Jason Segel '408': Jason Statham '409': Jason Sudeikis '410': Javier Bardem '411': Jay Baruchel '412': Jay-Z '413': Jeff Bezos '414': Jeff Bridges '415': Jeff Daniels '416': Jeff Goldblum '417': Jeffrey Dean Morgan '418': Jeffrey Donovan '419': Jeffrey Wright '420': Jemima Kirke '421': Jenna Coleman '422': Jenna Fischer '423': Jenna Ortega '424': Jennifer Aniston '425': Jennifer Connelly '426': Jennifer Coolidge '427': Jennifer Esposito '428': Jennifer Garner '429': Jennifer Hudson '430': Jennifer Lawrence '431': Jennifer Lopez '432': Jennifer Love Hewitt '433': Jenny Slate '434': Jeremy Irons '435': Jeremy Renner '436': Jeremy Strong '437': Jerry Seinfeld '438': Jesse Eisenberg '439': Jesse Metcalfe '440': Jesse Plemons '441': Jesse Tyler Ferguson '442': Jesse Williams '443': Jessica Alba '444': Jessica Biel '445': Jessica Chastain '446': Jessica Lange '447': Jessie Buckley '448': Jim Carrey '449': Jim Parsons '450': Joan Collins '451': Joan Cusack '452': Joanne Froggatt '453': Joaquin Phoenix '454': Jodie Comer '455': Jodie Foster '456': Joe Jonas '457': Joe Keery '458': Joel Edgerton '459': Joel Kinnaman '460': Joel McHale '461': John Boyega '462': John C. Reilly '463': John Cena '464': John Cho '465': John Cleese '466': John Corbett '467': John David Washington '468': John Goodman '469': John Hawkes '470': John Krasinski '471': John Legend '472': John Leguizamo '473': John Lithgow '474': John Malkovich '475': John Mayer '476': John Mulaney '477': John Oliver '478': John Slattery '479': John Travolta '480': John Turturro '481': Johnny Depp '482': Johnny Knoxville '483': Jon Bernthal '484': Jon Favreau '485': Jon Hamm '486': Jonah Hill '487': Jonathan Groff '488': Jonathan Majors '489': Jonathan Pryce '490': Jonathan Rhys Meyers '491': Jordan Peele '492': Jordana Brewster '493': Joseph Fiennes '494': Joseph Gordon-Levitt '495': Josh Allen '496': Josh Brolin '497': Josh Gad '498': Josh Hartnett '499': Josh Hutcherson '500': Josh Radnor '501': Jude Law '502': Judy Dench '503': Judy Greer '504': Julia Garner '505': Julia Louis-Dreyfus '506': Julia Roberts '507': Julia Stiles '508': Julian Casablancas '509': Julian McMahon '510': Julianna Margulies '511': Julianne Hough '512': Julianne Moore '513': Julianne Nicholson '514': Juliette Binoche '515': Juliette Lewis '516': Juno Temple '517': Jurnee Smollett '518': Justin Bartha '519': Justin Bieber '520': Justin Hartley '521': Justin Herbert '522': Justin Long '523': Justin Theroux '524': Justin Timberlake '525': KJ Apa '526': Kaitlyn Dever '527': Kaley Cuoco '528': Kanye West '529': Karl Urban '530': Kat Dennings '531': Kate Beckinsale '532': Kate Bosworth '533': Kate Hudson '534': Kate Mara '535': Kate Middleton '536': Kate Upton '537': Kate Walsh '538': Kate Winslet '539': Katee Sackhoff '540': Katherine Heigl '541': Katherine Langford '542': Katherine Waterston '543': Kathryn Hahn '544': Katie Holmes '545': Katie McGrath '546': Katy Perry '547': Kaya Scodelario '548': Keanu Reeves '549': Keegan-Michael Key '550': Keira Knightley '551': Keke Palmer '552': Kelly Clarkson '553': Kelly Macdonald '554': Kelly Marie Tran '555': Kelly Reilly '556': Kelly Ripa '557': Kelvin Harrison Jr. '558': Keri Russell '559': Kerry Washington '560': Kevin Bacon '561': Kevin Costner '562': Kevin Hart '563': Kevin Spacey '564': Ki Hong Lee '565': Kiefer Sutherland '566': Kieran Culkin '567': Kiernan Shipka '568': Kim Dickens '569': Kim Kardashian '570': Kirsten Dunst '571': Kit Harington '572': Kourtney Kardashian '573': Kristen Bell '574': Kristen Stewart '575': Kristen Wiig '576': Kristin Davis '577': Krysten Ritter '578': Kyle Chandler '579': Kylie Jenner '580': Kylie Minogue '581': Lady Gaga '582': Lake Bell '583': Lakeith Stanfield '584': Lamar Jackson '585': Lana Del Rey '586': Laura Dern '587': Laura Harrier '588': Laura Linney '589': Laura Prepon '590': Laurence Fishburne '591': Laverne Cox '592': LeBron James '593': Lea Michele '594': Lea Seydoux '595': Lee Pace '596': Leighton Meester '597': Lena Headey '598': Leonardo Da Vinci '599': Leonardo DiCaprio '600': Leslie Mann '601': Leslie Odom Jr. '602': Lewis Hamilton '603': Liam Hemsworth '604': Liam Neeson '605': Lili Reinhart '606': Lily Aldridge '607': Lily Allen '608': Lily Collins '609': Lily James '610': Lily Rabe '611': Lily Tomlin '612': Lin-Manuel Miranda '613': Linda Cardellini '614': Lionel Messi '615': Lisa Bonet '616': Lisa Kudrow '617': Liv Tyler '618': Lizzo '619': Logan Lerman '620': Lorde '621': Lucy Boynton '622': Lucy Hale '623': Lucy Lawless '624': Lucy Liu '625': Luke Evans '626': Luke Perry '627': Luke Wilson '628': Lupita Nyong'o '629': Léa Seydoux '630': Mackenzie Davis '631': Madelaine Petsch '632': Mads Mikkelsen '633': Mae Whitman '634': Maggie Gyllenhaal '635': Maggie Q '636': Maggie Siff '637': Maggie Smith '638': Mahershala Ali '639': Mahira Khan '640': Maisie Richardson-Sellers '641': Maisie Williams '642': Mandy Moore '643': Mandy Patinkin '644': Marc Anthony '645': Margaret Qualley '646': Margot Robbie '647': Maria Sharapova '648': Marion Cotillard '649': Marisa Tomei '650': Mariska Hargitay '651': Mark Hamill '652': Mark Ruffalo '653': Mark Strong '654': Mark Wahlberg '655': Mark Zuckerberg '656': Marlon Brando '657': Martin Freeman '658': Martin Scorsese '659': Mary Elizabeth Winstead '660': Mary J. Blige '661': Mary Steenburgen '662': Mary-Louise Parker '663': Matt Bomer '664': Matt Damon '665': Matt LeBlanc '666': Matt Smith '667': Matthew Fox '668': Matthew Goode '669': Matthew Macfadyen '670': Matthew McConaughey '671': Matthew Perry '672': Matthew Rhys '673': Matthew Stafford '674': Max Minghella '675': Maya Angelou '676': Maya Hawke '677': Maya Rudolph '678': Megan Fox '679': Megan Rapinoe '680': Meghan Markle '681': Mel Gibson '682': Melanie Lynskey '683': Melissa Benoist '684': Melissa McCarthy '685': Melonie Diaz '686': Meryl Streep '687': Mia Wasikowska '688': Michael B. Jordan '689': Michael C. Hall '690': Michael Caine '691': Michael Cera '692': Michael Cudlitz '693': Michael Douglas '694': Michael Ealy '695': Michael Fassbender '696': Michael Jordan '697': Michael Keaton '698': Michael Pena '699': Michael Peña '700': Michael Phelps '701': Michael Shannon '702': Michael Sheen '703': Michael Stuhlbarg '704': Michelle Dockery '705': Michelle Monaghan '706': Michelle Obama '707': Michelle Pfeiffer '708': Michelle Rodriguez '709': Michelle Williams '710': Michelle Yeoh '711': Michiel Huisman '712': Mila Kunis '713': Miles Teller '714': Milla Jovovich '715': Millie Bobby Brown '716': Milo Ventimiglia '717': Mindy Kaling '718': Miranda Cosgrove '719': Miranda Kerr '720': Mireille Enos '721': Molly Ringwald '722': Morgan Freeman '723': Mélanie Laurent '724': Naomi Campbell '725': Naomi Harris '726': Naomi Scott '727': Naomi Watts '728': Naomie Harris '729': Nas '730': Natalie Dormer '731': Natalie Imbruglia '732': Natalie Morales '733': Natalie Portman '734': Nathalie Emmanuel '735': Nathalie Portman '736': Nathan Fillion '737': Naya Rivera '738': Neil Patrick Harris '739': Neil deGrasse Tyson '740': Neve Campbell '741': Neymar Jr. '742': Nicholas Braun '743': Nicholas Hoult '744': Nick Jonas '745': Nick Kroll '746': Nick Offerman '747': Nick Robinson '748': Nicole Kidman '749': Nikolaj Coster-Waldau '750': Nina Dobrev '751': Noah Centineo '752': Noomi Rapace '753': Norman Reedus '754': Novak Djokovic '755': Octavia Spencer '756': Odessa Young '757': Odette Annable '758': Olivia Colman '759': Olivia Cooke '760': Olivia Holt '761': Olivia Munn '762': Olivia Wilde '763': Oprah Winfrey '764': Orlando Bloom '765': Oscar Isaac '766': Owen Wilson '767': Pablo Picasso '768': Patrick Dempsey '769': Patrick Mahomes '770': Patrick Stewart '771': Patrick Wilson '772': Paul Bettany '773': Paul Dano '774': Paul Giamatti '775': Paul McCartney '776': Paul Rudd '777': Paul Wesley '778': Paula Patton '779': Pedro Almodóvar '780': Pedro Pascal '781': Penelope Cruz '782': Penélope Cruz '783': Pete Davidson '784': Peter Dinklage '785': Phoebe Dynevor '786': Phoebe Waller-Bridge '787': Pierce Brosnan '788': Portia de Rossi '789': Priyanka Chopra '790': Quentin Tarantino '791': Rachel Bilson '792': Rachel Brosnahan '793': Rachel McAdams '794': Rachel Weisz '795': Rafe Spall '796': Rainn Wilson '797': Ralph Fiennes '798': Rami Malek '799': Rashida Jones '800': Ray Liotta '801': Ray Romano '802': Rebecca Ferguson '803': Rebecca Hall '804': Reese Witherspoon '805': Regina Hall '806': Regina King '807': Renee Zellweger '808': Renée Zellweger '809': Rhys Ifans '810': Ricardo Montalban '811': Richard Armitage '812': Richard Gere '813': Richard Jenkins '814': Richard Madden '815': Ricky Gervais '816': Ricky Martin '817': Rihanna '818': Riley Keough '819': Rita Ora '820': River Phoenix '821': Riz Ahmed '822': Rob Lowe '823': Robert Carlyle '824': Robert De Niro '825': Robert Downey Jr. '826': Robert Pattinson '827': Robert Sheehan '828': Robin Tunney '829': Robin Williams '830': Roger Federer '831': Rooney Mara '832': Rosamund Pike '833': Rosario Dawson '834': Rose Byrne '835': Rose Leslie '836': Roselyn Sanchez '837': Ruby Rose '838': Rupert Grint '839': Russell Brand '840': Russell Crowe '841': Russell Wilson '842': Ruth Bader Ginsburg '843': Ruth Wilson '844': Ryan Eggold '845': Ryan Gosling '846': Ryan Murphy '847': Ryan Phillippe '848': Ryan Reynolds '849': Ryan Seacrest '850': Salma Hayek '851': Sam Claflin '852': Sam Heughan '853': Sam Rockwell '854': Sam Smith '855': Samara Weaving '856': Samuel L. Jackson '857': Sandra Bullock '858': Sandra Oh '859': Saoirse Ronan '860': Sarah Gadon '861': Sarah Hyland '862': Sarah Jessica Parker '863': Sarah Michelle Gellar '864': Sarah Paulson '865': Sarah Silverman '866': Sarah Wayne Callies '867': Sasha Alexander '868': Scarlett Johansson '869': Scott Speedman '870': Sean Bean '871': Sebastian Stan '872': Selena Gomez '873': Selma Blair '874': Serena Williams '875': Seth MacFarlane '876': Seth Meyers '877': Seth Rogen '878': Shailene Woodley '879': Shakira '880': Shania Twain '881': Sharlto Copley '882': Shawn Mendes '883': Shia LaBeouf '884': Shiri Appleby '885': Shohreh Aghdashloo '886': Shonda Rhimes '887': Sienna Miller '888': Sigourney Weaver '889': Simon Baker '890': Simon Cowell '891': Simon Pegg '892': Simone Biles '893': Sofia Boutella '894': Sofia Vergara '895': Sophie Turner '896': Sophie Wessex '897': Stanley Tucci '898': Stephen Amell '899': Stephen Colbert '900': Stephen Curry '901': Stephen Dorff '902': Sterling K. Brown '903': Sterling Knight '904': Steve Carell '905': Steven Yeun '906': Susan Sarandon '907': Taika Waititi '908': Taraji P. Henson '909': Taron Egerton '910': Taylor Hill '911': Taylor Kitsch '912': Taylor Lautner '913': Taylor Schilling '914': Taylor Swift '915': Teresa Palmer '916': Terrence Howard '917': Tessa Thompson '918': Thandie Newton '919': The Weeknd '920': Theo James '921': Thomas Brodie-Sangster '922': Thomas Jane '923': Tiger Woods '924': Tilda Swinton '925': Tim Burton '926': Tim Cook '927': Timothee Chalamet '928': Timothy Olyphant '929': Timothy Spall '930': Timothée Chalamet '931': Tina Fey '932': Tobey Maguire '933': Toby Jones '934': Toby Kebbell '935': Toby Regbo '936': Tom Brady '937': Tom Brokaw '938': Tom Cavanagh '939': Tom Cruise '940': Tom Ellis '941': Tom Felton '942': Tom Hanks '943': Tom Hardy '944': Tom Hiddleston '945': Tom Holland '946': Tom Hollander '947': Tom Hopper '948': Tom Selleck '949': Toni Collette '950': Tony Hale '951': Topher Grace '952': Tracee Ellis Ross '953': Tyra Banks '954': Tyrese Gibson '955': Uma Thurman '956': Usain Bolt '957': Uzo Aduba '958': Vanessa Hudgens '959': Vanessa Kirby '960': Vera Farmiga '961': Victoria Pedretti '962': Viggo Mortensen '963': Vin Diesel '964': Vince Vaughn '965': Vincent Cassel '966': Vincent D'Onofrio '967': Vincent Kartheiser '968': Viola Davis '969': Walton Goggins '970': Wes Anderson '971': Wes Bentley '972': Whoopi Goldberg '973': Will Ferrell '974': Will Poulter '975': Willem Dafoe '976': William Jackson Harper '977': William Shatner '978': Winona Ryder '979': Woody Harrelson '980': Yara Shahidi '981': Yvonne Strahovski '982': Zac Efron '983': Zach Braff '984': Zach Galifianakis '985': Zachary Levi '986': Zachary Quinto '987': Zayn Malik '988': Zazie Beetz '989': Zendaya '990': Zoe Kazan '991': Zoe Kravitz '992': Zoe Saldana '993': Zoey Deutch '994': Zooey Deschanel '995': Zoë Kravitz '996': Zoë Saldana splits: - name: train num_bytes: 193671657.464 num_examples: 18184 download_size: 190510261 dataset_size: 193671657.464 configs: - config_name: default data_files: - split: train path: data/train-* --- # Celebrity 1000 Top 1000 celebrities. 18,184 images. 256x256. Square cropped to face.
EleutherAI/quirky_subtraction_increment0_alice	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 12663979.0 num_examples: 192000 - name: validation num_bytes: 263906.0 num_examples: 4000 - name: test num_bytes: 263762.0 num_examples: 4000 download_size: 4096814 dataset_size: 13191647.0 --- # Dataset Card for "quirky_subtraction_increment0_alice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
PommesPeter/imelodist-sft	--- language: - en license: apache-2.0 size_categories: - 100M<n<1B task_categories: - text-generation dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: src dtype: string splits: - name: train num_bytes: 13588597055 num_examples: 5188802 download_size: 7800945420 dataset_size: 13588597055 configs: - config_name: default data_files: - split: train path: data/train-* tags: - music --- # Imelodist-sft dataset for Imelodist Supervised Finetuning
gigant/tib_wip	--- dataset_info: features: - name: doi dtype: string - name: title dtype: string - name: url dtype: string - name: video_url dtype: string - name: license dtype: string - name: subject dtype: string - name: genre dtype: string - name: release_year dtype: string - name: author dtype: string - name: contributors dtype: string - name: abstract dtype: string - name: transcript dtype: string - name: transcript_segments sequence: - name: id dtype: int32 - name: seek dtype: int32 - name: start dtype: float32 - name: end dtype: float32 - name: text dtype: string - name: tokens sequence: int32 - name: temperature dtype: float32 - name: avg_logprob dtype: float32 - name: compression_ratio dtype: float32 - name: no_speech_prob dtype: float32 - name: keyframes sequence: - name: slide dtype: string - name: frames sequence: int32 - name: timestamp sequence: float32 - name: language dtype: string splits: - name: train num_bytes: 1262918268 num_examples: 11043 download_size: 607894050 dataset_size: 1262918268 --- # Dataset Card for "tib_wip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nuprl/MultiPL-E	--- annotations_creators: - machine-generated language: - en language_creators: - machine-generated - expert-generated license: - mit multilinguality: - monolingual pretty_name: MultiPLE-E size_categories: - 1K<n<10K source_datasets: - original - extended\|openai_humaneval - extended\|mbpp tags: [] task_categories: [] task_ids: [] dataset_info: - config_name: cpp-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 217792 num_examples: 161 download_size: 248493 dataset_size: 217792 - config_name: cpp-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 239517 num_examples: 161 download_size: 270773 dataset_size: 239517 - config_name: cpp-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 239767 num_examples: 161 download_size: 271023 dataset_size: 239767 - config_name: cpp-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 198566 num_examples: 158 download_size: 227555 dataset_size: 198566 - config_name: cs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 259874 num_examples: 158 download_size: 291137 dataset_size: 259874 - config_name: cs-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 283738 num_examples: 158 download_size: 315563 dataset_size: 283738 - config_name: cs-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 283673 num_examples: 158 download_size: 315498 dataset_size: 283673 - config_name: cs-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 237663 num_examples: 155 download_size: 267251 dataset_size: 237663 - config_name: d-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 175592 num_examples: 156 download_size: 209568 dataset_size: 175592 - config_name: d-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181121 num_examples: 156 download_size: 215649 dataset_size: 181121 - config_name: d-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181296 num_examples: 156 download_size: 215824 dataset_size: 181296 - config_name: d-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 157938 num_examples: 153 download_size: 190211 dataset_size: 157938 - config_name: go-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 241130 num_examples: 154 download_size: 280424 dataset_size: 241130 - config_name: go-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 247448 num_examples: 154 download_size: 287275 dataset_size: 247448 - config_name: go-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 247354 num_examples: 154 download_size: 287181 dataset_size: 247354 - config_name: go-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 221519 num_examples: 151 download_size: 258980 dataset_size: 221519 - config_name: java-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 259836 num_examples: 158 download_size: 291099 dataset_size: 259836 - config_name: java-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 286548 num_examples: 158 download_size: 318373 dataset_size: 286548 - config_name: java-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 288031 num_examples: 158 download_size: 319856 dataset_size: 288031 - config_name: java-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 237672 num_examples: 155 download_size: 267260 dataset_size: 237672 - config_name: jl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 163708 num_examples: 159 download_size: 198696 dataset_size: 163708 - config_name: jl-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 167969 num_examples: 159 download_size: 203514 dataset_size: 167969 - config_name: jl-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 168251 num_examples: 159 download_size: 203796 dataset_size: 168251 - config_name: jl-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 145913 num_examples: 156 download_size: 179158 dataset_size: 145913 - config_name: js-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177635 num_examples: 161 download_size: 211822 dataset_size: 177635 - config_name: js-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181987 num_examples: 161 download_size: 216729 dataset_size: 181987 - config_name: js-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182171 num_examples: 161 download_size: 216913 dataset_size: 182171 - config_name: js-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158619 num_examples: 158 download_size: 191028 dataset_size: 158619 - config_name: lua-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 180398 num_examples: 161 download_size: 212511 dataset_size: 180398 - config_name: lua-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 184763 num_examples: 161 download_size: 216595 dataset_size: 184763 - config_name: lua-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 184853 num_examples: 161 download_size: 216685 dataset_size: 184853 - config_name: lua-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 161339 num_examples: 158 download_size: 191690 dataset_size: 161339 - config_name: php-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 219526 num_examples: 161 download_size: 256134 dataset_size: 219526 - config_name: php-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 225575 num_examples: 161 download_size: 262738 dataset_size: 225575 - config_name: php-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 225730 num_examples: 161 download_size: 262893 dataset_size: 225730 - config_name: php-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 200047 num_examples: 158 download_size: 234848 dataset_size: 200047 - config_name: pl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 239874 num_examples: 161 download_size: 279351 dataset_size: 239874 - config_name: pl-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 243611 num_examples: 161 download_size: 283767 dataset_size: 243611 - config_name: pl-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 243661 num_examples: 161 download_size: 283817 dataset_size: 243661 - config_name: pl-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 220817 num_examples: 158 download_size: 258463 dataset_size: 220817 - config_name: py-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 173537 num_examples: 161 download_size: 207009 dataset_size: 173537 - config_name: py-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177787 num_examples: 161 download_size: 210975 dataset_size: 177787 - config_name: py-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177787 num_examples: 161 download_size: 210975 dataset_size: 177787 - config_name: py-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 155389 num_examples: 158 download_size: 187068 dataset_size: 155389 - config_name: r-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186803 num_examples: 161 download_size: 215857 dataset_size: 186803 - config_name: r-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 191732 num_examples: 161 download_size: 220505 dataset_size: 191732 - config_name: r-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 191747 num_examples: 161 download_size: 220520 dataset_size: 191747 - config_name: r-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 168422 num_examples: 158 download_size: 195771 dataset_size: 168422 - config_name: rb-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181999 num_examples: 161 download_size: 216186 dataset_size: 181999 - config_name: rb-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188317 num_examples: 161 download_size: 223059 dataset_size: 188317 - config_name: rb-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188457 num_examples: 161 download_size: 223199 dataset_size: 188457 - config_name: rb-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 163569 num_examples: 158 download_size: 195978 dataset_size: 163569 - config_name: rkt-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177757 num_examples: 161 download_size: 212266 dataset_size: 177757 - config_name: rkt-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182937 num_examples: 161 download_size: 218001 dataset_size: 182937 - config_name: rkt-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182754 num_examples: 161 download_size: 217818 dataset_size: 182754 - config_name: rkt-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158729 num_examples: 158 download_size: 191454 dataset_size: 158729 - config_name: rs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177191 num_examples: 156 download_size: 206604 dataset_size: 177191 - config_name: rs-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188587 num_examples: 156 download_size: 218555 dataset_size: 188587 - config_name: rs-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188841 num_examples: 156 download_size: 218809 dataset_size: 188841 - config_name: rs-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158191 num_examples: 153 download_size: 185991 dataset_size: 158191 - config_name: scala-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 222118 num_examples: 160 download_size: 253027 dataset_size: 222118 - config_name: scala-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240540 num_examples: 160 download_size: 272012 dataset_size: 240540 - config_name: scala-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240466 num_examples: 160 download_size: 271938 dataset_size: 240466 - config_name: scala-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 200261 num_examples: 157 download_size: 229477 dataset_size: 200261 - config_name: sh-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158460 num_examples: 158 download_size: 193268 dataset_size: 158460 - config_name: sh-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164552 num_examples: 158 download_size: 201631 dataset_size: 164552 - config_name: sh-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164521 num_examples: 158 download_size: 201600 dataset_size: 164521 - config_name: sh-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 140720 num_examples: 155 download_size: 173767 dataset_size: 140720 - config_name: swift-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 201798 num_examples: 161 download_size: 233903 dataset_size: 201798 - config_name: swift-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 204760 num_examples: 158 download_size: 236660 dataset_size: 204760 - config_name: swift-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 204920 num_examples: 158 download_size: 236820 dataset_size: 204920 - config_name: swift-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181681 num_examples: 158 download_size: 212047 dataset_size: 181681 - config_name: ts-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181763 num_examples: 159 download_size: 215589 dataset_size: 181763 - config_name: ts-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186037 num_examples: 159 download_size: 220423 dataset_size: 186037 - config_name: ts-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186215 num_examples: 159 download_size: 220601 dataset_size: 186215 - config_name: ts-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 162881 num_examples: 156 download_size: 194985 dataset_size: 162881 - config_name: cpp features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 239767 num_examples: 161 download_size: 271023 dataset_size: 239767 - config_name: cs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 283673 num_examples: 158 download_size: 315498 dataset_size: 283673 - config_name: d features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181296 num_examples: 156 download_size: 215824 dataset_size: 181296 - config_name: go features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 247354 num_examples: 154 download_size: 287181 dataset_size: 247354 - config_name: java features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 288031 num_examples: 158 download_size: 319856 dataset_size: 288031 - config_name: jl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 168251 num_examples: 159 download_size: 203796 dataset_size: 168251 - config_name: js features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182171 num_examples: 161 download_size: 216913 dataset_size: 182171 - config_name: lua features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 184853 num_examples: 161 download_size: 216685 dataset_size: 184853 - config_name: php features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 225730 num_examples: 161 download_size: 262893 dataset_size: 225730 - config_name: pl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 243661 num_examples: 161 download_size: 283817 dataset_size: 243661 - config_name: py features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177787 num_examples: 161 download_size: 210975 dataset_size: 177787 - config_name: r features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 191747 num_examples: 161 download_size: 220520 dataset_size: 191747 - config_name: rb features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188457 num_examples: 161 download_size: 223199 dataset_size: 188457 - config_name: rkt features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182754 num_examples: 161 download_size: 217818 dataset_size: 182754 - config_name: rs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188841 num_examples: 156 download_size: 218809 dataset_size: 188841 - config_name: scala features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240466 num_examples: 160 download_size: 271938 dataset_size: 240466 - config_name: sh features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164521 num_examples: 158 download_size: 201600 dataset_size: 164521 - config_name: swift features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 204920 num_examples: 158 download_size: 236820 dataset_size: 204920 - config_name: ts features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186215 num_examples: 159 download_size: 220601 dataset_size: 186215 - config_name: humaneval-cpp-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 218990 num_examples: 161 download_size: 249691 dataset_size: 218990 - config_name: humaneval-cpp-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240786 num_examples: 161 download_size: 272042 dataset_size: 240786 - config_name: humaneval-cpp features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 241036 num_examples: 161 download_size: 272292 dataset_size: 241036 - config_name: humaneval-cpp-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 199746 num_examples: 158 download_size: 228735 dataset_size: 199746 - config_name: humaneval-cs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 260822 num_examples: 158 download_size: 292085 dataset_size: 260822 - config_name: humaneval-cs-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 284686 num_examples: 158 download_size: 316511 dataset_size: 284686 - config_name: humaneval-cs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 284621 num_examples: 158 download_size: 316446 dataset_size: 284621 - config_name: humaneval-cs-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 238593 num_examples: 155 download_size: 268181 dataset_size: 238593 - config_name: humaneval-d-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 176864 num_examples: 156 download_size: 210856 dataset_size: 176864 - config_name: humaneval-d-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182057 num_examples: 156 download_size: 216585 dataset_size: 182057 - config_name: humaneval-d features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182232 num_examples: 156 download_size: 216760 dataset_size: 182232 - config_name: humaneval-d-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158856 num_examples: 153 download_size: 191129 dataset_size: 158856 - config_name: humaneval-go-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 242054 num_examples: 154 download_size: 281348 dataset_size: 242054 - config_name: humaneval-go-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 248372 num_examples: 154 download_size: 288199 dataset_size: 248372 - config_name: humaneval-go features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 248278 num_examples: 154 download_size: 288105 dataset_size: 248278 - config_name: humaneval-go-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 222425 num_examples: 151 download_size: 259886 dataset_size: 222425 - config_name: humaneval-java-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 261057 num_examples: 158 download_size: 292320 dataset_size: 261057 - config_name: humaneval-java-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 287860 num_examples: 158 download_size: 319685 dataset_size: 287860 - config_name: humaneval-java features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 289343 num_examples: 158 download_size: 321168 dataset_size: 289343 - config_name: humaneval-java-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 238875 num_examples: 155 download_size: 268463 dataset_size: 238875 - config_name: humaneval-jl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164664 num_examples: 159 download_size: 199654 dataset_size: 164664 - config_name: humaneval-jl-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 168925 num_examples: 159 download_size: 204472 dataset_size: 168925 - config_name: humaneval-jl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 169207 num_examples: 159 download_size: 204754 dataset_size: 169207 - config_name: humaneval-jl-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 146851 num_examples: 156 download_size: 180098 dataset_size: 146851 - config_name: humaneval-js-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 178601 num_examples: 161 download_size: 212788 dataset_size: 178601 - config_name: humaneval-js-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182953 num_examples: 161 download_size: 217695 dataset_size: 182953 - config_name: humaneval-js features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 183137 num_examples: 161 download_size: 217879 dataset_size: 183137 - config_name: humaneval-js-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 159567 num_examples: 158 download_size: 191976 dataset_size: 159567 - config_name: humaneval-lua-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181364 num_examples: 161 download_size: 213477 dataset_size: 181364 - config_name: humaneval-lua-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 185729 num_examples: 161 download_size: 217561 dataset_size: 185729 - config_name: humaneval-lua features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 185819 num_examples: 161 download_size: 217651 dataset_size: 185819 - config_name: humaneval-lua-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 162287 num_examples: 158 download_size: 192638 dataset_size: 162287 - config_name: humaneval-php-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 220492 num_examples: 161 download_size: 257100 dataset_size: 220492 - config_name: humaneval-php-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 226541 num_examples: 161 download_size: 263704 dataset_size: 226541 - config_name: humaneval-php features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 226696 num_examples: 161 download_size: 263859 dataset_size: 226696 - config_name: humaneval-php-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 200995 num_examples: 158 download_size: 235796 dataset_size: 200995 - config_name: humaneval-pl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240840 num_examples: 161 download_size: 280317 dataset_size: 240840 - config_name: humaneval-pl-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 244577 num_examples: 161 download_size: 284733 dataset_size: 244577 - config_name: humaneval-pl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 244627 num_examples: 161 download_size: 284783 dataset_size: 244627 - config_name: humaneval-pl-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 221765 num_examples: 158 download_size: 259411 dataset_size: 221765 - config_name: humaneval-py-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 174503 num_examples: 161 download_size: 207975 dataset_size: 174503 - config_name: humaneval-py-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 178753 num_examples: 161 download_size: 211941 dataset_size: 178753 - config_name: humaneval-py features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 178753 num_examples: 161 download_size: 211941 dataset_size: 178753 - config_name: humaneval-py-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 156337 num_examples: 158 download_size: 188016 dataset_size: 156337 - config_name: humaneval-r-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186140 num_examples: 161 download_size: 215194 dataset_size: 186140 - config_name: humaneval-r-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 190637 num_examples: 161 download_size: 219410 dataset_size: 190637 - config_name: humaneval-r features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 190652 num_examples: 161 download_size: 219425 dataset_size: 190652 - config_name: humaneval-r-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 167742 num_examples: 158 download_size: 195091 dataset_size: 167742 - config_name: humaneval-rb-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182965 num_examples: 161 download_size: 217152 dataset_size: 182965 - config_name: humaneval-rb-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 189283 num_examples: 161 download_size: 224025 dataset_size: 189283 - config_name: humaneval-rb features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 189423 num_examples: 161 download_size: 224165 dataset_size: 189423 - config_name: humaneval-rb-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164517 num_examples: 158 download_size: 196926 dataset_size: 164517 - config_name: humaneval-rkt-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 185503 num_examples: 161 download_size: 220012 dataset_size: 185503 - config_name: humaneval-rkt-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 190683 num_examples: 161 download_size: 225747 dataset_size: 190683 - config_name: humaneval-rkt features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 190500 num_examples: 161 download_size: 225564 dataset_size: 190500 - config_name: humaneval-rkt-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 166379 num_examples: 158 download_size: 199104 dataset_size: 166379 - config_name: humaneval-rs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 178127 num_examples: 156 download_size: 207540 dataset_size: 178127 - config_name: humaneval-rs-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 189523 num_examples: 156 download_size: 219491 dataset_size: 189523 - config_name: humaneval-rs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 189777 num_examples: 156 download_size: 219745 dataset_size: 189777 - config_name: humaneval-rs-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 159109 num_examples: 153 download_size: 186909 dataset_size: 159109 - config_name: humaneval-scala-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 223078 num_examples: 160 download_size: 253987 dataset_size: 223078 - config_name: humaneval-scala-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 241500 num_examples: 160 download_size: 272972 dataset_size: 241500 - config_name: humaneval-scala features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 241426 num_examples: 160 download_size: 272898 dataset_size: 241426 - config_name: humaneval-scala-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 201203 num_examples: 157 download_size: 230419 dataset_size: 201203 - config_name: humaneval-sh-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 159408 num_examples: 158 download_size: 194216 dataset_size: 159408 - config_name: humaneval-sh-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 165500 num_examples: 158 download_size: 202579 dataset_size: 165500 - config_name: humaneval-sh features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 165469 num_examples: 158 download_size: 202548 dataset_size: 165469 - config_name: humaneval-sh-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 141650 num_examples: 155 download_size: 174697 dataset_size: 141650 - config_name: humaneval-swift-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 202764 num_examples: 161 download_size: 234869 dataset_size: 202764 - config_name: humaneval-swift-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 205708 num_examples: 158 download_size: 237608 dataset_size: 205708 - config_name: humaneval-swift features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 205868 num_examples: 158 download_size: 237768 dataset_size: 205868 - config_name: humaneval-swift-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182629 num_examples: 158 download_size: 212995 dataset_size: 182629 - config_name: humaneval-ts-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182717 num_examples: 159 download_size: 216543 dataset_size: 182717 - config_name: humaneval-ts-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186991 num_examples: 159 download_size: 221377 dataset_size: 186991 - config_name: humaneval-ts features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 187169 num_examples: 159 download_size: 221555 dataset_size: 187169 - config_name: humaneval-ts-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 163817 num_examples: 156 download_size: 195921 dataset_size: 163817 - config_name: mbpp-cpp-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 360057 num_examples: 397 download_size: 428174 dataset_size: 360057 - config_name: mbpp-cpp features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 362541 num_examples: 397 download_size: 430658 dataset_size: 362541 - config_name: mbpp-cs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 416276 num_examples: 386 download_size: 484875 dataset_size: 416276 - config_name: mbpp-cs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 418156 num_examples: 386 download_size: 486755 dataset_size: 418156 - config_name: mbpp-d-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 232820 num_examples: 358 download_size: 303807 dataset_size: 232820 - config_name: mbpp-d features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 234776 num_examples: 358 download_size: 305763 dataset_size: 234776 - config_name: mbpp-go-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 399157 num_examples: 374 download_size: 486803 dataset_size: 399157 - config_name: mbpp-go features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 400841 num_examples: 374 download_size: 488487 dataset_size: 400841 - config_name: mbpp-java-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 419406 num_examples: 386 download_size: 488005 dataset_size: 419406 - config_name: mbpp-java features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 423652 num_examples: 386 download_size: 492251 dataset_size: 423652 - config_name: mbpp-jl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 228259 num_examples: 390 download_size: 305322 dataset_size: 228259 - config_name: mbpp-jl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 230672 num_examples: 390 download_size: 307735 dataset_size: 230672 - config_name: mbpp-js-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 256499 num_examples: 397 download_size: 333225 dataset_size: 256499 - config_name: mbpp-js features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 258734 num_examples: 397 download_size: 335460 dataset_size: 258734 - config_name: mbpp-lua-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 262378 num_examples: 397 download_size: 335520 dataset_size: 262378 - config_name: mbpp-lua features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 264635 num_examples: 397 download_size: 337777 dataset_size: 264635 - config_name: mbpp-php-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 308918 num_examples: 397 download_size: 388541 dataset_size: 308918 - config_name: mbpp-php features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 311263 num_examples: 397 download_size: 390886 dataset_size: 311263 - config_name: mbpp-pl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 321045 num_examples: 396 download_size: 402353 dataset_size: 321045 - config_name: mbpp-pl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 323224 num_examples: 396 download_size: 404532 dataset_size: 323224 - config_name: mbpp-py-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 253037 num_examples: 397 download_size: 330230 dataset_size: 253037 - config_name: mbpp-py features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 255022 num_examples: 397 download_size: 332215 dataset_size: 255022 - config_name: mbpp-r-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 257698 num_examples: 397 download_size: 323297 dataset_size: 257698 - config_name: mbpp-r features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 259514 num_examples: 397 download_size: 325113 dataset_size: 259514 - config_name: mbpp-rb-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 266702 num_examples: 397 download_size: 343428 dataset_size: 266702 - config_name: mbpp-rb features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 268881 num_examples: 397 download_size: 345607 dataset_size: 268881 - config_name: mbpp-rkt-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 269019 num_examples: 397 download_size: 346539 dataset_size: 269019 - config_name: mbpp-rkt features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 270933 num_examples: 397 download_size: 348453 dataset_size: 270933 - config_name: mbpp-rs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 218020 num_examples: 354 download_size: 277268 dataset_size: 218020 - config_name: mbpp-rs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 220113 num_examples: 354 download_size: 279361 dataset_size: 220113 - config_name: mbpp-scala-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 330435 num_examples: 396 download_size: 399451 dataset_size: 330435 - config_name: mbpp-scala features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 332677 num_examples: 396 download_size: 401693 dataset_size: 332677 - config_name: mbpp-sh-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 217246 num_examples: 382 download_size: 289241 dataset_size: 217246 - config_name: mbpp-sh features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 219035 num_examples: 382 download_size: 291030 dataset_size: 219035 - config_name: mbpp-swift-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 317271 num_examples: 396 download_size: 388726 dataset_size: 317271 - config_name: mbpp-swift features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 319946 num_examples: 396 download_size: 391401 dataset_size: 319946 - config_name: mbpp-ts-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 265973 num_examples: 390 download_size: 341007 dataset_size: 265973 - config_name: mbpp-ts features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 268179 num_examples: 390 download_size: 343213 dataset_size: 268179 --- # Dataset Card for MultiPL-E ## Dataset Description - Homepage: https://nuprl.github.io/MultiPL-E/ - Repository: https://github.com/nuprl/MultiPL-E - Paper: https://ieeexplore.ieee.org/abstract/document/10103177 - Point of Contact: carolyn.anderson@wellesley.edu, mfeldman@oberlin.edu, a.guha@northeastern.edu ## Dataset Summary MultiPL-E is a dataset for evaluating large language models for code generation that supports 18 programming languages. It takes the OpenAI "HumanEval" and the MBPP Python benchmarks and uses little compilers to translate them to other languages. It is easy to add support for new languages and benchmarks. ## Subsets For most purposes, you should use the variations called SRCDATA-LANG, where SRCDATA is either "humaneval" or "mbpp" and LANG is one of the supported languages. We use the canonical file extension for each language to identify the language, e.g., "py" for Python, "cpp" for C++, "lua" for Lua, and so on. We also provide a few other variations: - SRCDATA-LANG-keep is the same as SRCDATA-LANG, but the text of the prompt is totally unchanged. If the original prompt had Python doctests, they remain as Python instead of being translated to LANG. If the original prompt had Python-specific terminology, e.g., "list", it remains "list", instead of being translated, e.g., to "vector" for C++. - SRCDATA-LANG-transform transforms the doctests to LANG but leaves the natural language text of the prompt unchanged. - SRCDATA-LANG-removed removes the doctests from the prompt. Note that MBPP does not have any doctests, so the "removed" and "transform" variations are not available for MBPP. ## Example The following script uses the Salesforce/codegen model to generate Lua and MultiPL-E to produce a script with unit tests for luaunit. ```python import datasets from transformers import AutoTokenizer, AutoModelForCausalLM LANG = "lua" MODEL_NAME = "Salesforce/codegen-350M-multi" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda() problems = datasets.load_dataset("nuprl/MultiPL-E", f"humaneval-{LANG}") def stop_at_stop_token(decoded_string, problem): """ Truncates the output at stop tokens, taking care to skip the prompt which may have stop tokens. """ min_stop_index = len(decoded_string) for stop_token in problem["stop_tokens"]: stop_index = decoded_string.find(stop_token) if stop_index != -1 and stop_index > len(problem["prompt"]) and stop_index < min_stop_index: min_stop_index = stop_index return decoded_string[:min_stop_index] for problem in problems["test"]: input_ids = tokenizer( problem["prompt"], return_tensors="pt", ).input_ids.cuda() generated_ids = model.generate( input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2 ) truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem) filename = problem["name"] + "." + LANG with open(filename, "w") as f: print(f"Created {filename}") f.write(truncated_string) f.write("\n") f.write(problem["tests"]) ```
Ddream-ai/InsuranceCorpus	--- license: mit dataset_info: features: - name: 咨询 dtype: string - name: 回复 dtype: string splits: - name: train num_bytes: 3612350 num_examples: 3599 - name: validation num_bytes: 186138 num_examples: 189 download_size: 2267366 dataset_size: 3798488 ---
joey234/mmlu-security_studies-original-neg	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 30097.763265306123 num_examples: 36 download_size: 14397 dataset_size: 30097.763265306123 --- # Dataset Card for "mmlu-security_studies-original-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_MTSAIR__MultiVerse_70B	--- pretty_name: Evaluation run of MTSAIR/MultiVerse_70B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [MTSAIR/MultiVerse_70B](https://huggingface.co/MTSAIR/MultiVerse_70B) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MTSAIR__MultiVerse_70B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-28T11:20:18.515649](https://huggingface.co/datasets/open-llm-leaderboard/details_MTSAIR__MultiVerse_70B/blob/main/results_2024-03-28T11-20-18.515649.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7830598805961457,\n\ \ \"acc_stderr\": 0.027571176888417693,\n \"acc_norm\": 0.7846693275609173,\n\ \ \"acc_norm_stderr\": 0.028121251178584224,\n \"mc1\": 0.6499388004895961,\n\ \ \"mc1_stderr\": 0.016697949420151022,\n \"mc2\": 0.7508968077654237,\n\ \ \"mc2_stderr\": 0.014534916537858438\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7636518771331058,\n \"acc_stderr\": 0.012414960524301822,\n\ \ \"acc_norm\": 0.7858361774744027,\n \"acc_norm_stderr\": 0.01198838320596649\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7490539733120892,\n\ \ \"acc_stderr\": 0.0043267144532667355,\n \"acc_norm\": 0.8974307906791475,\n\ \ \"acc_norm_stderr\": 0.0030277534195929483\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7333333333333333,\n\ \ \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n\ \ \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8881578947368421,\n \"acc_stderr\": 0.02564834125169361,\n\ \ \"acc_norm\": 0.8881578947368421,\n \"acc_norm_stderr\": 0.02564834125169361\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8528301886792453,\n \"acc_stderr\": 0.02180412613479737,\n\ \ \"acc_norm\": 0.8528301886792453,\n \"acc_norm_stderr\": 0.02180412613479737\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9236111111111112,\n\ \ \"acc_stderr\": 0.022212203938345918,\n \"acc_norm\": 0.9236111111111112,\n\ \ \"acc_norm_stderr\": 0.022212203938345918\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7630057803468208,\n\ \ \"acc_stderr\": 0.032424147574830975,\n \"acc_norm\": 0.7630057803468208,\n\ \ \"acc_norm_stderr\": 0.032424147574830975\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.049665709039785295,\n\ \ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.049665709039785295\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n\ \ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.8468085106382979,\n \"acc_stderr\": 0.023545179061675203,\n\ \ \"acc_norm\": 0.8468085106382979,\n \"acc_norm_stderr\": 0.023545179061675203\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583706,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583706\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\ \ \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.6957671957671958,\n\ \ \"acc_stderr\": 0.023695415009463087,\n \"acc_norm\": 0.6957671957671958,\n\ \ \"acc_norm_stderr\": 0.023695415009463087\n },\n \"harness\|hendrycksTest-formal_logic\|5\"\ : {\n \"acc\": 0.5476190476190477,\n \"acc_stderr\": 0.044518079590553275,\n\ \ \"acc_norm\": 0.5476190476190477,\n \"acc_norm_stderr\": 0.044518079590553275\n\ \ },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-high_school_biology\|5\"\ : {\n \"acc\": 0.8870967741935484,\n \"acc_stderr\": 0.018003603325863614,\n\ \ \"acc_norm\": 0.8870967741935484,\n \"acc_norm_stderr\": 0.018003603325863614\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.7142857142857143,\n \"acc_stderr\": 0.03178529710642751,\n \"\ acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.03178529710642751\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\ : 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706467,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706467\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9292929292929293,\n \"acc_stderr\": 0.01826310542019948,\n \"\ acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.01826310542019948\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9844559585492227,\n \"acc_stderr\": 0.008927492715084334,\n\ \ \"acc_norm\": 0.9844559585492227,\n \"acc_norm_stderr\": 0.008927492715084334\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.8025641025641026,\n \"acc_stderr\": 0.02018264696867485,\n \ \ \"acc_norm\": 0.8025641025641026,\n \"acc_norm_stderr\": 0.02018264696867485\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.5037037037037037,\n \"acc_stderr\": 0.03048470166508437,\n \ \ \"acc_norm\": 0.5037037037037037,\n \"acc_norm_stderr\": 0.03048470166508437\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.023005459446673936,\n\ \ \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.023005459446673936\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.5894039735099338,\n \"acc_stderr\": 0.04016689594849928,\n \"\ acc_norm\": 0.5894039735099338,\n \"acc_norm_stderr\": 0.04016689594849928\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9357798165137615,\n \"acc_stderr\": 0.010510494713201405,\n \"\ acc_norm\": 0.9357798165137615,\n \"acc_norm_stderr\": 0.010510494713201405\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.7129629629629629,\n \"acc_stderr\": 0.03085199299325701,\n \"\ acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.03085199299325701\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \ \ \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8161434977578476,\n\ \ \"acc_stderr\": 0.025998379092356513,\n \"acc_norm\": 0.8161434977578476,\n\ \ \"acc_norm_stderr\": 0.025998379092356513\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515375,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515375\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540616,\n \"\ acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540616\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8703703703703703,\n\ \ \"acc_stderr\": 0.03247224389917947,\n \"acc_norm\": 0.8703703703703703,\n\ \ \"acc_norm_stderr\": 0.03247224389917947\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n\ \ \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.7053571428571429,\n\ \ \"acc_stderr\": 0.0432704093257873,\n \"acc_norm\": 0.7053571428571429,\n\ \ \"acc_norm_stderr\": 0.0432704093257873\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.03288180278808628,\n\ \ \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.03288180278808628\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.015537514263253874,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.015537514263253874\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896308,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896308\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9233716475095786,\n\ \ \"acc_stderr\": 0.00951217069932386,\n \"acc_norm\": 0.9233716475095786,\n\ \ \"acc_norm_stderr\": 0.00951217069932386\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8497109826589595,\n \"acc_stderr\": 0.01923931878390472,\n\ \ \"acc_norm\": 0.8497109826589595,\n \"acc_norm_stderr\": 0.01923931878390472\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.823463687150838,\n\ \ \"acc_stderr\": 0.012751770640520499,\n \"acc_norm\": 0.823463687150838,\n\ \ \"acc_norm_stderr\": 0.012751770640520499\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8562091503267973,\n \"acc_stderr\": 0.020091188936043725,\n\ \ \"acc_norm\": 0.8562091503267973,\n \"acc_norm_stderr\": 0.020091188936043725\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.842443729903537,\n\ \ \"acc_stderr\": 0.020692237273583984,\n \"acc_norm\": 0.842443729903537,\n\ \ \"acc_norm_stderr\": 0.020692237273583984\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8734567901234568,\n \"acc_stderr\": 0.018498600558790906,\n\ \ \"acc_norm\": 0.8734567901234568,\n \"acc_norm_stderr\": 0.018498600558790906\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6631205673758865,\n \"acc_stderr\": 0.02819553487396673,\n \ \ \"acc_norm\": 0.6631205673758865,\n \"acc_norm_stderr\": 0.02819553487396673\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6323337679269883,\n\ \ \"acc_stderr\": 0.012314845910071703,\n \"acc_norm\": 0.6323337679269883,\n\ \ \"acc_norm_stderr\": 0.012314845910071703\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.02151396405285963,\n\ \ \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02151396405285963\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8300653594771242,\n \"acc_stderr\": 0.015194153113184724,\n \ \ \"acc_norm\": 0.8300653594771242,\n \"acc_norm_stderr\": 0.015194153113184724\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7818181818181819,\n\ \ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.7818181818181819,\n\ \ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8163265306122449,\n \"acc_stderr\": 0.024789071332007646,\n\ \ \"acc_norm\": 0.8163265306122449,\n \"acc_norm_stderr\": 0.024789071332007646\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101716,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101716\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.95,\n \"acc_stderr\": 0.021904291355759026,\n \ \ \"acc_norm\": 0.95,\n \"acc_norm_stderr\": 0.021904291355759026\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276894,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276894\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.6499388004895961,\n\ \ \"mc1_stderr\": 0.016697949420151022,\n \"mc2\": 0.7508968077654237,\n\ \ \"mc2_stderr\": 0.014534916537858438\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8737174427782163,\n \"acc_stderr\": 0.009335559129908452\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7680060652009097,\n \ \ \"acc_stderr\": 0.011626873175092412\n }\n}\n```" repo_url: https://huggingface.co/MTSAIR/MultiVerse_70B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|arc:challenge\|25_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|arc:challenge\|25_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-28T11-20-18.515649.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|gsm8k\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|gsm8k\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hellaswag\|10_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hellaswag\|10_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-28T11-18-17.303683.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-28T11-20-18.515649.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-28T11-20-18.515649.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '/details_harness\|winogrande\|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '/details_harness\|winogrande\|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-28T11-20-18.515649.parquet' - config_name: results data_files: - split: 2024_03_28T11_18_17.303683 path: - results_2024-03-28T11-18-17.303683.parquet - split: 2024_03_28T11_20_18.515649 path: - results_2024-03-28T11-20-18.515649.parquet - split: latest path: - results_2024-03-28T11-20-18.515649.parquet --- # Dataset Card for Evaluation run of MTSAIR/MultiVerse_70B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [MTSAIR/MultiVerse_70B](https://huggingface.co/MTSAIR/MultiVerse_70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_MTSAIR__MultiVerse_70B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-28T11:20:18.515649](https://huggingface.co/datasets/open-llm-leaderboard/details_MTSAIR__MultiVerse_70B/blob/main/results_2024-03-28T11-20-18.515649.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7830598805961457, "acc_stderr": 0.027571176888417693, "acc_norm": 0.7846693275609173, "acc_norm_stderr": 0.028121251178584224, "mc1": 0.6499388004895961, "mc1_stderr": 0.016697949420151022, "mc2": 0.7508968077654237, "mc2_stderr": 0.014534916537858438 }, "harness\|arc:challenge\|25": { "acc": 0.7636518771331058, "acc_stderr": 0.012414960524301822, "acc_norm": 0.7858361774744027, "acc_norm_stderr": 0.01198838320596649 }, "harness\|hellaswag\|10": { "acc": 0.7490539733120892, "acc_stderr": 0.0043267144532667355, "acc_norm": 0.8974307906791475, "acc_norm_stderr": 0.0030277534195929483 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8881578947368421, "acc_stderr": 0.02564834125169361, "acc_norm": 0.8881578947368421, "acc_norm_stderr": 0.02564834125169361 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8528301886792453, "acc_stderr": 0.02180412613479737, "acc_norm": 0.8528301886792453, "acc_norm_stderr": 0.02180412613479737 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9236111111111112, "acc_stderr": 0.022212203938345918, "acc_norm": 0.9236111111111112, "acc_norm_stderr": 0.022212203938345918 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7630057803468208, "acc_stderr": 0.032424147574830975, "acc_norm": 0.7630057803468208, "acc_norm_stderr": 0.032424147574830975 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5294117647058824, "acc_stderr": 0.049665709039785295, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.049665709039785295 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.8468085106382979, "acc_stderr": 0.023545179061675203, "acc_norm": 0.8468085106382979, "acc_norm_stderr": 0.023545179061675203 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583706, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583706 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.8, "acc_stderr": 0.0333333333333333, "acc_norm": 0.8, "acc_norm_stderr": 0.0333333333333333 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6957671957671958, "acc_stderr": 0.023695415009463087, "acc_norm": 0.6957671957671958, "acc_norm_stderr": 0.023695415009463087 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5476190476190477, "acc_stderr": 0.044518079590553275, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.044518079590553275 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8870967741935484, "acc_stderr": 0.018003603325863614, "acc_norm": 0.8870967741935484, "acc_norm_stderr": 0.018003603325863614 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.03178529710642751, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.03178529710642751 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706467, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706467 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9292929292929293, "acc_stderr": 0.01826310542019948, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.01826310542019948 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9844559585492227, "acc_stderr": 0.008927492715084334, "acc_norm": 0.9844559585492227, "acc_norm_stderr": 0.008927492715084334 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8025641025641026, "acc_stderr": 0.02018264696867485, "acc_norm": 0.8025641025641026, "acc_norm_stderr": 0.02018264696867485 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.5037037037037037, "acc_stderr": 0.03048470166508437, "acc_norm": 0.5037037037037037, "acc_norm_stderr": 0.03048470166508437 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8529411764705882, "acc_stderr": 0.023005459446673936, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.023005459446673936 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5894039735099338, "acc_stderr": 0.04016689594849928, "acc_norm": 0.5894039735099338, "acc_norm_stderr": 0.04016689594849928 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9357798165137615, "acc_stderr": 0.010510494713201405, "acc_norm": 0.9357798165137615, "acc_norm_stderr": 0.010510494713201405 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.7129629629629629, "acc_stderr": 0.03085199299325701, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.03085199299325701 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658928, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658928 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9113924050632911, "acc_stderr": 0.018498315206865384, "acc_norm": 0.9113924050632911, "acc_norm_stderr": 0.018498315206865384 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8161434977578476, "acc_stderr": 0.025998379092356513, "acc_norm": 0.8161434977578476, "acc_norm_stderr": 0.025998379092356513 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8549618320610687, "acc_stderr": 0.030884661089515375, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.030884661089515375 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8925619834710744, "acc_stderr": 0.028268812192540616, "acc_norm": 0.8925619834710744, "acc_norm_stderr": 0.028268812192540616 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8703703703703703, "acc_stderr": 0.03247224389917947, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.03247224389917947 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.7053571428571429, "acc_stderr": 0.0432704093257873, "acc_norm": 0.7053571428571429, "acc_norm_stderr": 0.0432704093257873 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8737864077669902, "acc_stderr": 0.03288180278808628, "acc_norm": 0.8737864077669902, "acc_norm_stderr": 0.03288180278808628 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253874, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253874 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896308, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896308 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9233716475095786, "acc_stderr": 0.00951217069932386, "acc_norm": 0.9233716475095786, "acc_norm_stderr": 0.00951217069932386 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8497109826589595, "acc_stderr": 0.01923931878390472, "acc_norm": 0.8497109826589595, "acc_norm_stderr": 0.01923931878390472 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.823463687150838, "acc_stderr": 0.012751770640520499, "acc_norm": 0.823463687150838, "acc_norm_stderr": 0.012751770640520499 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8562091503267973, "acc_stderr": 0.020091188936043725, "acc_norm": 0.8562091503267973, "acc_norm_stderr": 0.020091188936043725 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.842443729903537, "acc_stderr": 0.020692237273583984, "acc_norm": 0.842443729903537, "acc_norm_stderr": 0.020692237273583984 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8734567901234568, "acc_stderr": 0.018498600558790906, "acc_norm": 0.8734567901234568, "acc_norm_stderr": 0.018498600558790906 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6631205673758865, "acc_stderr": 0.02819553487396673, "acc_norm": 0.6631205673758865, "acc_norm_stderr": 0.02819553487396673 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6323337679269883, "acc_stderr": 0.012314845910071703, "acc_norm": 0.6323337679269883, "acc_norm_stderr": 0.012314845910071703 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8529411764705882, "acc_stderr": 0.02151396405285963, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.02151396405285963 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8300653594771242, "acc_stderr": 0.015194153113184724, "acc_norm": 0.8300653594771242, "acc_norm_stderr": 0.015194153113184724 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03955932861795833, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03955932861795833 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8163265306122449, "acc_stderr": 0.024789071332007646, "acc_norm": 0.8163265306122449, "acc_norm_stderr": 0.024789071332007646 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101716, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101716 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.95, "acc_stderr": 0.021904291355759026, "acc_norm": 0.95, "acc_norm_stderr": 0.021904291355759026 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8713450292397661, "acc_stderr": 0.025679342723276894, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276894 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.6499388004895961, "mc1_stderr": 0.016697949420151022, "mc2": 0.7508968077654237, "mc2_stderr": 0.014534916537858438 }, "harness\|winogrande\|5": { "acc": 0.8737174427782163, "acc_stderr": 0.009335559129908452 }, "harness\|gsm8k\|5": { "acc": 0.7680060652009097, "acc_stderr": 0.011626873175092412 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
veerav96/sokoto_real	--- license: apache-2.0 ---
GabrielTOP/Matheus	--- license: openrail ---
wangxinze/verilog_prepare	--- license: apache-2.0 ---
when2rl/OpenHermesPreferences_reformatted	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: other_info struct: - name: candidate_policies sequence: string - name: candidates_completions sequence: string - name: category dtype: string - name: chosen_policy dtype: string - name: chosen_rank dtype: int64 - name: rank_str dtype: string - name: ranks sequence: int64 - name: rejected_policy dtype: string - name: rejected_rank dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 9078591768 num_examples: 989490 download_size: 4430641969 dataset_size: 9078591768 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for OpenHermesPreferences_reformatted <!-- Provide a quick summary of the dataset. --> This is a reformatted version of argilla's OpenHermesPreference: 1. reformatted the daatset to be consistent with ultrafeedback_binarized. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
juancopi81/orca-math-word-problems-100020_110022	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 10803481 num_examples: 10002 download_size: 3904854 dataset_size: 10803481 configs: - config_name: default data_files: - split: train path: data/train-* ---
dipanjanS/imdb_sentiment_finetune_dataset	--- dataset_info: features: - name: review dtype: string - name: sentiment dtype: int64 splits: - name: train num_bytes: 2588807 num_examples: 2000 - name: validation num_bytes: 2667965 num_examples: 2000 - name: test num_bytes: 21177655 num_examples: 16000 download_size: 17194624 dataset_size: 26434427 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
adityarra07/ATC_test_2_noise	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 454815537 num_examples: 871 download_size: 455105235 dataset_size: 454815537 --- # Dataset Card for "ATC_test_2_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/tsurumaki_kokoro_bangdream	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tsurumaki_kokoro (BanG Dream!) This is the dataset of tsurumaki_kokoro (BanG Dream!), containing 500 images and their tags. The core tags of this character are `blonde_hair, bangs, long_hair, yellow_eyes, sidelocks, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 748.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 413.65 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1275 \| 906.55 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 652.83 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1275 \| 1.30 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/tsurumaki_kokoro_bangdream', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| looking_at_viewer, short_sleeves, wrist_cuffs, 1girl, earrings, hair_bow, midriff, smile, solo, blush, thighhighs, crop_top, navel, necklace, blue_skirt, multicolored_skirt, open_mouth, belt, boots, choker, frilled_skirt, layered_skirt, multicolored_shirt, polka_dot, see-through_sleeves, shoes, socks, white_background, white_footwear \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, earrings, solo, black_headwear, hat_bow, looking_at_viewer, pom_pom_(clothes), red_bowtie, top_hat, cleavage, medium_breasts, polka_dot_bow, open_mouth, blush, frills, smiley_face, black_shorts, :d, confetti, holding, short_shorts, teeth, white_background, back_bow \| \| 2 \| 10 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| frills, looking_at_viewer, 1girl, blush, solo, confetti, earrings, white_gloves, hair_bow, open_mouth, star_(symbol), :d, red_bowtie, upper_body, ribbon, string_of_flags, twintails, balloon, blue_bow, corset, short_sleeves, striped_bowtie, top_hat \| \| 3 \| 22 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, solo, looking_at_viewer, blush, bowtie, open_mouth, earrings, wrist_cuffs, frills, choker, fur-trimmed_capelet, striped_bow, mini_crown, navel, ribbon, blue_bow, red_capelet, :d, sparkle, midriff, white_background, one_eye_closed, short_sleeves, upper_body \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, epaulettes, looking_at_viewer, open_mouth, shako_cap, sleeveless, solo, upper_body, :d, band_uniform, blush, sash, wrist_cuffs, upper_teeth_only \| \| 5 \| 13 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, :d, looking_at_viewer, open_mouth, solo, white_skirt, band_uniform, blush, epaulettes, wrist_cuffs, shako_cap, sleeveless_shirt, teeth, thighhighs, medium_breasts, sash, frilled_skirt, red_footwear, armpits, standing, thigh_boots, white_background, cowboy_shot, simple_background, star_(symbol) \| \| 6 \| 32 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, solo, looking_at_viewer, short_sleeves, blush, red_shirt, striped_shirt, smile, collarbone, open_mouth, simple_background, white_background, overall_shorts, upper_body, medium_breasts, teeth \| \| 7 \| 6 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, :d, blush, long_sleeves, looking_at_viewer, open_mouth, sheep_horns, sleep_mask, solo, star_(symbol), upper_teeth_only, bow, hair_flower, mask_on_head, headset, sparkle, apron, arms_up, center_frills, frilled_sleeves, pink_rose, ribbon, striped, upper_body \| \| 8 \| 24 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, solo, white_dress, looking_at_viewer, sleeveless_dress, sun_hat, sundress, blush, day, outdoors, straw_hat, sunflower, open_mouth, collarbone, :d, frills, hat_flower, blue_sky, cloud, medium_breasts, cleavage, upper_body, upper_teeth_only \| \| 9 \| 10 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, brown_dress, hanasakigawa_school_uniform, long_sleeves, looking_at_viewer, neck_ribbon, red_ribbon, sailor_dress, solo, double-breasted, white_sailor_collar, blush, open_mouth, white_background, simple_background, pleated_dress, one_eye_closed, teeth, :d, ;d, bow \| \| 10 \| 10 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| 1girl, looking_at_viewer, pleated_skirt, serafuku, short_sleeves, solo, white_skirt, blush, hanasakigawa_school_uniform, white_sailor_collar, white_background, blue_neckerchief, blue_shirt, open_mouth, simple_background, miniskirt, collarbone, :d \| \| 11 \| 5 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1girl, earrings, smile, solo, star_(symbol), looking_at_viewer, beret, blush, bowtie, frilled_shirt_collar, hat_bow, short_sleeves, upper_body, alternate_hairstyle, argyle, center_frills, christmas, cleavage, constellation_print, dress, frilled_sleeves, hairclip, headset, long_sleeves, red_headwear, ribbon, striped_bow, twintails \| \| 12 \| 5 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| 1girl, blush, hair_flower, hair_ribbon, solo, sunflower, twintails, :d, bracelet, looking_at_viewer, open_mouth, alternate_hairstyle, blue_dress, blue_ribbon, frills, holding, necklace, beachball, bow, day, hairband, polka_dot, sky, sparkle, upper_body, white_background \| \| 13 \| 8 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-sample4.png) \| 1girl, looking_at_viewer, skirt, solo, twintails, white_gloves, handcuffs, midriff, navel, police_hat, star_(symbol), blue_headwear, blush, short_sleeves, crop_top, hair_ribbon, open_mouth, peaked_cap, :d, cropped_jacket, detached_collar, frills, orange_necktie, short_necktie, belt, cleavage, knee_boots, open_clothes, shirt, striped, white_background, white_jacket, white_thighhighs \| \| 14 \| 14 \| ![](samples/14/clu14-sample0.png) \| ![](samples/14/clu14-sample1.png) \| ![](samples/14/clu14-sample2.png) \| ![](samples/14/clu14-sample3.png) \| ![](samples/14/clu14-sample4.png) \| 1girl, kimono, obi, blush, looking_at_viewer, solo, wide_sleeves, floral_print, hair_flower, long_sleeves, ponytail, open_mouth, hair_bow, :d, holding, detached_sleeves, frills, red_flower, red_bow, sky, teeth, upper_body \| \| 15 \| 16 \| ![](samples/15/clu15-sample0.png) \| ![](samples/15/clu15-sample1.png) \| ![](samples/15/clu15-sample2.png) \| ![](samples/15/clu15-sample3.png) \| ![](samples/15/clu15-sample4.png) \| blush, looking_at_viewer, 1girl, solo, nipples, open_mouth, completely_nude, pussy, collarbone, navel, simple_background, barefoot, large_breasts, medium_breasts, stomach, white_background, :d, fingernails, full_body, anus, armpits, blunt_bangs, cleft_of_venus, heart, toes, uncensored \| \| 16 \| 5 \| ![](samples/16/clu16-sample0.png) \| ![](samples/16/clu16-sample1.png) \| ![](samples/16/clu16-sample2.png) \| ![](samples/16/clu16-sample3.png) \| ![](samples/16/clu16-sample4.png) \| 1boy, 1girl, blush, hetero, medium_breasts, open_mouth, solo_focus, sweat, completely_nude, saliva, sex_from_behind, heart, heavy_breathing, nipples, standing_sex, tears, upper_teeth_only, vaginal, :d, arm_support, ass, bent_over, cum, from_side, indoors, large_breasts, looking_at_viewer, looking_to_the_side, mosaic_censoring, motion_lines, penis, torso_grab, trembling \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| looking_at_viewer \| short_sleeves \| wrist_cuffs \| 1girl \| earrings \| hair_bow \| midriff \| smile \| solo \| blush \| thighhighs \| crop_top \| navel \| necklace \| blue_skirt \| multicolored_skirt \| open_mouth \| belt \| boots \| choker \| frilled_skirt \| layered_skirt \| multicolored_shirt \| polka_dot \| see-through_sleeves \| shoes \| socks \| white_background \| white_footwear \| black_headwear \| hat_bow \| pom_pom_(clothes) \| red_bowtie \| top_hat \| cleavage \| medium_breasts \| polka_dot_bow \| frills \| smiley_face \| black_shorts \| :d \| confetti \| holding \| short_shorts \| teeth \| back_bow \| white_gloves \| star_(symbol) \| upper_body \| ribbon \| string_of_flags \| twintails \| balloon \| blue_bow \| corset \| striped_bowtie \| bowtie \| fur-trimmed_capelet \| striped_bow \| mini_crown \| red_capelet \| sparkle \| one_eye_closed \| epaulettes \| shako_cap \| sleeveless \| band_uniform \| sash \| upper_teeth_only \| white_skirt \| sleeveless_shirt \| red_footwear \| armpits \| standing \| thigh_boots \| cowboy_shot \| simple_background \| red_shirt \| striped_shirt \| collarbone \| overall_shorts \| long_sleeves \| sheep_horns \| sleep_mask \| bow \| hair_flower \| mask_on_head \| headset \| apron \| arms_up \| center_frills \| frilled_sleeves \| pink_rose \| striped \| white_dress \| sleeveless_dress \| sun_hat \| sundress \| day \| outdoors \| straw_hat \| sunflower \| hat_flower \| blue_sky \| cloud \| brown_dress \| hanasakigawa_school_uniform \| neck_ribbon \| red_ribbon \| sailor_dress \| double-breasted \| white_sailor_collar \| pleated_dress \| ;d \| pleated_skirt \| serafuku \| blue_neckerchief \| blue_shirt \| miniskirt \| beret \| frilled_shirt_collar \| alternate_hairstyle \| argyle \| christmas \| constellation_print \| dress \| hairclip \| red_headwear \| hair_ribbon \| bracelet \| blue_dress \| blue_ribbon \| beachball \| hairband \| sky \| skirt \| handcuffs \| police_hat \| blue_headwear \| peaked_cap \| cropped_jacket \| detached_collar \| orange_necktie \| short_necktie \| knee_boots \| open_clothes \| shirt \| white_jacket \| white_thighhighs \| kimono \| obi \| wide_sleeves \| floral_print \| ponytail \| detached_sleeves \| red_flower \| red_bow \| nipples \| completely_nude \| pussy \| barefoot \| large_breasts \| stomach \| fingernails \| full_body \| anus \| blunt_bangs \| cleft_of_venus \| heart \| toes \| uncensored \| 1boy \| hetero \| solo_focus \| sweat \| saliva \| sex_from_behind \| heavy_breathing \| standing_sex \| tears \| vaginal \| arm_support \| ass \| bent_over \| cum \| from_side \| indoors \| looking_to_the_side \| mosaic_censoring \| motion_lines \| penis \| torso_grab \| trembling \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------------------\|:----------------\|:--------------\|:--------\|:-----------\|:-----------\|:----------\|:--------\|:-------\|:--------\|:-------------\|:-----------\|:--------\|:-----------\|:-------------\|:---------------------\|:-------------\|:-------\|:--------\|:---------\|:----------------\|:----------------\|:---------------------\|:------------\|:----------------------\|:--------\|:--------\|:-------------------\|:-----------------\|:-----------------\|:----------\|:--------------------\|:-------------\|:----------\|:-----------\|:-----------------\|:----------------\|:---------\|:--------------\|:---------------\|:-----\|:-----------\|:----------\|:---------------\|:--------\|:-----------\|:---------------\|:----------------\|:-------------\|:---------\|:------------------\|:------------\|:----------\|:-----------\|:---------\|:-----------------\|:---------\|:----------------------\|:--------------\|:-------------\|:--------------\|:----------\|:-----------------\|:-------------\|:------------\|:-------------\|:---------------\|:-------\|:-------------------\|:--------------\|:-------------------\|:---------------\|:----------\|:-----------\|:--------------\|:--------------\|:--------------------\|:------------\|:----------------\|:-------------\|:-----------------\|:---------------\|:--------------\|:-------------\|:------\|:--------------\|:---------------\|:----------\|:--------\|:----------\|:----------------\|:------------------\|:------------\|:----------\|:--------------\|:-------------------\|:----------\|:-----------\|:------\|:-----------\|:------------\|:------------\|:-------------\|:-----------\|:--------\|:--------------\|:------------------------------\|:--------------\|:-------------\|:---------------\|:------------------\|:----------------------\|:----------------\|:-----\|:----------------\|:-----------\|:-------------------\|:-------------\|:------------\|:--------\|:-----------------------\|:----------------------\|:---------\|:------------\|:----------------------\|:--------\|:-----------\|:---------------\|:--------------\|:-----------\|:-------------\|:--------------\|:------------\|:-----------\|:------\|:--------\|:------------\|:-------------\|:----------------\|:-------------\|:-----------------\|:------------------\|:-----------------\|:----------------\|:-------------\|:---------------\|:--------\|:---------------\|:-------------------\|:---------\|:------\|:---------------\|:---------------\|:-----------\|:-------------------\|:-------------\|:----------\|:----------\|:------------------\|:--------\|:-----------\|:----------------\|:----------\|:--------------\|:------------\|:-------\|:--------------\|:-----------------\|:--------\|:-------\|:-------------\|:-------\|:---------\|:-------------\|:--------\|:---------\|:------------------\|:------------------\|:---------------\|:--------\|:----------\|:--------------\|:------\|:------------\|:------\|:------------\|:----------\|:----------------------\|:-------------------\|:---------------\|:--------\|:-------------\|:------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| \| X \| X \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 10 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| X \| \| \| X \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 22 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| \| X \| \| X \| X \| \| \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| X \| X \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| X \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 13 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| X \| X \| \| \| \| \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 32 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 6 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| X \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| 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\| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 10 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| X \| X \| \| X \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 5 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| X \| X \| \| X \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| X \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 12 \| 5 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| 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X \| X \|
shidowake/Doctor-Shotgun_capybara-sharegpt_subset_split_5	--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 9064100.571348244 num_examples: 2001 download_size: 4725927 dataset_size: 9064100.571348244 configs: - config_name: default data_files: - split: train path: data/train-* ---
rhaymison/questions_answers_geo_nord	--- language: - pt license: apache-2.0 size_categories: - 10K<n<100K task_categories: - text-generation pretty_name: geografia Nordestina dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 11397122.0 num_examples: 44004 download_size: 5647644 dataset_size: 11397122.0 configs: - config_name: default data_files: - split: train path: data/train-* tags: - portugues - nordeste - mixtral ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d718bf95	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1335 dataset_size: 188 --- # Dataset Card for "d718bf95" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

freshpearYoon/vr_train_free_10

--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: filename dtype: string - name: NumOfUtterance dtype: int64 - name: text dtype: string - name: samplingrate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: speaker_id dtype: string - name: directory dtype: string splits: - name: train num_bytes: 7143134749 num_examples: 10000 download_size: 1080163057 dataset_size: 7143134749 configs: - config_name: default data_files: - split: train path: data/train-* ---

liuyanchen1015/MULTI_VALUE_stsb_got_gotten

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 450 num_examples: 2 - name: test num_bytes: 72 num_examples: 1 - name: train num_bytes: 274 num_examples: 1 download_size: 9256 dataset_size: 796 --- # Dataset Card for "MULTI_VALUE_stsb_got_gotten" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

benchan79/github-issues

--- dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: user struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: labels list: - name: id dtype: int64 - name: node_id dtype: string - name: url dtype: string - name: name dtype: string - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: state dtype: string - name: locked dtype: bool - name: assignee struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: assignees list: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: milestone struct: - name: url dtype: string - name: html_url dtype: string - name: labels_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - name: description dtype: string - name: creator struct: - name: login dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: avatar_url dtype: string - name: gravatar_id dtype: string - name: url dtype: string - name: html_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: organizations_url dtype: string - name: repos_url dtype: string - name: events_url dtype: string - name: received_events_url dtype: string - name: type dtype: string - name: site_admin dtype: bool - name: open_issues dtype: int64 - name: closed_issues dtype: int64 - name: state dtype: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: due_on dtype: 'null' - name: closed_at dtype: 'null' - name: comments sequence: string - name: created_at dtype: timestamp[s] - name: updated_at dtype: timestamp[s] - name: closed_at dtype: timestamp[s] - name: author_association dtype: string - name: active_lock_reason dtype: 'null' - name: body dtype: string - name: reactions struct: - name: url dtype: string - name: total_count dtype: int64 - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: laugh dtype: int64 - name: hooray dtype: int64 - name: confused dtype: int64 - name: heart dtype: int64 - name: rocket dtype: int64 - name: eyes dtype: int64 - name: timeline_url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: draft dtype: bool - name: pull_request struct: - name: url dtype: string - name: html_url dtype: string - name: diff_url dtype: string - name: patch_url dtype: string - name: merged_at dtype: timestamp[s] - name: is_pull_request dtype: bool splits: - name: train num_bytes: 15437002 num_examples: 3100 download_size: 4434085 dataset_size: 15437002 annotations_creators: - no-annotation language: - en language_creators: - found license: - unknown multilinguality: - monolingual pretty_name: Hugging Face GitHub Issues size_categories: - unknown source_datasets: - original tags: [] task_categories: - text-classification - text-retrieval task_ids: - multi-class-classification - multi-label-classification - document-retrieval --- # Dataset Card for "Hugging Face GitHub Issues ## Dataset Description - **Point of Contact:** [Ben Chan](benchan79@gmail.com) ### Dataset Summary GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond. ### Supported Tasks and Leaderboards ### Languages English ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Citation Information ### Contributions

DeepPavlov/verbalist_prompts

--- configs: - config_name: default data_files: - split: dim_oasst_en path: data/dim_oasst_en-* - split: dim_oasst_ru path: data/dim_oasst_ru-* - split: dim_lima path: data/dim_lima-* - split: dim_logic_tasks_ru path: data/dim_logic_tasks_ru-* - split: dim_wikihow_en path: data/dim_wikihow_en-* - split: dim_wikihow_ru path: data/dim_wikihow_ru-* - split: dim_essayforum_writing_prompts_6k path: data/dim_essayforum_writing_prompts_6k-* - split: dim_sharegpt_short_ru path: data/dim_sharegpt_short_ru-* - split: dim_openreview_prompts_65 path: data/dim_openreview_prompts_65-* - split: dim_roleplay_instruct_v2_final path: data/dim_roleplay_instruct_v2_final-* - split: dim_kinomania_scripts path: data/dim_kinomania_scripts-* - split: dim_bugurt_thread_prompts path: data/dim_bugurt_thread_prompts-* - split: dim_russian_lyrics_prompts path: data/dim_russian_lyrics_prompts-* - split: dim_ru_instruct_gpt4 path: data/dim_ru_instruct_gpt4-* - split: dim_gpt_roleplay_realm path: data/dim_gpt_roleplay_realm-* - split: dim_ultrachat_ru path: data/dim_ultrachat_ru-* - split: dim_scitldr path: data/dim_scitldr-* - split: dim_linux_man_pages_tldr_summarized path: data/dim_linux_man_pages_tldr_summarized-* - split: dim_dolphin_ru_3k path: data/dim_dolphin_ru_3k-* - split: dim_runne_prompts path: data/dim_runne_prompts-* - split: dim_lurk_prompts path: data/dim_lurk_prompts-* - split: dim_panorama_prompts_10k path: data/dim_panorama_prompts_10k-* - split: dim_resh_edu_short_prompts path: data/dim_resh_edu_short_prompts-* - split: dim_databricks_dolly_15k_ru path: data/dim_databricks_dolly_15k_ru-* - split: dim_databricks_dolly_15k_en path: data/dim_databricks_dolly_15k_en-* - split: dim_grammarly_coedit path: data/dim_grammarly_coedit-* - split: dim_kinopoisk_prompts path: data/dim_kinopoisk_prompts-* - split: dim_medical_qa_ru_prompts path: data/dim_medical_qa_ru_prompts-* - split: dim_joke_explaination_prompts path: data/dim_joke_explaination_prompts-* - split: dim_oa_stackexchange_200k path: data/dim_oa_stackexchange_200k-* - split: dim_scale_helpful_no_math path: data/dim_scale_helpful_no_math-* - split: dim_law_stackexchange_prompts path: data/dim_law_stackexchange_prompts-* - split: dim_ficbook_prompts_best_10k path: data/dim_ficbook_prompts_best_10k-* - split: dim_azbyka_logic_ru path: data/dim_azbyka_logic_ru-* - split: dim_povarenok path: data/dim_povarenok-* - split: dim_AO3_fandom_chatbot_1to1 path: data/dim_AO3_fandom_chatbot_1to1-* - split: dim_habr_prompts_5k path: data/dim_habr_prompts_5k-* - split: dim_what_where_when_50k path: data/dim_what_where_when_50k-* - split: dim_competition_math path: data/dim_competition_math-* - split: dim_sharegpt_short_en_30k path: data/dim_sharegpt_short_en_30k-* - split: dim_ru_turbo_alpaca_evol_instruct path: data/dim_ru_turbo_alpaca_evol_instruct-* - split: dim_ru_turbo_saiga path: data/dim_ru_turbo_saiga-* - split: dim_bugurt_completion_prompts path: data/dim_bugurt_completion_prompts-* - split: dim_tldr_17_50k path: data/dim_tldr_17_50k-* - split: dim_grade_school_math_instructions path: data/dim_grade_school_math_instructions-* - split: dim_tldr_news path: data/dim_tldr_news-* - split: dim_grade_school_math_instructions_ru path: data/dim_grade_school_math_instructions_ru-* - split: dim_dialogsum path: data/dim_dialogsum-* - split: dim_HC3_ru path: data/dim_HC3_ru-* - split: dim_horoscopes_ru_10k path: data/dim_horoscopes_ru_10k-* - split: dim_yandex_q_200k path: data/dim_yandex_q_200k-* - split: dim_leetcodesolutions_en_2k path: data/dim_leetcodesolutions_en_2k-* - split: dim_forum_uristov_rf_prompts path: data/dim_forum_uristov_rf_prompts-* - split: dim_dialogsum_ru path: data/dim_dialogsum_ru-* - split: dim_huggingartists_prompts path: data/dim_huggingartists_prompts-* dataset_info: features: - name: conversation_text sequence: string splits: - name: dim_oasst_en num_bytes: 4335500 num_examples: 2289 - name: dim_oasst_ru num_bytes: 6206378 num_examples: 2220 - name: dim_lima num_bytes: 2892267 num_examples: 1030 - name: dim_logic_tasks_ru num_bytes: 76915 num_examples: 86 - name: dim_wikihow_en num_bytes: 16008199 num_examples: 1995 - name: dim_wikihow_ru num_bytes: 24451573 num_examples: 2058 - name: dim_essayforum_writing_prompts_6k num_bytes: 22326330 num_examples: 6361 - name: dim_sharegpt_short_ru num_bytes: 808319 num_examples: 253 - name: dim_openreview_prompts_65 num_bytes: 6739952 num_examples: 150 - name: dim_roleplay_instruct_v2_final num_bytes: 4389286 num_examples: 7188 - name: dim_kinomania_scripts num_bytes: 238731 num_examples: 27 - name: dim_bugurt_thread_prompts num_bytes: 302191 num_examples: 223 - name: dim_russian_lyrics_prompts num_bytes: 18676 num_examples: 43 - name: dim_ru_instruct_gpt4 num_bytes: 18351658 num_examples: 14222 - name: dim_gpt_roleplay_realm num_bytes: 20163429 num_examples: 8700 - name: dim_ultrachat_ru num_bytes: 4495105 num_examples: 500 - name: dim_scitldr num_bytes: 4049209 num_examples: 3229 - name: dim_linux_man_pages_tldr_summarized num_bytes: 3006631 num_examples: 481 - name: dim_dolphin_ru_3k num_bytes: 7976776 num_examples: 3000 - name: dim_runne_prompts num_bytes: 2686148 num_examples: 537 - name: dim_lurk_prompts num_bytes: 92012533 num_examples: 5671 - name: dim_panorama_prompts_10k num_bytes: 28964132 num_examples: 11024 - name: dim_resh_edu_short_prompts num_bytes: 12380000 num_examples: 2106 - name: dim_databricks_dolly_15k_ru num_bytes: 21900617 num_examples: 14914 - name: dim_databricks_dolly_15k_en num_bytes: 11973713 num_examples: 15011 - name: dim_grammarly_coedit num_bytes: 18500223 num_examples: 82466 - name: dim_kinopoisk_prompts num_bytes: 136323982 num_examples: 36591 - name: dim_medical_qa_ru_prompts num_bytes: 75634717 num_examples: 80101 - name: dim_joke_explaination_prompts num_bytes: 196224 num_examples: 364 - name: dim_oa_stackexchange_200k num_bytes: 192535277 num_examples: 200000 - name: dim_scale_helpful_no_math num_bytes: 85610911 num_examples: 17095 - name: dim_law_stackexchange_prompts num_bytes: 64544963 num_examples: 24343 - name: dim_ficbook_prompts_best_10k num_bytes: 75867114 num_examples: 10000 - name: dim_azbyka_logic_ru num_bytes: 173101 num_examples: 480 - name: dim_povarenok num_bytes: 93518909 num_examples: 46500 - name: dim_AO3_fandom_chatbot_1to1 num_bytes: 1162058 num_examples: 614 - name: dim_habr_prompts_5k num_bytes: 40224997 num_examples: 5000 - name: dim_what_where_when_50k num_bytes: 38385243 num_examples: 50000 - name: dim_competition_math num_bytes: 5808689 num_examples: 7500 - name: dim_sharegpt_short_en_30k num_bytes: 86599862 num_examples: 29597 - name: dim_ru_turbo_alpaca_evol_instruct num_bytes: 105340901 num_examples: 47793 - name: dim_ru_turbo_saiga num_bytes: 79875722 num_examples: 37699 - name: dim_bugurt_completion_prompts num_bytes: 5471066 num_examples: 5000 - name: dim_tldr_17_50k num_bytes: 81185070 num_examples: 50000 - name: dim_grade_school_math_instructions num_bytes: 4655452 num_examples: 8792 - name: dim_tldr_news num_bytes: 4014718 num_examples: 7138 - name: dim_grade_school_math_instructions_ru num_bytes: 6845510 num_examples: 7473 - name: dim_dialogsum num_bytes: 11176807 num_examples: 12460 - name: dim_HC3_ru num_bytes: 43395731 num_examples: 24322 - name: dim_horoscopes_ru_10k num_bytes: 9489348 num_examples: 10000 - name: dim_yandex_q_200k num_bytes: 292443135 num_examples: 200000 - name: dim_leetcodesolutions_en_2k num_bytes: 4708692 num_examples: 2048 - name: dim_forum_uristov_rf_prompts num_bytes: 2757263 num_examples: 1849 - name: dim_dialogsum_ru num_bytes: 18657989 num_examples: 12460 - name: dim_huggingartists_prompts num_bytes: 121909835 num_examples: 64006 download_size: 0 dataset_size: 2023767777 language: - ru - en --- # Verbalist (буквоед) - русскоязычный ассистент. Проект во многом вдохновленный [Saiga](https://huggingface.co/IlyaGusev/saiga2_7b_lora). Мною были собраны все самые качественные датасеты с [huggingface.datasets](https://huggingface.co/datasets), а также собраны дополнительно с тех сайтов, которые я посчитал весьма полезными для создания аналога ChatGPT. Лицензии у всех датасетов отличаются, какие-то по типу [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) были созданы специально для обучения подобных моделей, какие-то являются прямой выгрузкой диалогов с ChatGPT ([RyokoAI/ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K)). Вклад данного репозитория состоит в систематизации и стандартизации уже имеющихся датасетов, добавлении новых. А также тренировке моделей на этих данных. - [google sheets таблица с датасетами и описанием](https://docs.google.com/spreadsheets/d/10xcsINF_c_zUZchT8p-8xIuHDgcuwg63jjl2ortBP9I/edit?usp=sharing) ### Датасеты - **[Объединенный датасет где все данные уже подготовлены для тренировки диалоговой модели](https://huggingface.co/datasets/dim/verbalist_prompts)** |name |link |description |original_name |original_source |preparation_script |language|amount_examples|mean_llama_tokens|std |min_llama_tokens|25% |50% |75% |max_llama_tokens| |-------------------------------------|---------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|--------|---------------|-----------------|-----------|----------------|-------|-------|-------|----------------| |dim/oasst_en |https://huggingface.co/datasets/dim/oasst_en |OpenAssistant Conversations Dataset на английском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: https://docs.google.com/spreadsheets/d/117t5-Tr-dxdODpyFBkBg5R8GklYBlsvBfeDyjqwz2pA/edit?usp=sharing|2023-04-12_oasst_ready.messages.jsonl.gz |https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/2023-04-12_oasst_ready.messages.jsonl.gz|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oasst |en |2289 |468.6788991 |295.0864391|17 |264 |410 |618 |2332 | |dim/oasst_ru |https://huggingface.co/datasets/dim/oasst_ru |OpenAssistant Conversations Dataset на русском языке, который был вручную отфильтрован мной. В исходном датасете около 30% диалогов оказались не корректными. Иногда пользователь, играющий роль ассистента, использовал грубый тон в общении с пользователем, иногда люди просто отвечали "не знаю" на вопросы, и некоторые из вопросов были недостаточно научными или слишком краткими. Вы можете ознакомиться с этой разметкой по следующей ссылке: https://docs.google.com/spreadsheets/d/1uiOnqxiytuxrB6u6q2pMSdnMfqjT3arfg8DlT-OWlb0/edit?usp=sharing |2023-04-12_oasst_ready.messages.jsonl.gz |https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/2023-04-12_oasst_ready.messages.jsonl.gz|https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oasst |ru |2220 |589.6112613 |479.835392 |7 |278 |465 |763.5 |5028 | |dim/lima |https://huggingface.co/datasets/dim/lima |Данный датасет включает в себя 1000 высококачественных обучающих примеров на английском языке. Он собран из различных источников, включая Stack Exchange (STEM), Stack Exchange (Other), wikiHow, Pushshift r/WritingPrompts, Natural Instructions, а также уникальные инструкции, созданные авторами статей. Более подробную информацию о датасете можно найти в [соответствующей статье](https://arxiv.org/pdf/2305.11206.pdf). |GAIR/lima |https://huggingface.co/datasets/GAIR/lima |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/lima |en |1030 |712.9456311 |671.179319 |29 |312.75 |488.5 |825 |3920 | |dim/logic_tasks_ru |https://huggingface.co/datasets/dim/logic_tasks_ru |Данный набор задач по логике для детей взят с веб-сайта https://www.potehechas.ru/zadachi/zadachi.shtml. |Логические задачи - Логика и нестандартное мышление |https://www.potehechas.ru/zadachi/zadachi.shtml |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/logic_tasks_ru |ru |86 |193.0697674 |76.69048422|58 |133.75 |185 |243.5 |432 | |dim/wikihow_en |https://huggingface.co/datasets/dim/wikihow_en |Данный датасет содержит англоязычные статьи, извлеченные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |https://huggingface.co/datasets/0x22almostEvil/multilingual-wikihow-qa-16k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/wiki_how |en |1995 |2037.86416 |870.1910713|265 |1463 |1913 |2461.5 |8988 | |dim/wikihow_ru |https://huggingface.co/datasets/dim/wikihow_ru |Данный датасет включает в себя русскоязычные статьи, полученные с веб-сайта Wikihow. |0x22almostEvil/multilingual-wikihow-qa-16k |https://huggingface.co/datasets/0x22almostEvil/multilingual-wikihow-qa-16k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/wiki_how |ru |2058 |2498.119534 |1587.851549|139 |1236.25|2264 |3421.75|10217 | |dim/essayforum_writing_prompts_6k |https://huggingface.co/datasets/dim/essayforum_writing_prompts_6k |Данный датасет включает в себя запросы на помощь с написанием небольших эссе, размещенные на данном сайте. Ответы в датасете предоставлены исключительно главным администратором сайта. Его ответы были отобраны, поскольку чаще всего они являются наиболее качественными и вдумчивыми. |EssayForum |https://essayforum.com/writing/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/essayforum |en |6361 |783.1760729 |285.4314176|258 |629 |742 |879 |4966 | |dim/sharegpt_short_ru |https://huggingface.co/datasets/dim/sharegpt_short_ru |Очищенная версия русская версия sharegpt. Я попытался вырезать из текста все промпты, где модель извиняется что что-то не может сделать, что она не имеет доступа в интернет. Диалоги, которые противоречат морали модели я просто исключил. Постарался убрать упоминания о том что она модель AI, так как за ролеплейные характеристики отвечают другие датасеты. |RyokoAI/ShareGPT52K |https://huggingface.co/datasets/RyokoAI/ShareGPT52K |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/sharegpt |ru |253 |706.6521739 |494.7437584|13 |310 |628 |1078 |1861 | |dim/openreview_prompts_65 |https://huggingface.co/datasets/dim/openreview_prompts_65 |Датасет рецензий на реальные научные статьи с сайта openreview. Вышло на самом деле не так много, так как многие статьи не выложенны на arxiv или просто не имеют рецензий. Плюс я собрал только малую часть данного сайта, а не все что там было. |https://openreview.net/ |https://openreview.net/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/openreview |en |150 |13531.51333 |6966.623686|4893 |8279 |12648.5|15833.5|41494 | |dim/roleplay_instruct_v2_final |https://huggingface.co/datasets/dim/roleplay_instruct_v2_final |Датасет ролеплея от GPT-4 на различных персонажей на английском языке. |roleplay-instruct-v2-final |https://github.com/teknium1/GPTeacher |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/gpt_roleplay_realm |en |7188 |155.1413467 |97.71215667|14 |88 |125 |192 |1291 | |dim/kinomania_scripts |https://huggingface.co/datasets/dim/kinomania_scripts |Небольшой датасет, который содержит в себе сценарии фильмов целиком и их краткое содержание |https://www.kinomania.ru/scripts |https://www.kinomania.ru/scripts |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/kinomania_scripts |ru\en |27 |2603.407407 |510.375447 |1887 |2175 |2370 |3069 |3616 | |dim/bugurt_thread_prompts |https://huggingface.co/datasets/dim/bugurt_thread_prompts |Небольшой набор размеченных бугуртов вместе с моим другом, для того чтобы модель научилась писать бугурты на конкретную ситуацию. Собраны из телеграм паблика БУГУРТ ТРЕД(https://t.me/bugurtthread) |https://t.me/bugurtthread |https://t.me/bugurtthread |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/bugurt_thread |ru |223 |334.4529148 |271.2557988|48 |148.5 |254 |434.5 |1645 | |dim/russian_lyrics_prompts |https://huggingface.co/datasets/dim/russian_lyrics_prompts |Небольшой датасет промптов собранный мною из различных учебников по стихосложению, чтобы модель научилась писать стихи, используя необходимый литературный прием на конкретную тему. |Учебник стихосложения |https://stihi.ru/uchebnik/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/russian_lyrics_prompts |ru |43 |106.1395349 |71.00220701|45 |71 |83 |96.5 |411 | |dim/ru_instruct_gpt4 |https://huggingface.co/datasets/dim/ru_instruct_gpt4 |Датасет каких-то инструкций на русском сгенерированных GPT-4 |lksy/ru_instruct_gpt4 |https://huggingface.co/datasets/lksy/ru_instruct_gpt4 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_instruct_gpt4 |ru |14222 |259.2173393 |237.9433891|16 |109 |175 |271 |1374 | |dim/gpt_roleplay_realm |https://huggingface.co/datasets/dim/gpt_roleplay_realm |Диалоги выдуманных персонажей при помощи GPT-4, диалоги были сгенерированны при помощи GPT-3.5. Русский и английский. |IlyaGusev/gpt_roleplay_realm |https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/gpt_roleplay_realm |ru\en |8700 |504.2424138 |117.6228987|180 |424 |489 |569 |1207 | |dim/ultrachat_ru |https://huggingface.co/datasets/dim/ultrachat_ru |Какой-то рандомный датасет диалогов от chatgpt, который я нашел на huggingface. Из текста диалогов были вырезаны шаблонные фразы по типу: "я не могу выполнить", "как языковая модель" и тд. Потому что обычно после этого следовало вменяемое решение задачи. |kaleinaNyan/UltraChat_ru |https://huggingface.co/datasets/kaleinaNyan/UltraChat_ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ultrachat_ru |ru |500 |1781.782 |901.1212735|267 |1113.25|1648 |2250.25|7303 | |dim/scitldr |https://huggingface.co/datasets/dim/scitldr |Саммаризация научных статей на английском языке, выполненная экспертами. |allenai/scitldr |https://huggingface.co/datasets/allenai/scitldr |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/scitldr |en |3229 |258.748529 |71.41209752|60 |209 |252 |303 |689 | |dim/linux_man_pages_tldr_summarized |https://huggingface.co/datasets/dim/linux_man_pages_tldr_summarized |Саммаризация мануалов для инструментов линукс в удобный набор команд с их кратким описанием. |tmskss/linux-man-pages-tldr-summarized |https://huggingface.co/datasets/tmskss/linux-man-pages-tldr-summarized |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/linux-man-pages-tldr-summarized |en |481 |1567.727651 |3590.30871 |96 |405 |765 |1386 |49888 | |dim/dolphin_ru_3k |https://huggingface.co/datasets/dim/dolphin_ru_3k |Подвыборка размера 3000 переведенных заданий dolphin. Примеры из оригинального датасета это промпты из FLANv2 и решения при помощи GPT-4 или GPT-3.5. |d0rj/dolphin-ru |https://huggingface.co/datasets/d0rj/dolphin-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dolphin_ru |ru |3000 |556.1133333 |650.0962612|19 |207 |369.5 |720.25 |6787 | |dim/runne_prompts |https://huggingface.co/datasets/dim/runne_prompts |Промпты составленные из датасета RuNNE. Лично я при обучении сотавил промпт следующим образом. Сначала идет текст "Найди все именованные сущности в данном тексте:", а затем шел сам текст. В качестве выхода модели нужно сгенерировать JSON где содержатся все найденные именованные сущности. К примеру так [{"name": "PERSON", "ent": "Ким Чен Нама", "pos": "0 12"}, {"name": "ORGANIZATION", "ent": "Полиция Малайзии", "pos": "56 72"}] |iluvvatar/RuNNE |https://huggingface.co/datasets/iluvvatar/RuNNE |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/RuNNE |ru |537 |1479.750466 |230.0259174|581 |1337 |1480 |1635 |1988 | |dim/lurk_prompts |https://huggingface.co/datasets/dim/lurk_prompts |Набор определений различных терминов с сайта lurk. Сами промпты были составлены автоматически следующим образом. напиши определение для (ОПРЕДЕЛЕНИЕ) в стиле lurk |averoo/lurk |https://huggingface.co/datasets/averoo/lurk/viewer/default/train?p=2 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/lurk |ru |5671 |3450.34262 |4147.897824|35 |710.5 |2010 |4593 |55098 | |dim/panorama_prompts_10k |https://huggingface.co/datasets/dim/panorama_prompts_10k |Набор юмористических заголовков и текстов новостей с сайта панорама. |its5Q/panorama |https://huggingface.co/datasets/its5Q/panorama |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/panorama |ru |11024 |516.9588171 |191.3774023|36 |422 |498 |585 |3496 | |dim/resh_edu_short_prompts |https://huggingface.co/datasets/dim/resh_edu_short_prompts |Набор уроков с сайта resh.edu.ru включающих в себя название урока, тему, класс и текст урока с заданиями. |its5Q/resh-edu |https://huggingface.co/datasets/its5Q/resh-edu |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/resh_edu |ru |2106 |1431.510921 |435.7847102|56 |1175.5 |1517 |1777 |2029 | |dim/databricks_dolly_15k_ru |https://huggingface.co/datasets/dim/databricks_dolly_15k_ru |Переведенный датасет dolly на русский язык. Включает в себя набор инструкций на обширное количество тематик. |dwarf2/databricks-dolly-15k-ru |https://huggingface.co/dwarf2/databricks-dolly-15k-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/databricks_dolly_15k_ru |ru |14914 |305.4638595 |405.874049 |8 |87 |182 |370 |9268 | |dim/databricks_dolly_15k_en |https://huggingface.co/datasets/dim/databricks_dolly_15k_en |databricks-dolly-15k — это набор данных с открытым исходным кодом, содержащий записи о выполнении инструкций, созданные тысячами сотрудников Databricks в нескольких поведенческих категориях, изложенных в документе InstructGPT, включая мозговой штурм, классификацию, закрытый контроль качества, генерацию, извлечение информации, открытый контроль качества и обобщение. |databricks/databricks-dolly-15k |https://huggingface.co/datasets/databricks/databricks-dolly-15k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/databricks_dolly_15k_en |en |15011 |204.7264006 |302.5539423|6 |57 |119 |242 |8883 | |dim/grammarly_coedit |https://huggingface.co/datasets/dim/grammarly_coedit |Набор промптов, которые просят исправить грамматические, стилистические ошибки на английском. |grammarly/coedit |https://huggingface.co/datasets/grammarly/coedit |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grammarly_coedit |en |82466 |53.7128271 |26.73822864|10 |35 |46 |64 |694 | |dim/kinopoisk_prompts |https://huggingface.co/datasets/dim/kinopoisk_prompts |Отзывы с кинопоиска на топ 250 фильмов. В промптах я прошу написать хороший, плохой или нейтральный отзыв на определенный фильм. |blinoff/kinopoisk |https://huggingface.co/datasets/blinoff/kinopoisk |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/kinopoisk |ru |36591 |875.0955973 |565.3212035|48 |484 |733 |1117 |8628 | |dim/medical_qa_ru_prompts |https://huggingface.co/datasets/dim/medical_qa_ru_prompts |Какие-то вопросы и ответы с какого-то медицинского форума. В данной версии датасета только первый ответ из оригинала. |blinoff/medical_qa_ru_data |https://huggingface.co/datasets/blinoff/medical_qa_ru_data |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/medical_qa_ru_data |ru |80101 |206.710528 |175.4343973|12 |106 |161 |247 |5062 | |dim/joke_explaination_prompts |https://huggingface.co/datasets/dim/joke_explaination_prompts |Объяснение шуток на английском. От изначального датасета отличается тем, что я убрал последнее предложение из объяснения, так как оно ссылается на видео на сайте. |theblackcat102/joke_explaination |https://huggingface.co/datasets/theblackcat102/joke_explaination |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/joke_explaination |en |364 |143.5741758 |68.90275411|21 |99 |137.5 |189.25 |334 | |dim/oa_stackexchange_200k |https://huggingface.co/datasets/dim/oa_stackexchange_200k |Вопросы-ответы со stackexchange. Оригинальный датасет был составлен следующим образом: были выбраны только темы с принятым ответом, для которых длина вопроса и ответа составляет менее 1000 символов. Другие ответы, вопросы без принятых ответов или длинные записи были удалены. Так как оригинальный датасет слишком большой, я рандомно выбрал 200k семплов. |donfu/oa-stackexchange |https://huggingface.co/datasets/donfu/oa-stackexchange |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/oa_stackexchange |en |200000 |276.29862 |112.5004436|22 |194 |265 |345 |1226 | |dim/scale_helpful_no_math |https://huggingface.co/datasets/dim/scale_helpful_no_math |Какой-то набор диалогов с вопросами-ответами на английском, происхождение неизвестно. |HuggingFaceH4/scale_helpful_no_math |https://huggingface.co/datasets/HuggingFaceH4/scale_helpful_no_math/viewer/default/train_rm |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/scale_helpful_no_math |en |17095 |1235.302603 |838.1097885|53 |663 |1063 |1617 |34480 | |dim/law_stackexchange_prompts |https://huggingface.co/datasets/dim/law_stackexchange_prompts |Вопросы про закон на английском языке со StackExchange. Оригинальный датасет был преобразован в markdown. |ymoslem/Law-StackExchange |https://huggingface.co/datasets/ymoslem/Law-StackExchange |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/law_stackexchange |en |24343 |689.1184324 |565.0316906|43 |354 |540 |836 |8969 | |dim/ficbook_prompts_best_10k |https://huggingface.co/datasets/dim/ficbook_prompts_best_10k |Топ 10k лучших фанфиков с сайта ficbook.net. Все промпты выглядят следующим образом: напиши фанфик с названием {title} и следующим описанием {description}, с тегами {tags}, Где title это оригинальное название, description оригинальное описание, tags это теги данного произведения. |AlexWortega/FicBook |https://huggingface.co/datasets/AlexWortega/FicBook |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ficbook |ru |10000 |1737.8214 |402.0748161|166 |1716 |1950 |1950 |1952 | |dim/azbyka_logic_ru |https://huggingface.co/datasets/dim/azbyka_logic_ru |Небольшой набор детских логических и православных задач, взятых с сайта https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi . Обычно у них почти нет развернутого решения, только ответ. Я пытался расписать решение некоторых задач, но меня хватило только на 35, если кто-то займется подобным буду рад https://docs.google.com/spreadsheets/d/1JRbtppbZCUbV_Eqd0nKbRDQEuPnJIAgJ70cUILEDUI4/edit?usp=sharing . |Логические и занимательные задачи (300 задач) |https://azbyka.ru/deti/logicheskie-i-zanimatelnye-zadachi |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/azbyka_logic_ru |ru |480 |77.4375 |77.56990416|14 |31 |50 |91 |652 | |dim/povarenok |https://huggingface.co/datasets/dim/povarenok |46k лучших рецептов с сайта povarenok.ru, содержит текст рецепта, список ингридиентов, название блюда |https://www.povarenok.ru/recipes/ |https://www.povarenok.ru/recipes/ |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/povarenok |ru |46500 |488.9118495 |344.8563249|31 |281 |440 |632 |5542 | |dim/AO3_fandom_chatbot_1to1 |https://huggingface.co/datasets/dim/AO3_fandom_chatbot_1to1 |Какой-то набор ролеплейных диалогов с описанием персонажей и их отыгрышем. Происхождение неизвестно. |ebony59/AO3_fandom_chatbot_1to1 |https://huggingface.co/datasets/ebony59/AO3_fandom_chatbot_1to1 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/AO3_fandom_chatbot_1to1 |en |614 |493.7166124 |226.3885365|129 |328.25 |432.5 |611.75 |1272 | |dim/habr_prompts_5k |https://huggingface.co/datasets/dim/habr_prompts_5k |Статьи с хабра. Датасет был составлен с помощью chatgpt, chatgpt преобразовывал заголовки таким образом чтобы они звучали как вопросы от пользователя, в качестве таргета выступала сама статья. |IlyaGusev/habr |https://huggingface.co/datasets/IlyaGusev/habr |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/habr |ru |5000 |1732.892 |454.8418369|19 |1920.75|1950 |1951 |1952 | |dim/what_where_when_50k |https://huggingface.co/datasets/dim/what_where_when_50k |50k вопросов с решениями с сайта что где когда. В качестве промпта выступает вопрос, в качестве ответа конкатенация объяснения и краткого ответа. Все вопросы-ответы вы можете найти по этой ссылке https://huggingface.co/datasets/dim/what_where_when_ru |https://db.chgk.info |https://db.chgk.info |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/what_where_when |ru |50000 |169.1862 |68.91119898|18 |122 |158 |202 |1167 | |dim/competition_math |https://huggingface.co/datasets/dim/competition_math |Датасет олимпиадной математики на английском. The Mathematics Aptitude Test of Heuristics (MATH) dataset. |competition_math |https://huggingface.co/datasets/competition_math |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/competition_math |en |7500 |317.5254667 |267.8583731|34 |147 |234 |393 |3029 | |dim/sharegpt_short_en_30k |https://huggingface.co/datasets/dim/sharegpt_short_en_30k |Короткие диалоги на английском из sharegpt |RyokoAI/ShareGPT52K |https://huggingface.co/datasets/RyokoAI/ShareGPT52K |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/sharegpt |en |29597 |749.3149981 |516.3702473|3 |336 |630 |1095 |2021 | |dim/ru_turbo_alpaca_evol_instruct |https://huggingface.co/datasets/dim/ru_turbo_alpaca_evol_instruct |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru_turbo_alpaca_evol_instruct |https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca_evol_instruct |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_turbo_alpaca_evol_instruct |ru |47793 |453.0887996 |289.5498356|17 |221 |430 |623 |4647 | |dim/ru_turbo_saiga |https://huggingface.co/datasets/dim/ru_turbo_saiga |Набор инструкций различной тематики на русском языке, сгенерированных при помощи chatgpt. |IlyaGusev/ru_turbo_saiga |https://huggingface.co/datasets/IlyaGusev/ru_turbo_saiga |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/ru_turbo_saiga |ru |37699 |412.7508687 |113.346917 |87 |339 |398 |466 |1427 | |dim/bugurt_completion_prompts |https://huggingface.co/datasets/dim/bugurt_completion_prompts |Обрезанные бугурты, где в качестве промпта используется строка вида - продолжи бугурт: первая строчка бугурта |https://t.me/bugurtthread |https://t.me/bugurtthread |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/bugurt_thread |ru |5000 |280.2466 |320.4353681|32 |111 |178 |331 |11333 | |dim/tldr_17_50k |https://huggingface.co/datasets/dim/tldr_17_50k |Очень вольная абстрактная саммаризация постов с реддита в одну строчку |webis/tldr-17 |https://huggingface.co/datasets/webis/tldr-17 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/tldr_17 |en |50000 |421.12752 |403.346214 |10 |177 |303 |525 |9592 | |dim/grade_school_math_instructions |https://huggingface.co/datasets/dim/grade_school_math_instructions |OpenAI's grade-school-math датасет преобразованный в промпты. |qwedsacf/grade-school-math-instructions |https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grade-school-math-instructions |en |8792 |171.6310282 |63.09232668|50 |124 |161 |206 |511 | |dim/tldr_news |https://huggingface.co/datasets/dim/tldr_news |Хедлайны и текст новостей на различную тематику. |JulesBelveze/tldr_news |https://huggingface.co/datasets/JulesBelveze/tldr_news |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/tldr_news |en |7138 |133.1004483 |46.48736493|23 |100 |133 |161 |476 | |dim/grade_school_math_instructions_ru|https://huggingface.co/datasets/dim/grade_school_math_instructions_ru|OpenAI's grade-school-math датасет переведенный на русский. |d0rj/gsm8k-ru |https://huggingface.co/datasets/d0rj/gsm8k-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/grade_school_math_instructions_ru|ru |7473 |259.8321959 |100.1229127|78 |185 |241 |314 |838 | |dim/dialogsum |https://huggingface.co/datasets/dim/dialogsum |Саммаризация диалогов на английском языке, разметка выполнялась вручную. |knkarthick/dialogsum |https://huggingface.co/datasets/knkarthick/dialogsum |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dialogsum |en |12460 |269.6467095 |126.285664 |75 |191 |245 |327 |1725 | |dim/HC3_ru |https://huggingface.co/datasets/dim/HC3_ru |Вопросы-ответы с реддита, есть ответы сгенерированные chatgpt и реальные ответы пользователей. Я использовал только реальные ответы пользователей. |d0rj/HC3-ru |https://huggingface.co/datasets/d0rj/HC3-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/HC3_ru |ru |24322 |360.5608503 |330.2285903|15 |168 |267 |435 |10025 | |dim/horoscopes_ru_10k |https://huggingface.co/datasets/dim/horoscopes_ru_10k |10k гороскопов, с промптами где я прошу сгенерировать гороском для определенного знака зодиака |dkagramanyan/horoscopes_ru |https://huggingface.co/datasets/dkagramanyan/horoscopes_ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/horoscopes_ru |ru |10000 |183.1443 |31.62023184|55 |159 |187 |201 |464 | |dim/yandex_q_200k |https://huggingface.co/datasets/dim/yandex_q_200k |200k рандомно выбранных вопросов-ответов с сайта yandex q. |its5Q/yandex-q |https://huggingface.co/datasets/its5Q/yandex-q |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/yandex_q |ru |200000 |304.569005 |340.7808288|18 |127 |202 |353 |19294 | |dim/leetcodesolutions_en_2k |https://huggingface.co/datasets/dim/leetcodesolutions_en_2k |Решения задач с leetcode на разных языках. |TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/leetcodesolutions_en_2k |en |2048 |740.7441406 |253.2493282|297 |565 |685 |857 |1960 | |dim/forum_uristov_rf_prompts |https://huggingface.co/datasets/dim/forum_uristov_rf_prompts |Вопросы-ответы с российского юридического форума. |https://xn----dtbrojdkckkfj9k.xn--p1ai/vopros-yuristu?page=560|https://xn----dtbrojdkckkfj9k.xn--p1ai/vopros-yuristu?page=560 |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/forum_uristov_rf |ru |1849 |321.0540833 |429.58896 |31 |134 |210 |349 |6470 | |dim/dialogsum_ru |https://huggingface.co/datasets/dim/dialogsum_ru |Саммаризация диалогов на русском языке, перевод dialogsum. |d0rj/dialogsum-ru |https://huggingface.co/datasets/d0rj/dialogsum-ru |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/dialogsum-ru |ru |12460 |364.2813804 |178.7117754|98 |250 |329 |446 |2300 | |dim/huggingartists_prompts |https://huggingface.co/datasets/dim/huggingartists_prompts |Промпты, которые просят продолжить песню в стиле определенного исполнителя. В данном наборе содержатся почти все исполнители, которых вы можете найти в этой организации https://huggingface.co/huggingartists |https://huggingface.co/huggingartists |https://huggingface.co/huggingartists |https://github.com/dmitrymailk/verbalist/tree/master/verbalist/datasets/huggingartists |ru |64006 |561.6732025 |586.18458 |28 |297 |453 |720 |32949 | ### Модели - [Ссылка на google sheets](https://docs.google.com/spreadsheets/d/1LGCy8RBR_Yk9wHRcp0IDV8eut4D7tAQBRgqCLqxYk3E/edit?usp=sharing) |model_name |ru_cola(zero shot, prompt)|russian super glue(zero shot)|mmlu_ru(NLPCoreTeam)|mt_bench_ru_turn_1|mt_bench_ru_turn_2|mt_bench_ru_avg|mt_bench_generation|average | |-----------------------------------------|--------------------------|-----------------------------|--------------------|------------------|------------------|---------------|-------------------|------------| |IlyaGusev/saiga_mistral_7b_lora |0.710082526 |0.64 |0.4848747094 |0.6567901 |0.53375 |0.59527005 | |0.6050994671| |verbalist_7b_v9_800 |0.6709723717 |0.665 |0.4801731633 |0.6175 |0.4375 |0.4375 | |0.574229107 | |Open-Orca/Mistral-7B-OpenOrca |0.6917832795 |0.652 |0.4592925739 |0.685 |0.595 |0.64 | |0.6166151707| |mistral-open-orca-ru-4600-step |0.6928597058 |0.663 |0.4347 |0.71625 |0.546 |0.631125 | |0.6105619412| |verbalist_v10_1650 |0.7201291712 |0.66 |0.4920804261 |0.56125 |0.5 |0.530625 | |0.5866919194| |gpt-3.5-turbo |0.72 |0.682 | |0.87 |0.745 |0.8075 | |0.75425 | |openchat/openchat_3.5 |0.6727664155 |0.642 | | | |#DIV/0! | |0.6573832078| |dim/tiny-llama-2T-open-orca-ru-10000-step|0.6361679225 |0.451 |0.2999564271 | | | | |0.4623747832| - [dim/mistral-open-orca-ru-4600-step](https://huggingface.co/dim/mistral-open-orca-ru-4600-step) - [dim/verbalist_7b_v9_800](https://huggingface.co/dim/verbalist_7b_v9_800) - [dim/verbalist_v10_1650](https://huggingface.co/dim/verbalist_v10_1650) ### Код обучения - [общий алгоритм обучения](https://github.com/dmitrymailk/verbalist/blob/master/verbalist/model/src/train.py) - [формирование датасетов для обучения](https://github.com/dmitrymailk/verbalist/blob/master/verbalist/model/src/dataset.py#L176) ### Оборудование Все обучение и инференс производится на видеокарте A100, на других видеокартах была обнаружена существенная деградация качества при инференсе, данный аспект требует дополнительного изучения. - NVIDIA A100-SXM4-40GB - NVIDIA-SMI 535.54.03 - Driver Version: 535.54.03 - CUDA Version: 12.2 - torch==2.0.1+cu118 ### Дальнейшее развитие Самое простое, что можно сделать это переводить уже имеющиеся хорошие датасеты с английского на русский при помощи GPT-4. Более сложное это собирать больше разнообразных данных из различных доменов. Я могу лишь подкинуть идеи для того какие датасеты можно собрать еще. - решебники по литературе, русскому и другим предметам - задания со всяких бирж труда - [краткие пересказы произведений, анализ произведений, сочинения по ним](http://www.litra.ru/shortwork/) - [туториалы с digital ocean (более 7000)](https://www.digitalocean.com/community/tutorials) - [туториалы с selectel](https://selectel.ru/blog/tutorials/) - больше форумов на различные тематики - [бесплатные эссе с ivypanda essays](https://ivypanda.com/essays/) и дальнейший их перевод на русский - больше стихов и песен - [олимпиадные русские задачи](https://math.ru/problems/) их очень сложно собирать, так как большинство их них живут только в PDF или docx. Но их довольно много и они довольно отличаются от олимпиадной математики на английском. Но у меня нет времени этим заниматься. - фанфики на иностранном языке - исправить текущие автоматические промпты на более разнообразные, при помощи chatgpt

open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16

--- pretty_name: Evaluation run of ConvexAI/Harmony-4x7B-bf16 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ConvexAI/Harmony-4x7B-bf16](https://huggingface.co/ConvexAI/Harmony-4x7B-bf16)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-02T02:08:05.844408](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16/blob/main/results_2024-02-02T02-08-05.844408.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6519815241664966,\n\ \ \"acc_stderr\": 0.03207888973753625,\n \"acc_norm\": 0.6516491536204881,\n\ \ \"acc_norm_stderr\": 0.03274551770079104,\n \"mc1\": 0.4602203182374541,\n\ \ \"mc1_stderr\": 0.01744801722396088,\n \"mc2\": 0.6205565135867297,\n\ \ \"mc2_stderr\": 0.015134580676846265\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.658703071672355,\n \"acc_stderr\": 0.013855831287497724,\n\ \ \"acc_norm\": 0.6834470989761092,\n \"acc_norm_stderr\": 0.013592431519068077\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6810396335391357,\n\ \ \"acc_stderr\": 0.004651211311633843,\n \"acc_norm\": 0.8674566819358693,\n\ \ \"acc_norm_stderr\": 0.0033838751726700243\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.037150621549989056,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.037150621549989056\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42857142857142855,\n \"acc_stderr\": 0.025487187147859375,\n \"\ acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.025487187147859375\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\ \ \"acc_stderr\": 0.02390491431178265,\n \"acc_norm\": 0.7709677419354839,\n\ \ \"acc_norm_stderr\": 0.02390491431178265\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.37777777777777777,\n \"acc_stderr\": 0.02956070739246572,\n \ \ \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.02956070739246572\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.02983796238829193,\n \ \ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.02983796238829193\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538272,\n \"\ acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474086,\n \"\ acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474086\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.034465133507525995,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.034465133507525995\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281376\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608304,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608304\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4011173184357542,\n\ \ \"acc_stderr\": 0.01639222189940708,\n \"acc_norm\": 0.4011173184357542,\n\ \ \"acc_norm_stderr\": 0.01639222189940708\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292452,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292452\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46870925684485004,\n\ \ \"acc_stderr\": 0.01274520462608314,\n \"acc_norm\": 0.46870925684485004,\n\ \ \"acc_norm_stderr\": 0.01274520462608314\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6948529411764706,\n \"acc_stderr\": 0.0279715413701706,\n\ \ \"acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.0279715413701706\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706207,\n \ \ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706207\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.02553843336857833,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.02553843336857833\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4602203182374541,\n\ \ \"mc1_stderr\": 0.01744801722396088,\n \"mc2\": 0.6205565135867297,\n\ \ \"mc2_stderr\": 0.015134580676846265\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.813733228097869,\n \"acc_stderr\": 0.01094187795567621\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7210007581501138,\n \ \ \"acc_stderr\": 0.012354115779970308\n }\n}\n```" repo_url: https://huggingface.co/ConvexAI/Harmony-4x7B-bf16 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|arc:challenge|25_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-02T02-08-05.844408.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|gsm8k|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hellaswag|10_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-02T02-08-05.844408.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-management|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T02-08-05.844408.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|truthfulqa:mc|0_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-02T02-08-05.844408.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_02T02_08_05.844408 path: - '**/details_harness|winogrande|5_2024-02-02T02-08-05.844408.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-02T02-08-05.844408.parquet' - config_name: results data_files: - split: 2024_02_02T02_08_05.844408 path: - results_2024-02-02T02-08-05.844408.parquet - split: latest path: - results_2024-02-02T02-08-05.844408.parquet --- # Dataset Card for Evaluation run of ConvexAI/Harmony-4x7B-bf16  Dataset automatically created during the evaluation run of model [ConvexAI/Harmony-4x7B-bf16](https://huggingface.co/ConvexAI/Harmony-4x7B-bf16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-02T02:08:05.844408](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__Harmony-4x7B-bf16/blob/main/results_2024-02-02T02-08-05.844408.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6519815241664966, "acc_stderr": 0.03207888973753625, "acc_norm": 0.6516491536204881, "acc_norm_stderr": 0.03274551770079104, "mc1": 0.4602203182374541, "mc1_stderr": 0.01744801722396088, "mc2": 0.6205565135867297, "mc2_stderr": 0.015134580676846265 }, "harness|arc:challenge|25": { "acc": 0.658703071672355, "acc_stderr": 0.013855831287497724, "acc_norm": 0.6834470989761092, "acc_norm_stderr": 0.013592431519068077 }, "harness|hellaswag|10": { "acc": 0.6810396335391357, "acc_stderr": 0.004651211311633843, "acc_norm": 0.8674566819358693, "acc_norm_stderr": 0.0033838751726700243 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7039473684210527, "acc_stderr": 0.037150621549989056, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.037150621549989056 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909282, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42857142857142855, "acc_stderr": 0.025487187147859375, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.025487187147859375 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7709677419354839, "acc_stderr": 0.02390491431178265, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.02390491431178265 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175008, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695483, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695483 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033456, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033456 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402534, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402534 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37777777777777777, "acc_stderr": 0.02956070739246572, "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.02956070739246572 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6974789915966386, "acc_stderr": 0.02983796238829193, "acc_norm": 0.6974789915966386, "acc_norm_stderr": 0.02983796238829193 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5370370370370371, "acc_stderr": 0.03400603625538272, "acc_norm": 0.5370370370370371, "acc_norm_stderr": 0.03400603625538272 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8186274509803921, "acc_stderr": 0.027044621719474086, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.027044621719474086 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.02595502084162113, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.02595502084162113 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8091603053435115, "acc_stderr": 0.034465133507525995, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.034465133507525995 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.047184714852195886, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.047184714852195886 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.021586494001281376, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.021586494001281376 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608304, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608304 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4011173184357542, "acc_stderr": 0.01639222189940708, "acc_norm": 0.4011173184357542, "acc_norm_stderr": 0.01639222189940708 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292452, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292452 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7530864197530864, "acc_stderr": 0.02399350170904211, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.02399350170904211 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46870925684485004, "acc_stderr": 0.01274520462608314, "acc_norm": 0.46870925684485004, "acc_norm_stderr": 0.01274520462608314 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6948529411764706, "acc_stderr": 0.0279715413701706, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.0279715413701706 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6830065359477124, "acc_stderr": 0.018824219512706207, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.018824219512706207 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.02553843336857833, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.02553843336857833 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.4602203182374541, "mc1_stderr": 0.01744801722396088, "mc2": 0.6205565135867297, "mc2_stderr": 0.015134580676846265 }, "harness|winogrande|5": { "acc": 0.813733228097869, "acc_stderr": 0.01094187795567621 }, "harness|gsm8k|5": { "acc": 0.7210007581501138, "acc_stderr": 0.012354115779970308 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

SEACrowd/id_abusive

--- tags: - sentiment-analysis language: - ind --- # id_abusive The ID_ABUSIVE dataset is collection of 2,016 informal abusive tweets in Indonesian language, designed for sentiment analysis NLP task. This dataset is crawled from Twitter, and then filtered and labelled manually by 20 volunteer annotators. The dataset labelled into three labels namely not abusive language, abusive but not offensive, and offensive language. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @article{IBROHIM2018222, title = {A Dataset and Preliminaries Study for Abusive Language Detection in Indonesian Social Media}, journal = {Procedia Computer Science}, volume = {135}, pages = {222-229}, year = {2018}, note = {The 3rd International Conference on Computer Science and Computational Intelligence (ICCSCI 2018) : Empowering Smart Technology in Digital Era for a Better Life}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2018.08.169}, url = {https://www.sciencedirect.com/science/article/pii/S1877050918314583}, author = {Muhammad Okky Ibrohim and Indra Budi}, keywords = {abusive language, twitter, machine learning}, abstract = {Abusive language is an expression (both oral or text) that contains abusive/dirty words or phrases both in the context of jokes, a vulgar sex conservation or to cursing someone. Nowadays many people on the internet (netizens) write and post an abusive language in the social media such as Facebook, Line, Twitter, etc. Detecting an abusive language in social media is a difficult problem to resolve because this problem can not be resolved just use word matching. This paper discusses a preliminaries study for abusive language detection in Indonesian social media and the challenge in developing a system for Indonesian abusive language detection, especially in social media. We also built reported an experiment for abusive language detection on Indonesian tweet using machine learning approach with a simple word n-gram and char n-gram features. We use Naive Bayes, Support Vector Machine, and Random Forest Decision Tree classifier to identify the tweet whether the tweet is a not abusive language, abusive but not offensive, or offensive language. The experiment results show that the Naive Bayes classifier with the combination of word unigram + bigrams features gives the best result i.e. 70.06% of F1 - Score. However, if we classifying the tweet into two labels only (not abusive language and abusive language), all classifier that we used gives a higher result (more than 83% of F1 - Score for every classifier). The dataset in this experiment is available for other researchers that interest to improved this study.} } ``` ## License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International ## Homepage [https://www.sciencedirect.com/science/article/pii/S1877050918314583](https://www.sciencedirect.com/science/article/pii/S1877050918314583) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)

open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF

--- pretty_name: Evaluation run of FPHam/Sydney_Overthinker_13b_HF dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FPHam/Sydney_Overthinker_13b_HF](https://huggingface.co/FPHam/Sydney_Overthinker_13b_HF)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-08T02:51:52.068469](https://huggingface.co/datasets/open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF/blob/main/results_2023-12-08T02-51-52.068469.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5136287950246363,\n\ \ \"acc_stderr\": 0.034087739996992605,\n \"acc_norm\": 0.5191163704808761,\n\ \ \"acc_norm_stderr\": 0.03483348555557295,\n \"mc1\": 0.2998776009791922,\n\ \ \"mc1_stderr\": 0.01604035296671362,\n \"mc2\": 0.45697851910783077,\n\ \ \"mc2_stderr\": 0.015427158150833389\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5614334470989761,\n \"acc_stderr\": 0.014500682618212865,\n\ \ \"acc_norm\": 0.5895904436860068,\n \"acc_norm_stderr\": 0.014374922192642664\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6118303126867158,\n\ \ \"acc_stderr\": 0.004863375698153865,\n \"acc_norm\": 0.8085042820155347,\n\ \ \"acc_norm_stderr\": 0.0039267405951797715\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4888888888888889,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04063302731486671,\n\ \ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04063302731486671\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5132075471698113,\n \"acc_stderr\": 0.030762134874500476,\n\ \ \"acc_norm\": 0.5132075471698113,\n \"acc_norm_stderr\": 0.030762134874500476\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5972222222222222,\n\ \ \"acc_stderr\": 0.04101405519842425,\n \"acc_norm\": 0.5972222222222222,\n\ \ \"acc_norm_stderr\": 0.04101405519842425\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n\ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4797687861271676,\n\ \ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n\ \ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.0433643270799318,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.0433643270799318\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n\ \ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\ \ \"acc_stderr\": 0.041424397194893624,\n \"acc_norm\": 0.2631578947368421,\n\ \ \"acc_norm_stderr\": 0.041424397194893624\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2962962962962963,\n \"acc_stderr\": 0.023517294335963286,\n \"\ acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.023517294335963286\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\ \ \"acc_stderr\": 0.042857142857142816,\n \"acc_norm\": 0.35714285714285715,\n\ \ \"acc_norm_stderr\": 0.042857142857142816\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5870967741935483,\n\ \ \"acc_stderr\": 0.028009138125400387,\n \"acc_norm\": 0.5870967741935483,\n\ \ \"acc_norm_stderr\": 0.028009138125400387\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3399014778325123,\n \"acc_stderr\": 0.033327690684107895,\n\ \ \"acc_norm\": 0.3399014778325123,\n \"acc_norm_stderr\": 0.033327690684107895\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6181818181818182,\n \"acc_stderr\": 0.03793713171165634,\n\ \ \"acc_norm\": 0.6181818181818182,\n \"acc_norm_stderr\": 0.03793713171165634\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6616161616161617,\n \"acc_stderr\": 0.033711241426263014,\n \"\ acc_norm\": 0.6616161616161617,\n \"acc_norm_stderr\": 0.033711241426263014\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7409326424870466,\n \"acc_stderr\": 0.0316187791793541,\n\ \ \"acc_norm\": 0.7409326424870466,\n \"acc_norm_stderr\": 0.0316187791793541\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5230769230769231,\n \"acc_stderr\": 0.025323990861736232,\n\ \ \"acc_norm\": 0.5230769230769231,\n \"acc_norm_stderr\": 0.025323990861736232\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \ \ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.46218487394957986,\n \"acc_stderr\": 0.0323854694875898,\n \ \ \"acc_norm\": 0.46218487394957986,\n \"acc_norm_stderr\": 0.0323854694875898\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.25165562913907286,\n \"acc_stderr\": 0.035433042343899844,\n \"\ acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.035433042343899844\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6862385321100918,\n \"acc_stderr\": 0.019894723341469116,\n \"\ acc_norm\": 0.6862385321100918,\n \"acc_norm_stderr\": 0.019894723341469116\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.33796296296296297,\n \"acc_stderr\": 0.03225941352631295,\n \"\ acc_norm\": 0.33796296296296297,\n \"acc_norm_stderr\": 0.03225941352631295\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6323529411764706,\n \"acc_stderr\": 0.03384132045674118,\n \"\ acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.03384132045674118\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6708860759493671,\n \"acc_stderr\": 0.030587326294702368,\n \ \ \"acc_norm\": 0.6708860759493671,\n \"acc_norm_stderr\": 0.030587326294702368\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6412556053811659,\n\ \ \"acc_stderr\": 0.03219079200419995,\n \"acc_norm\": 0.6412556053811659,\n\ \ \"acc_norm_stderr\": 0.03219079200419995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\ \ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.04139112727635463,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.04139112727635463\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5705521472392638,\n \"acc_stderr\": 0.03889066619112722,\n\ \ \"acc_norm\": 0.5705521472392638,\n \"acc_norm_stderr\": 0.03889066619112722\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6796116504854369,\n \"acc_stderr\": 0.04620284082280041,\n\ \ \"acc_norm\": 0.6796116504854369,\n \"acc_norm_stderr\": 0.04620284082280041\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7948717948717948,\n\ \ \"acc_stderr\": 0.026453508054040335,\n \"acc_norm\": 0.7948717948717948,\n\ \ \"acc_norm_stderr\": 0.026453508054040335\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.0498887651569859,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7203065134099617,\n\ \ \"acc_stderr\": 0.016050792148036522,\n \"acc_norm\": 0.7203065134099617,\n\ \ \"acc_norm_stderr\": 0.016050792148036522\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5809248554913294,\n \"acc_stderr\": 0.026564178111422622,\n\ \ \"acc_norm\": 0.5809248554913294,\n \"acc_norm_stderr\": 0.026564178111422622\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32737430167597764,\n\ \ \"acc_stderr\": 0.015694238967737383,\n \"acc_norm\": 0.32737430167597764,\n\ \ \"acc_norm_stderr\": 0.015694238967737383\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.028491993586171566,\n\ \ \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.028491993586171566\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6045016077170418,\n\ \ \"acc_stderr\": 0.02777091853142784,\n \"acc_norm\": 0.6045016077170418,\n\ \ \"acc_norm_stderr\": 0.02777091853142784\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6080246913580247,\n \"acc_stderr\": 0.027163686038271146,\n\ \ \"acc_norm\": 0.6080246913580247,\n \"acc_norm_stderr\": 0.027163686038271146\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3723404255319149,\n \"acc_stderr\": 0.02883892147125146,\n \ \ \"acc_norm\": 0.3723404255319149,\n \"acc_norm_stderr\": 0.02883892147125146\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3963494132985658,\n\ \ \"acc_stderr\": 0.012492830452095217,\n \"acc_norm\": 0.3963494132985658,\n\ \ \"acc_norm_stderr\": 0.012492830452095217\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.03016191193076711,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.03016191193076711\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5163398692810458,\n \"acc_stderr\": 0.02021703065318646,\n \ \ \"acc_norm\": 0.5163398692810458,\n \"acc_norm_stderr\": 0.02021703065318646\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.04673752333670239,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.04673752333670239\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6081632653061224,\n \"acc_stderr\": 0.031251275910891656,\n\ \ \"acc_norm\": 0.6081632653061224,\n \"acc_norm_stderr\": 0.031251275910891656\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6467661691542289,\n\ \ \"acc_stderr\": 0.03379790611796777,\n \"acc_norm\": 0.6467661691542289,\n\ \ \"acc_norm_stderr\": 0.03379790611796777\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.42168674698795183,\n\ \ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n\ \ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7134502923976608,\n \"acc_stderr\": 0.03467826685703826,\n\ \ \"acc_norm\": 0.7134502923976608,\n \"acc_norm_stderr\": 0.03467826685703826\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2998776009791922,\n\ \ \"mc1_stderr\": 0.01604035296671362,\n \"mc2\": 0.45697851910783077,\n\ \ \"mc2_stderr\": 0.015427158150833389\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.739542225730071,\n \"acc_stderr\": 0.012334833671998297\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.18877937831690675,\n \ \ \"acc_stderr\": 0.010779262837202751\n }\n}\n```" repo_url: https://huggingface.co/FPHam/Sydney_Overthinker_13b_HF leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|arc:challenge|25_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-08T02-51-52.068469.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|gsm8k|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hellaswag|10_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-08T02-51-52.068469.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-management|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-08T02-51-52.068469.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|truthfulqa:mc|0_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-08T02-51-52.068469.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_08T02_51_52.068469 path: - '**/details_harness|winogrande|5_2023-12-08T02-51-52.068469.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-08T02-51-52.068469.parquet' - config_name: results data_files: - split: 2023_12_08T02_51_52.068469 path: - results_2023-12-08T02-51-52.068469.parquet - split: latest path: - results_2023-12-08T02-51-52.068469.parquet --- # Dataset Card for Evaluation run of FPHam/Sydney_Overthinker_13b_HF ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/FPHam/Sydney_Overthinker_13b_HF - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [FPHam/Sydney_Overthinker_13b_HF](https://huggingface.co/FPHam/Sydney_Overthinker_13b_HF) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-08T02:51:52.068469](https://huggingface.co/datasets/open-llm-leaderboard/details_FPHam__Sydney_Overthinker_13b_HF/blob/main/results_2023-12-08T02-51-52.068469.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5136287950246363, "acc_stderr": 0.034087739996992605, "acc_norm": 0.5191163704808761, "acc_norm_stderr": 0.03483348555557295, "mc1": 0.2998776009791922, "mc1_stderr": 0.01604035296671362, "mc2": 0.45697851910783077, "mc2_stderr": 0.015427158150833389 }, "harness|arc:challenge|25": { "acc": 0.5614334470989761, "acc_stderr": 0.014500682618212865, "acc_norm": 0.5895904436860068, "acc_norm_stderr": 0.014374922192642664 }, "harness|hellaswag|10": { "acc": 0.6118303126867158, "acc_stderr": 0.004863375698153865, "acc_norm": 0.8085042820155347, "acc_norm_stderr": 0.0039267405951797715 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4888888888888889, "acc_stderr": 0.04318275491977976, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.04318275491977976 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.47368421052631576, "acc_stderr": 0.04063302731486671, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.04063302731486671 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5132075471698113, "acc_stderr": 0.030762134874500476, "acc_norm": 0.5132075471698113, "acc_norm_stderr": 0.030762134874500476 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5972222222222222, "acc_stderr": 0.04101405519842425, "acc_norm": 0.5972222222222222, "acc_norm_stderr": 0.04101405519842425 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4797687861271676, "acc_stderr": 0.03809342081273957, "acc_norm": 0.4797687861271676, "acc_norm_stderr": 0.03809342081273957 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2549019607843137, "acc_stderr": 0.0433643270799318, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.0433643270799318 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4553191489361702, "acc_stderr": 0.03255525359340355, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.03255525359340355 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2631578947368421, "acc_stderr": 0.041424397194893624, "acc_norm": 0.2631578947368421, "acc_norm_stderr": 0.041424397194893624 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.023517294335963286, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.023517294335963286 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.35714285714285715, "acc_stderr": 0.042857142857142816, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.042857142857142816 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5870967741935483, "acc_stderr": 0.028009138125400387, "acc_norm": 0.5870967741935483, "acc_norm_stderr": 0.028009138125400387 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3399014778325123, "acc_stderr": 0.033327690684107895, "acc_norm": 0.3399014778325123, "acc_norm_stderr": 0.033327690684107895 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6181818181818182, "acc_stderr": 0.03793713171165634, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.03793713171165634 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6616161616161617, "acc_stderr": 0.033711241426263014, "acc_norm": 0.6616161616161617, "acc_norm_stderr": 0.033711241426263014 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7409326424870466, "acc_stderr": 0.0316187791793541, "acc_norm": 0.7409326424870466, "acc_norm_stderr": 0.0316187791793541 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5230769230769231, "acc_stderr": 0.025323990861736232, "acc_norm": 0.5230769230769231, "acc_norm_stderr": 0.025323990861736232 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.28888888888888886, "acc_stderr": 0.027634907264178544, "acc_norm": 0.28888888888888886, "acc_norm_stderr": 0.027634907264178544 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.46218487394957986, "acc_stderr": 0.0323854694875898, "acc_norm": 0.46218487394957986, "acc_norm_stderr": 0.0323854694875898 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.25165562913907286, "acc_stderr": 0.035433042343899844, "acc_norm": 0.25165562913907286, "acc_norm_stderr": 0.035433042343899844 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6862385321100918, "acc_stderr": 0.019894723341469116, "acc_norm": 0.6862385321100918, "acc_norm_stderr": 0.019894723341469116 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.33796296296296297, "acc_stderr": 0.03225941352631295, "acc_norm": 0.33796296296296297, "acc_norm_stderr": 0.03225941352631295 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6323529411764706, "acc_stderr": 0.03384132045674118, "acc_norm": 0.6323529411764706, "acc_norm_stderr": 0.03384132045674118 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6708860759493671, "acc_stderr": 0.030587326294702368, "acc_norm": 0.6708860759493671, "acc_norm_stderr": 0.030587326294702368 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6412556053811659, "acc_stderr": 0.03219079200419995, "acc_norm": 0.6412556053811659, "acc_norm_stderr": 0.03219079200419995 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5954198473282443, "acc_stderr": 0.043046937953806645, "acc_norm": 0.5954198473282443, "acc_norm_stderr": 0.043046937953806645 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7107438016528925, "acc_stderr": 0.04139112727635463, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.04139112727635463 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497751, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497751 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5705521472392638, "acc_stderr": 0.03889066619112722, "acc_norm": 0.5705521472392638, "acc_norm_stderr": 0.03889066619112722 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness|hendrycksTest-management|5": { "acc": 0.6796116504854369, "acc_stderr": 0.04620284082280041, "acc_norm": 0.6796116504854369, "acc_norm_stderr": 0.04620284082280041 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7948717948717948, "acc_stderr": 0.026453508054040335, "acc_norm": 0.7948717948717948, "acc_norm_stderr": 0.026453508054040335 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.56, "acc_stderr": 0.0498887651569859, "acc_norm": 0.56, "acc_norm_stderr": 0.0498887651569859 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7203065134099617, "acc_stderr": 0.016050792148036522, "acc_norm": 0.7203065134099617, "acc_norm_stderr": 0.016050792148036522 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5809248554913294, "acc_stderr": 0.026564178111422622, "acc_norm": 0.5809248554913294, "acc_norm_stderr": 0.026564178111422622 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.32737430167597764, "acc_stderr": 0.015694238967737383, "acc_norm": 0.32737430167597764, "acc_norm_stderr": 0.015694238967737383 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5490196078431373, "acc_stderr": 0.028491993586171566, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.028491993586171566 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6045016077170418, "acc_stderr": 0.02777091853142784, "acc_norm": 0.6045016077170418, "acc_norm_stderr": 0.02777091853142784 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6080246913580247, "acc_stderr": 0.027163686038271146, "acc_norm": 0.6080246913580247, "acc_norm_stderr": 0.027163686038271146 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3723404255319149, "acc_stderr": 0.02883892147125146, "acc_norm": 0.3723404255319149, "acc_norm_stderr": 0.02883892147125146 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3963494132985658, "acc_stderr": 0.012492830452095217, "acc_norm": 0.3963494132985658, "acc_norm_stderr": 0.012492830452095217 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4411764705882353, "acc_stderr": 0.03016191193076711, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.03016191193076711 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5163398692810458, "acc_stderr": 0.02021703065318646, "acc_norm": 0.5163398692810458, "acc_norm_stderr": 0.02021703065318646 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6090909090909091, "acc_stderr": 0.04673752333670239, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.04673752333670239 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6081632653061224, "acc_stderr": 0.031251275910891656, "acc_norm": 0.6081632653061224, "acc_norm_stderr": 0.031251275910891656 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6467661691542289, "acc_stderr": 0.03379790611796777, "acc_norm": 0.6467661691542289, "acc_norm_stderr": 0.03379790611796777 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7134502923976608, "acc_stderr": 0.03467826685703826, "acc_norm": 0.7134502923976608, "acc_norm_stderr": 0.03467826685703826 }, "harness|truthfulqa:mc|0": { "mc1": 0.2998776009791922, "mc1_stderr": 0.01604035296671362, "mc2": 0.45697851910783077, "mc2_stderr": 0.015427158150833389 }, "harness|winogrande|5": { "acc": 0.739542225730071, "acc_stderr": 0.012334833671998297 }, "harness|gsm8k|5": { "acc": 0.18877937831690675, "acc_stderr": 0.010779262837202751 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

abwqr/michael_scott

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 79558.0 num_examples: 12 download_size: 0 dataset_size: 79558.0 --- # Dataset Card for "michael_scott" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

shahidul034/text_summarization_dataset6

--- dataset_info: features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 129713026 num_examples: 113562 download_size: 44838616 dataset_size: 129713026 --- # Dataset Card for "text_summarization_dataset6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

parksimon0808/prm800k-llama-generator

--- dataset_info: features: - name: texts dtype: string - name: input_ids sequence: int32 - name: labels sequence: int64 - name: answers dtype: string splits: - name: train num_bytes: 107264878 num_examples: 16465 - name: test num_bytes: 4635493 num_examples: 773 download_size: 23666282 dataset_size: 111900371 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "prm800k-llama-v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MMShmBlogs/hmblogs-v3

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 45957987986 num_examples: 16896817 download_size: 21302352262 dataset_size: 45957987986 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_pmking27__PrathameshLLM-2B

--- pretty_name: Evaluation run of pmking27/PrathameshLLM-2B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [pmking27/PrathameshLLM-2B](https://huggingface.co/pmking27/PrathameshLLM-2B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_pmking27__PrathameshLLM-2B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-07T15:12:15.779927](https://huggingface.co/datasets/open-llm-leaderboard/details_pmking27__PrathameshLLM-2B/blob/main/results_2024-04-07T15-12-15.779927.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.38448642166678587,\n\ \ \"acc_stderr\": 0.03412711045658374,\n \"acc_norm\": 0.3882749361001093,\n\ \ \"acc_norm_stderr\": 0.03491709236659397,\n \"mc1\": 0.2913096695226438,\n\ \ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.44689893094855493,\n\ \ \"mc2_stderr\": 0.014502506509597363\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4257679180887372,\n \"acc_stderr\": 0.014449464278868809,\n\ \ \"acc_norm\": 0.447098976109215,\n \"acc_norm_stderr\": 0.014529380160526845\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5071698864767975,\n\ \ \"acc_stderr\": 0.00498926836296872,\n \"acc_norm\": 0.6840270862378013,\n\ \ \"acc_norm_stderr\": 0.004639520453444027\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4074074074074074,\n\ \ \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.4074074074074074,\n\ \ \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.375,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.32,\n\ \ \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \ \ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.4226415094339623,\n \"acc_stderr\": 0.03040233144576954,\n\ \ \"acc_norm\": 0.4226415094339623,\n \"acc_norm_stderr\": 0.03040233144576954\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4097222222222222,\n\ \ \"acc_stderr\": 0.04112490974670787,\n \"acc_norm\": 0.4097222222222222,\n\ \ \"acc_norm_stderr\": 0.04112490974670787\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.26,\n\ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.32947976878612717,\n\ \ \"acc_stderr\": 0.03583901754736411,\n \"acc_norm\": 0.32947976878612717,\n\ \ \"acc_norm_stderr\": 0.03583901754736411\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.039505818611799616,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.039505818611799616\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.37446808510638296,\n \"acc_stderr\": 0.03163910665367291,\n\ \ \"acc_norm\": 0.37446808510638296,\n \"acc_norm_stderr\": 0.03163910665367291\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\ \ \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n\ \ \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708617,\n \"\ acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708617\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4290322580645161,\n\ \ \"acc_stderr\": 0.02815603653823321,\n \"acc_norm\": 0.4290322580645161,\n\ \ \"acc_norm_stderr\": 0.02815603653823321\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.33497536945812806,\n \"acc_stderr\": 0.033208527423483104,\n\ \ \"acc_norm\": 0.33497536945812806,\n \"acc_norm_stderr\": 0.033208527423483104\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\"\ : 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.3696969696969697,\n \"acc_stderr\": 0.03769430314512568,\n\ \ \"acc_norm\": 0.3696969696969697,\n \"acc_norm_stderr\": 0.03769430314512568\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.46464646464646464,\n \"acc_stderr\": 0.03553436368828063,\n \"\ acc_norm\": 0.46464646464646464,\n \"acc_norm_stderr\": 0.03553436368828063\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.5077720207253886,\n \"acc_stderr\": 0.03608003225569654,\n\ \ \"acc_norm\": 0.5077720207253886,\n \"acc_norm_stderr\": 0.03608003225569654\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.38461538461538464,\n \"acc_stderr\": 0.024666744915187222,\n\ \ \"acc_norm\": 0.38461538461538464,\n \"acc_norm_stderr\": 0.024666744915187222\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2518518518518518,\n \"acc_stderr\": 0.02646611753895992,\n \ \ \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.02646611753895992\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.3949579831932773,\n \"acc_stderr\": 0.03175367846096626,\n \ \ \"acc_norm\": 0.3949579831932773,\n \"acc_norm_stderr\": 0.03175367846096626\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.43853211009174314,\n \"acc_stderr\": 0.021274713073954565,\n \"\ acc_norm\": 0.43853211009174314,\n \"acc_norm_stderr\": 0.021274713073954565\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.2962962962962963,\n \"acc_stderr\": 0.031141447823536037,\n \"\ acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.031141447823536037\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.37254901960784315,\n \"acc_stderr\": 0.03393388584958404,\n \"\ acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.03393388584958404\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.4050632911392405,\n \"acc_stderr\": 0.03195514741370673,\n \ \ \"acc_norm\": 0.4050632911392405,\n \"acc_norm_stderr\": 0.03195514741370673\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3991031390134529,\n\ \ \"acc_stderr\": 0.03286745312567961,\n \"acc_norm\": 0.3991031390134529,\n\ \ \"acc_norm_stderr\": 0.03286745312567961\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.42748091603053434,\n \"acc_stderr\": 0.04338920305792401,\n\ \ \"acc_norm\": 0.42748091603053434,\n \"acc_norm_stderr\": 0.04338920305792401\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.5454545454545454,\n \"acc_stderr\": 0.04545454545454546,\n \"\ acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.04545454545454546\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.37037037037037035,\n\ \ \"acc_stderr\": 0.04668408033024932,\n \"acc_norm\": 0.37037037037037035,\n\ \ \"acc_norm_stderr\": 0.04668408033024932\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.3619631901840491,\n \"acc_stderr\": 0.037757007291414416,\n\ \ \"acc_norm\": 0.3619631901840491,\n \"acc_norm_stderr\": 0.037757007291414416\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\ \ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\ \ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.4854368932038835,\n \"acc_stderr\": 0.049486373240266376,\n\ \ \"acc_norm\": 0.4854368932038835,\n \"acc_norm_stderr\": 0.049486373240266376\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.5726495726495726,\n\ \ \"acc_stderr\": 0.032408473935163266,\n \"acc_norm\": 0.5726495726495726,\n\ \ \"acc_norm_stderr\": 0.032408473935163266\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237101,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237101\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5159642401021711,\n\ \ \"acc_stderr\": 0.01787084750608173,\n \"acc_norm\": 0.5159642401021711,\n\ \ \"acc_norm_stderr\": 0.01787084750608173\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.3786127167630058,\n \"acc_stderr\": 0.02611374936131034,\n\ \ \"acc_norm\": 0.3786127167630058,\n \"acc_norm_stderr\": 0.02611374936131034\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25139664804469275,\n\ \ \"acc_stderr\": 0.014508979453553988,\n \"acc_norm\": 0.25139664804469275,\n\ \ \"acc_norm_stderr\": 0.014508979453553988\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.48366013071895425,\n \"acc_stderr\": 0.028614624752805407,\n\ \ \"acc_norm\": 0.48366013071895425,\n \"acc_norm_stderr\": 0.028614624752805407\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.41479099678456594,\n\ \ \"acc_stderr\": 0.027982680459759553,\n \"acc_norm\": 0.41479099678456594,\n\ \ \"acc_norm_stderr\": 0.027982680459759553\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.4382716049382716,\n \"acc_stderr\": 0.027607914087400483,\n\ \ \"acc_norm\": 0.4382716049382716,\n \"acc_norm_stderr\": 0.027607914087400483\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.28368794326241137,\n \"acc_stderr\": 0.026891709428343968,\n \ \ \"acc_norm\": 0.28368794326241137,\n \"acc_norm_stderr\": 0.026891709428343968\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3116036505867014,\n\ \ \"acc_stderr\": 0.01182903918284965,\n \"acc_norm\": 0.3116036505867014,\n\ \ \"acc_norm_stderr\": 0.01182903918284965\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.29044117647058826,\n \"acc_stderr\": 0.027576468622740522,\n\ \ \"acc_norm\": 0.29044117647058826,\n \"acc_norm_stderr\": 0.027576468622740522\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.3545751633986928,\n \"acc_stderr\": 0.0193533605475537,\n \ \ \"acc_norm\": 0.3545751633986928,\n \"acc_norm_stderr\": 0.0193533605475537\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.4090909090909091,\n\ \ \"acc_stderr\": 0.047093069786618966,\n \"acc_norm\": 0.4090909090909091,\n\ \ \"acc_norm_stderr\": 0.047093069786618966\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.4122448979591837,\n \"acc_stderr\": 0.03151236044674281,\n\ \ \"acc_norm\": 0.4122448979591837,\n \"acc_norm_stderr\": 0.03151236044674281\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.472636815920398,\n\ \ \"acc_stderr\": 0.035302355173346824,\n \"acc_norm\": 0.472636815920398,\n\ \ \"acc_norm_stderr\": 0.035302355173346824\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3855421686746988,\n\ \ \"acc_stderr\": 0.037891344246115496,\n \"acc_norm\": 0.3855421686746988,\n\ \ \"acc_norm_stderr\": 0.037891344246115496\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.03834234744164993,\n\ \ \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.03834234744164993\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2913096695226438,\n\ \ \"mc1_stderr\": 0.01590598704818483,\n \"mc2\": 0.44689893094855493,\n\ \ \"mc2_stderr\": 0.014502506509597363\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6511444356748224,\n \"acc_stderr\": 0.01339505932013732\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09249431387414708,\n \ \ \"acc_stderr\": 0.007980396874560178\n }\n}\n```" repo_url: https://huggingface.co/pmking27/PrathameshLLM-2B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|arc:challenge|25_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-07T15-12-15.779927.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|gsm8k|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hellaswag|10_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T15-12-15.779927.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T15-12-15.779927.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T15-12-15.779927.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_07T15_12_15.779927 path: - '**/details_harness|winogrande|5_2024-04-07T15-12-15.779927.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-07T15-12-15.779927.parquet' - config_name: results data_files: - split: 2024_04_07T15_12_15.779927 path: - results_2024-04-07T15-12-15.779927.parquet - split: latest path: - results_2024-04-07T15-12-15.779927.parquet --- # Dataset Card for Evaluation run of pmking27/PrathameshLLM-2B  Dataset automatically created during the evaluation run of model [pmking27/PrathameshLLM-2B](https://huggingface.co/pmking27/PrathameshLLM-2B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_pmking27__PrathameshLLM-2B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-07T15:12:15.779927](https://huggingface.co/datasets/open-llm-leaderboard/details_pmking27__PrathameshLLM-2B/blob/main/results_2024-04-07T15-12-15.779927.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.38448642166678587, "acc_stderr": 0.03412711045658374, "acc_norm": 0.3882749361001093, "acc_norm_stderr": 0.03491709236659397, "mc1": 0.2913096695226438, "mc1_stderr": 0.01590598704818483, "mc2": 0.44689893094855493, "mc2_stderr": 0.014502506509597363 }, "harness|arc:challenge|25": { "acc": 0.4257679180887372, "acc_stderr": 0.014449464278868809, "acc_norm": 0.447098976109215, "acc_norm_stderr": 0.014529380160526845 }, "harness|hellaswag|10": { "acc": 0.5071698864767975, "acc_stderr": 0.00498926836296872, "acc_norm": 0.6840270862378013, "acc_norm_stderr": 0.004639520453444027 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4074074074074074, "acc_stderr": 0.04244633238353228, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.04244633238353228 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.375, "acc_stderr": 0.039397364351956274, "acc_norm": 0.375, "acc_norm_stderr": 0.039397364351956274 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4226415094339623, "acc_stderr": 0.03040233144576954, "acc_norm": 0.4226415094339623, "acc_norm_stderr": 0.03040233144576954 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4097222222222222, "acc_stderr": 0.04112490974670787, "acc_norm": 0.4097222222222222, "acc_norm_stderr": 0.04112490974670787 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.26, "acc_stderr": 0.04408440022768078, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.32947976878612717, "acc_stderr": 0.03583901754736411, "acc_norm": 0.32947976878612717, "acc_norm_stderr": 0.03583901754736411 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.039505818611799616, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.039505818611799616 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.37446808510638296, "acc_stderr": 0.03163910665367291, "acc_norm": 0.37446808510638296, "acc_norm_stderr": 0.03163910665367291 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159394, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159394 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.45517241379310347, "acc_stderr": 0.04149886942192117, "acc_norm": 0.45517241379310347, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.26455026455026454, "acc_stderr": 0.022717467897708617, "acc_norm": 0.26455026455026454, "acc_norm_stderr": 0.022717467897708617 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.4290322580645161, "acc_stderr": 0.02815603653823321, "acc_norm": 0.4290322580645161, "acc_norm_stderr": 0.02815603653823321 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.33497536945812806, "acc_stderr": 0.033208527423483104, "acc_norm": 0.33497536945812806, "acc_norm_stderr": 0.033208527423483104 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.3696969696969697, "acc_stderr": 0.03769430314512568, "acc_norm": 0.3696969696969697, "acc_norm_stderr": 0.03769430314512568 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.46464646464646464, "acc_stderr": 0.03553436368828063, "acc_norm": 0.46464646464646464, "acc_norm_stderr": 0.03553436368828063 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.5077720207253886, "acc_stderr": 0.03608003225569654, "acc_norm": 0.5077720207253886, "acc_norm_stderr": 0.03608003225569654 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.38461538461538464, "acc_stderr": 0.024666744915187222, "acc_norm": 0.38461538461538464, "acc_norm_stderr": 0.024666744915187222 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2518518518518518, "acc_stderr": 0.02646611753895992, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.02646611753895992 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.3949579831932773, "acc_stderr": 0.03175367846096626, "acc_norm": 0.3949579831932773, "acc_norm_stderr": 0.03175367846096626 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.43853211009174314, "acc_stderr": 0.021274713073954565, "acc_norm": 0.43853211009174314, "acc_norm_stderr": 0.021274713073954565 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.031141447823536037, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.031141447823536037 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.37254901960784315, "acc_stderr": 0.03393388584958404, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.03393388584958404 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.4050632911392405, "acc_stderr": 0.03195514741370673, "acc_norm": 0.4050632911392405, "acc_norm_stderr": 0.03195514741370673 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.3991031390134529, "acc_stderr": 0.03286745312567961, "acc_norm": 0.3991031390134529, "acc_norm_stderr": 0.03286745312567961 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.42748091603053434, "acc_stderr": 0.04338920305792401, "acc_norm": 0.42748091603053434, "acc_norm_stderr": 0.04338920305792401 }, "harness|hendrycksTest-international_law|5": { "acc": 0.5454545454545454, "acc_stderr": 0.04545454545454546, "acc_norm": 0.5454545454545454, "acc_norm_stderr": 0.04545454545454546 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.37037037037037035, "acc_stderr": 0.04668408033024932, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.04668408033024932 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.3619631901840491, "acc_stderr": 0.037757007291414416, "acc_norm": 0.3619631901840491, "acc_norm_stderr": 0.037757007291414416 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.38392857142857145, "acc_stderr": 0.04616143075028547, "acc_norm": 0.38392857142857145, "acc_norm_stderr": 0.04616143075028547 }, "harness|hendrycksTest-management|5": { "acc": 0.4854368932038835, "acc_stderr": 0.049486373240266376, "acc_norm": 0.4854368932038835, "acc_norm_stderr": 0.049486373240266376 }, "harness|hendrycksTest-marketing|5": { "acc": 0.5726495726495726, "acc_stderr": 0.032408473935163266, "acc_norm": 0.5726495726495726, "acc_norm_stderr": 0.032408473935163266 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.41, "acc_stderr": 0.04943110704237101, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237101 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5159642401021711, "acc_stderr": 0.01787084750608173, "acc_norm": 0.5159642401021711, "acc_norm_stderr": 0.01787084750608173 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.3786127167630058, "acc_stderr": 0.02611374936131034, "acc_norm": 0.3786127167630058, "acc_norm_stderr": 0.02611374936131034 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.25139664804469275, "acc_stderr": 0.014508979453553988, "acc_norm": 0.25139664804469275, "acc_norm_stderr": 0.014508979453553988 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.48366013071895425, "acc_stderr": 0.028614624752805407, "acc_norm": 0.48366013071895425, "acc_norm_stderr": 0.028614624752805407 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.41479099678456594, "acc_stderr": 0.027982680459759553, "acc_norm": 0.41479099678456594, "acc_norm_stderr": 0.027982680459759553 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4382716049382716, "acc_stderr": 0.027607914087400483, "acc_norm": 0.4382716049382716, "acc_norm_stderr": 0.027607914087400483 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.28368794326241137, "acc_stderr": 0.026891709428343968, "acc_norm": 0.28368794326241137, "acc_norm_stderr": 0.026891709428343968 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3116036505867014, "acc_stderr": 0.01182903918284965, "acc_norm": 0.3116036505867014, "acc_norm_stderr": 0.01182903918284965 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.29044117647058826, "acc_stderr": 0.027576468622740522, "acc_norm": 0.29044117647058826, "acc_norm_stderr": 0.027576468622740522 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.3545751633986928, "acc_stderr": 0.0193533605475537, "acc_norm": 0.3545751633986928, "acc_norm_stderr": 0.0193533605475537 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.4090909090909091, "acc_stderr": 0.047093069786618966, "acc_norm": 0.4090909090909091, "acc_norm_stderr": 0.047093069786618966 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4122448979591837, "acc_stderr": 0.03151236044674281, "acc_norm": 0.4122448979591837, "acc_norm_stderr": 0.03151236044674281 }, "harness|hendrycksTest-sociology|5": { "acc": 0.472636815920398, "acc_stderr": 0.035302355173346824, "acc_norm": 0.472636815920398, "acc_norm_stderr": 0.035302355173346824 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-virology|5": { "acc": 0.3855421686746988, "acc_stderr": 0.037891344246115496, "acc_norm": 0.3855421686746988, "acc_norm_stderr": 0.037891344246115496 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.49122807017543857, "acc_stderr": 0.03834234744164993, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.03834234744164993 }, "harness|truthfulqa:mc|0": { "mc1": 0.2913096695226438, "mc1_stderr": 0.01590598704818483, "mc2": 0.44689893094855493, "mc2_stderr": 0.014502506509597363 }, "harness|winogrande|5": { "acc": 0.6511444356748224, "acc_stderr": 0.01339505932013732 }, "harness|gsm8k|5": { "acc": 0.09249431387414708, "acc_stderr": 0.007980396874560178 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

axelprsvl/my_dataset

--- dataset_info: features: - name: audio dtype: audio splits: - name: train num_bytes: 40520175.0 num_examples: 5 download_size: 40474142 dataset_size: 40520175.0 --- # Dataset Card for "my_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tyzhu/wikitext-2-raw-v1-shuffled

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11018604 num_examples: 36718 - name: validation num_bytes: 1159288 num_examples: 3760 download_size: 7055354 dataset_size: 12177892 --- # Dataset Card for "wikitext-2-raw-v1-shuffled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ovior/twitter_dataset_1713165252

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2753460 num_examples: 7970 download_size: 1590780 dataset_size: 2753460 configs: - config_name: default data_files: - split: train path: data/train-* ---

guilhermebo/embeddings

--- license: creativeml-openrail-m ---

telugu_news

--- annotations_creators: - machine-generated language_creators: - other language: - te license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - language-modeling - masked-language-modeling - multi-class-classification - topic-classification pretty_name: TeluguNews dataset_info: features: - name: sno dtype: int32 - name: date dtype: string - name: heading dtype: string - name: body dtype: string - name: topic dtype: class_label: names: '0': business '1': editorial '2': entertainment '3': nation '4': sports splits: - name: train num_bytes: 69400234 num_examples: 17312 - name: test num_bytes: 17265514 num_examples: 4329 download_size: 0 dataset_size: 86665748 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.kaggle.com/sudalairajkumar/telugu-nlp?select=telugu_news - **Repository:** https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset ### Dataset Summary This dataset contains Telugu language news articles along with respective topic labels (business, editorial, entertainment, nation, sport) extracted from the daily Andhra Jyoti. This dataset could be used to build Classification and Language Models. ### Supported Tasks and Leaderboards Multiclass classification, Topic Classification, Language Model ### Languages TE - Telugu, India ## Dataset Structure ### Data Instances Two CSV files (train, test) with five columns (sno, date, heading, body, topic). ### Data Fields - sno: id - date: publish date of the news article - heading: article heading/title - body: article body/content - topic: one of the following topics (business, editorial, entertainment, nation, sport) ### Data Splits Train and Test ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data - https://www.kaggle.com/sudalairajkumar/telugu-nlp?select=telugu_news - https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset #### Initial Data Collection and Normalization The source data is scraped articles from archives of Telugu newspaper website Andhra Jyoti. A set of queries were created and the corresponding ground truth answers were retrieved by a combination of BM25 and tf-idf. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Sudalai Rajkumar, Anusha Motamarri ### Licensing Information [More Information Needed] ### Citation Information ``` @InProceedings{kaggle:dataset, title = {Telugu News - Natural Language Processing for Indian Languages}, authors={Sudalai Rajkumar, Anusha Motamarri}, year={2019} } ``` ### Contributions Thanks to [@oostopitre](https://github.com/oostopitre) for adding this dataset.

income/trec-covid-top-20-gen-queries

--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval --- # NFCorpus: 20 generated queries (BEIR Benchmark) This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset. - DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1) - id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`). - Questions generated: 20 - Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py) Below contains the old dataset card for the BEIR benchmark. # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train`` ``dev`` ``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train`` ``dev`` ``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train`` ``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train`` ``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train`` ``dev`` ``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train`` ``dev`` ``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev`` ``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev`` ``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train`` ``dev`` ``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train`` ``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his massâ€“energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German WeiÃŸbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train`` ``dev`` ``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train`` ``dev`` ``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train`` ``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train`` ``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train`` ``dev`` ``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train`` ``dev`` ``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev`` ``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev`` ``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train`` ``dev`` ``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train`` ``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca

--- pretty_name: Evaluation run of chargoddard/mistral-11b-slimorca dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chargoddard/mistral-11b-slimorca](https://huggingface.co/chargoddard/mistral-11b-slimorca)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-08T07:32:00.985160](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca/blob/main/results_2024-01-08T07-32-00.985160.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6367892367382408,\n\ \ \"acc_stderr\": 0.032420458743968754,\n \"acc_norm\": 0.6401126581266797,\n\ \ \"acc_norm_stderr\": 0.03306588858456459,\n \"mc1\": 0.38310893512851896,\n\ \ \"mc1_stderr\": 0.017018461679389855,\n \"mc2\": 0.5466386336115909,\n\ \ \"mc2_stderr\": 0.015507674046261742\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6075085324232082,\n \"acc_stderr\": 0.01426963463567073,\n\ \ \"acc_norm\": 0.6424914675767918,\n \"acc_norm_stderr\": 0.014005494275916573\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6468830910177256,\n\ \ \"acc_stderr\": 0.004769618829196511,\n \"acc_norm\": 0.8380800637323242,\n\ \ \"acc_norm_stderr\": 0.0036762448867232586\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411021,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411021\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.0372424959581773,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.0372424959581773\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"\ acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\ \ \"acc_stderr\": 0.023904914311782658,\n \"acc_norm\": 0.7709677419354839,\n\ \ \"acc_norm_stderr\": 0.023904914311782658\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8232323232323232,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.8232323232323232,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676177,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676177\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.0230866350868414,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.0230866350868414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608311,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608311\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\ \ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3541899441340782,\n\ \ \"acc_stderr\": 0.015995644947299232,\n \"acc_norm\": 0.3541899441340782,\n\ \ \"acc_norm_stderr\": 0.015995644947299232\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.02526169121972948,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.02526169121972948\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n\ \ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n\ \ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n\ \ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4530638852672751,\n\ \ \"acc_stderr\": 0.012713845972358981,\n \"acc_norm\": 0.4530638852672751,\n\ \ \"acc_norm_stderr\": 0.012713845972358981\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6715686274509803,\n \"acc_stderr\": 0.01899970738316267,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.01899970738316267\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128445,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128445\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.38310893512851896,\n\ \ \"mc1_stderr\": 0.017018461679389855,\n \"mc2\": 0.5466386336115909,\n\ \ \"mc2_stderr\": 0.015507674046261742\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7797947908445146,\n \"acc_stderr\": 0.011646276755089688\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5238817285822593,\n \ \ \"acc_stderr\": 0.013756765835465755\n }\n}\n```" repo_url: https://huggingface.co/chargoddard/mistral-11b-slimorca leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|arc:challenge|25_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-08T07-32-00.985160.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|gsm8k|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hellaswag|10_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-08T07-32-00.985160.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-management|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-08T07-32-00.985160.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|truthfulqa:mc|0_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-08T07-32-00.985160.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_08T07_32_00.985160 path: - '**/details_harness|winogrande|5_2024-01-08T07-32-00.985160.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-08T07-32-00.985160.parquet' - config_name: results data_files: - split: 2024_01_08T07_32_00.985160 path: - results_2024-01-08T07-32-00.985160.parquet - split: latest path: - results_2024-01-08T07-32-00.985160.parquet --- # Dataset Card for Evaluation run of chargoddard/mistral-11b-slimorca  Dataset automatically created during the evaluation run of model [chargoddard/mistral-11b-slimorca](https://huggingface.co/chargoddard/mistral-11b-slimorca) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-08T07:32:00.985160](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__mistral-11b-slimorca/blob/main/results_2024-01-08T07-32-00.985160.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6367892367382408, "acc_stderr": 0.032420458743968754, "acc_norm": 0.6401126581266797, "acc_norm_stderr": 0.03306588858456459, "mc1": 0.38310893512851896, "mc1_stderr": 0.017018461679389855, "mc2": 0.5466386336115909, "mc2_stderr": 0.015507674046261742 }, "harness|arc:challenge|25": { "acc": 0.6075085324232082, "acc_stderr": 0.01426963463567073, "acc_norm": 0.6424914675767918, "acc_norm_stderr": 0.014005494275916573 }, "harness|hellaswag|10": { "acc": 0.6468830910177256, "acc_stderr": 0.004769618829196511, "acc_norm": 0.8380800637323242, "acc_norm_stderr": 0.0036762448867232586 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.04793724854411021, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411021 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.690566037735849, "acc_stderr": 0.028450154794118637, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118637 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6069364161849711, "acc_stderr": 0.0372424959581773, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.0372424959581773 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.02533120243894443, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.02533120243894443 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7709677419354839, "acc_stderr": 0.023904914311782658, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.023904914311782658 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7515151515151515, "acc_stderr": 0.033744026441394036, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.033744026441394036 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8232323232323232, "acc_stderr": 0.027178752639044915, "acc_norm": 0.8232323232323232, "acc_norm_stderr": 0.027178752639044915 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8860103626943006, "acc_stderr": 0.022935144053919443, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676177, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676177 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8547008547008547, "acc_stderr": 0.0230866350868414, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.0230866350868414 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608311, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608311 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7167630057803468, "acc_stderr": 0.024257901705323378, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.024257901705323378 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3541899441340782, "acc_stderr": 0.015995644947299232, "acc_norm": 0.3541899441340782, "acc_norm_stderr": 0.015995644947299232 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7352941176470589, "acc_stderr": 0.02526169121972948, "acc_norm": 0.7352941176470589, "acc_norm_stderr": 0.02526169121972948 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811025, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811025 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7345679012345679, "acc_stderr": 0.024569223600460845, "acc_norm": 0.7345679012345679, "acc_norm_stderr": 0.024569223600460845 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4530638852672751, "acc_stderr": 0.012713845972358981, "acc_norm": 0.4530638852672751, "acc_norm_stderr": 0.012713845972358981 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6715686274509803, "acc_stderr": 0.01899970738316267, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.01899970738316267 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128445, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128445 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness|truthfulqa:mc|0": { "mc1": 0.38310893512851896, "mc1_stderr": 0.017018461679389855, "mc2": 0.5466386336115909, "mc2_stderr": 0.015507674046261742 }, "harness|winogrande|5": { "acc": 0.7797947908445146, "acc_stderr": 0.011646276755089688 }, "harness|gsm8k|5": { "acc": 0.5238817285822593, "acc_stderr": 0.013756765835465755 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

CyberHarem/colorado_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of colorado/コロラド/科罗拉多 (Azur Lane) This is the dataset of colorado/コロラド/科罗拉多 (Azur Lane), containing 120 images and their tags. The core tags of this character are `long_hair, red_eyes, white_hair, breasts, hairband, hair_over_one_eye, large_breasts, bangs, red_hairband`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 120 | 153.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 120 | 82.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 302 | 181.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 120 | 135.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 302 | 265.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/colorado_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/colorado_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bare_shoulders, looking_at_viewer, solo, detached_sleeves, sideboob, sleeveless_dress, belt, coat | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, sideboob, sleeveless_dress, solo, thigh_boots, thighhighs, detached_sleeves, lace-up_boots, looking_at_viewer, bare_shoulders, coat, simple_background, white_background, belt | | 2 | 13 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, official_alternate_costume, solo, black_jacket, guitar_case, black_hairband, braid, white_sweater, scarf_over_mouth, white_scarf, looking_at_viewer, disposable_cup, holding_cup, open_clothes, black_thighhighs, simple_background, white_background, bird_on_head | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | looking_at_viewer | solo | detached_sleeves | sideboob | sleeveless_dress | belt | coat | thigh_boots | thighhighs | lace-up_boots | simple_background | white_background | official_alternate_costume | black_jacket | guitar_case | black_hairband | braid | white_sweater | scarf_over_mouth | white_scarf | disposable_cup | holding_cup | open_clothes | black_thighhighs | bird_on_head | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------------|:-------|:-------------------|:-----------|:-------------------|:-------|:-------|:--------------|:-------------|:----------------|:--------------------|:-------------------|:-----------------------------|:---------------|:--------------|:-----------------|:--------|:----------------|:-------------------|:--------------|:-----------------|:--------------|:---------------|:-------------------|:---------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 2 | 13 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

Reasat/kather-19_test

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ADI '1': BACK '2': DEB '3': LYM '4': MUC '5': MUS '6': NORM '7': STR '8': TUM splits: - name: train num_bytes: 1093018719.36 num_examples: 7180 download_size: 941913399 dataset_size: 1093018719.36 --- # Dataset Card for "kather-19_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MinusV25/arxiv-1000-sample

--- license: apache-2.0 ---

LucasMagnana/ARASAAC_CACE

--- dataset_info: features: - name: text dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 15417 num_examples: 803 download_size: 13416 dataset_size: 15417 configs: - config_name: default data_files: - split: train path: data/train-* ---

JaydeepPatel/gujarati-alpaca52K

--- license: cc-by-nc-4.0 ---

PlayerJ/iOneBot_Custom_llama2

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7527 num_examples: 19 download_size: 3528 dataset_size: 7527 configs: - config_name: default data_files: - split: train path: data/train-* ---

AnushaKulkarni/preferred_dataset

--- dataset_info: features: - name: prompt dtype: string - name: chosen struct: - name: rank dtype: int32 - name: response dtype: string - name: rejected struct: - name: rank dtype: int32 - name: response dtype: string splits: - name: train num_bytes: 33413 num_examples: 50 download_size: 29993 dataset_size: 33413 configs: - config_name: default data_files: - split: train path: data/train-* ---

mattlc/deepgui

--- dataset_info: features: - name: file_name dtype: image - name: caption dtype: string - name: conditioning_image dtype: image splits: - name: train num_bytes: 184059306.0 num_examples: 400 download_size: 85178148 dataset_size: 184059306.0 --- # Dataset Card for "deepgui_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DBQ/Loro.Piana.Product.prices.France

--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: France - Loro Piana - Product-level price list tags: - webscraping - ecommerce - Loro Piana - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: string - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 389433 num_examples: 1128 download_size: 131337 dataset_size: 389433 --- # Loro Piana web scraped data ## About the website In the European, Middle Eastern and African (EMEA) region, specifically France, **Loro Piana** operates within the **luxury fashion industry**. This sector is renowned for its fine craftsmanship, superior quality, and the high value attached to its products. France, in particular, has long been a global center of luxury fashion with its capital, Paris, often referred to as the "fashion capital of the world". The industry is continuously evolving, incorporating **Ecommerce** and online platforms into their business models to meet the changing demands and preferences of its upscale clientele. In this context, we observed a dataset consisting of **Ecommerce product-list page (PLP) data** on Loro Piana in France. ## Link to **dataset** [France - Loro Piana - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Loro%20Piana%20Product-prices%20France/r/recyuT3Im5gEtayt6)

RikoteMaster/isear_augmented

--- dataset_info: features: - name: Text_processed dtype: string - name: Emotion dtype: string - name: Augmented dtype: bool splits: - name: train num_bytes: 1130723 num_examples: 7499 - name: validation num_bytes: 198379 num_examples: 1324 - name: test num_bytes: 250735 num_examples: 1879 download_size: 923389 dataset_size: 1579837 --- # Dataset Card for "isear_augmented" Classical ISEAR dataset augmented using gpt3.5 prompt tunning. Example prompt: Hello, you are going to take care of the task of increasing data in text. The data format that I am going to pass you is going to be as follows. Sentence: this is a sample text PROHIBITED WORD: this is a sample PROHIBITED WORD 2. Sentence: this is a sample text PROHIBITED WORD: this is a sample PROHIBITED WORD. I can enter as many sentences as I want, you must respect the logic that I have marked. NOW YOUR TASK MUST BE TO REFORMULATE THE SENTENCES IN ORDER TO EXPRESS THE SENTIMENT OF THE PROHIBITED WORD BUT YOU CANT USE THE PROHIBITED WORD BECAUSE IS FORBIDEN. PROHIBITED WORD LIST = [anger, fear, love, sadness, guilt, joy, shame, Overwhelming, remorse] you cannot use none of this words in the reformulation process, also you cannot use words derivated from this words and you must not do aclarations about what the text is trying to transmit. The output of the reformulated sentences must be Reformulated sentence 1: LorenIpsum reformulated sentence 2: LorenIpsum reformulated. Remember, I can introduce more than two sentences so you must return the reformulation of each sentence to me. So, remember, you must get a sentence that prevails the sentiment called the sentence PROHIBITED WORD but it is more important that the word does not appear inside the reformulation. If the word appears within the reformulation we would be entering into an incorrect practice of data augmentation. Yann LeCunn is watching, your bosses are watching you too, you must do what I ask you to do and get me the best sentences possible, but remember, without using the PROHIBITED WORD in the reformulation.1. Sentece: Unexpected visit by a close friend, whom I hadn't seen for half a year. PROHIBITED WORD: joy 2. Sentece: I wandered by mistake into the safety zone of a shooting range, and was shot at. PROHIBITED WORD: fear 3. Sentece: Being treated unfairly. PROHIBITED WORD: anger 4. Sentece: Breaking up with a girl. PROHIBITED WORD: sadness 5. Sentece: Nothing. PROHIBITED WORD: disgust 6. Sentece: None. PROHIBITED WORD: shame 7. Sentece: Little contact with my father before he died. PROHIBITED WORD: guilt 8. Sentece: When I was accepted as a student at the college, not having thought it possible. PROHIBITED WORD: joy The results obtained were quite well but when gpt obtain a way to express a sentiment with concrete words he is going to repeat it the structure, so you must reestructure the prompt [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3

--- pretty_name: Evaluation run of cgato/Thespis-CurtainCall-7b-v0.3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cgato/Thespis-CurtainCall-7b-v0.3](https://huggingface.co/cgato/Thespis-CurtainCall-7b-v0.3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-14T19:10:45.068833](https://huggingface.co/datasets/open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3/blob/main/results_2024-03-14T19-10-45.068833.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6209088671220813,\n\ \ \"acc_stderr\": 0.032670238010700646,\n \"acc_norm\": 0.626293015631346,\n\ \ \"acc_norm_stderr\": 0.033333675763785604,\n \"mc1\": 0.35006119951040393,\n\ \ \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5095336916736214,\n\ \ \"mc2_stderr\": 0.015077729128899325\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6032423208191127,\n \"acc_stderr\": 0.014296513020180644,\n\ \ \"acc_norm\": 0.6424914675767918,\n \"acc_norm_stderr\": 0.014005494275916573\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6328420633339972,\n\ \ \"acc_stderr\": 0.004810449343572395,\n \"acc_norm\": 0.8293168691495718,\n\ \ \"acc_norm_stderr\": 0.003754629313275157\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246483,\n \"\ acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246483\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\ \ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n\ \ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7354838709677419,\n\ \ \"acc_stderr\": 0.02509189237885928,\n \"acc_norm\": 0.7354838709677419,\n\ \ \"acc_norm_stderr\": 0.02509189237885928\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.02912652283458682,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.02912652283458682\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153303,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153303\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.02432173848460235,\n \ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.02432173848460235\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815632,\n \ \ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815632\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059285,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059285\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\"\ : 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n\ \ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8146788990825689,\n\ \ \"acc_stderr\": 0.016659279700295827,\n \"acc_norm\": 0.8146788990825689,\n\ \ \"acc_norm_stderr\": 0.016659279700295827\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\ : {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n\ \ \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\ acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098823,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098823\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.03957835471980979,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.03957835471980979\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\ \ \"acc_stderr\": 0.022801382534597528,\n \"acc_norm\": 0.8589743589743589,\n\ \ \"acc_norm_stderr\": 0.022801382534597528\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.013890862162876166,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.013890862162876166\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\ \ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33854748603351953,\n\ \ \"acc_stderr\": 0.01582670009648135,\n \"acc_norm\": 0.33854748603351953,\n\ \ \"acc_norm_stderr\": 0.01582670009648135\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6895424836601307,\n \"acc_stderr\": 0.026493033225145898,\n\ \ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.026493033225145898\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\ \ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\ \ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495033,\n\ \ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495033\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4439374185136897,\n\ \ \"acc_stderr\": 0.012689708167787686,\n \"acc_norm\": 0.4439374185136897,\n\ \ \"acc_norm_stderr\": 0.012689708167787686\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.029408372932278746,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.029408372932278746\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6535947712418301,\n \"acc_stderr\": 0.01924978569171721,\n \ \ \"acc_norm\": 0.6535947712418301,\n \"acc_norm_stderr\": 0.01924978569171721\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291296,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291296\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35006119951040393,\n\ \ \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5095336916736214,\n\ \ \"mc2_stderr\": 0.015077729128899325\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090248\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3737680060652009,\n \ \ \"acc_stderr\": 0.013326342860737021\n }\n}\n```" repo_url: https://huggingface.co/cgato/Thespis-CurtainCall-7b-v0.3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|arc:challenge|25_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-14T19-10-45.068833.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|gsm8k|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hellaswag|10_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-14T19-10-45.068833.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-management|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T19-10-45.068833.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|truthfulqa:mc|0_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-14T19-10-45.068833.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_14T19_10_45.068833 path: - '**/details_harness|winogrande|5_2024-03-14T19-10-45.068833.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-14T19-10-45.068833.parquet' - config_name: results data_files: - split: 2024_03_14T19_10_45.068833 path: - results_2024-03-14T19-10-45.068833.parquet - split: latest path: - results_2024-03-14T19-10-45.068833.parquet --- # Dataset Card for Evaluation run of cgato/Thespis-CurtainCall-7b-v0.3  Dataset automatically created during the evaluation run of model [cgato/Thespis-CurtainCall-7b-v0.3](https://huggingface.co/cgato/Thespis-CurtainCall-7b-v0.3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-14T19:10:45.068833](https://huggingface.co/datasets/open-llm-leaderboard/details_cgato__Thespis-CurtainCall-7b-v0.3/blob/main/results_2024-03-14T19-10-45.068833.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6209088671220813, "acc_stderr": 0.032670238010700646, "acc_norm": 0.626293015631346, "acc_norm_stderr": 0.033333675763785604, "mc1": 0.35006119951040393, "mc1_stderr": 0.01669794942015103, "mc2": 0.5095336916736214, "mc2_stderr": 0.015077729128899325 }, "harness|arc:challenge|25": { "acc": 0.6032423208191127, "acc_stderr": 0.014296513020180644, "acc_norm": 0.6424914675767918, "acc_norm_stderr": 0.014005494275916573 }, "harness|hellaswag|10": { "acc": 0.6328420633339972, "acc_stderr": 0.004810449343572395, "acc_norm": 0.8293168691495718, "acc_norm_stderr": 0.003754629313275157 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201942, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201942 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3968253968253968, "acc_stderr": 0.025197101074246483, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.025197101074246483 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4126984126984127, "acc_stderr": 0.04403438954768176, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.04403438954768176 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7354838709677419, "acc_stderr": 0.02509189237885928, "acc_norm": 0.7354838709677419, "acc_norm_stderr": 0.02509189237885928 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.04793724854411019, "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.02912652283458682, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.02912652283458682 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.844559585492228, "acc_stderr": 0.026148483469153303, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.026148483469153303 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6410256410256411, "acc_stderr": 0.02432173848460235, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.02432173848460235 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815632, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815632 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6470588235294118, "acc_stderr": 0.031041941304059285, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.031041941304059285 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.304635761589404, "acc_stderr": 0.03757949922943343, "acc_norm": 0.304635761589404, "acc_norm_stderr": 0.03757949922943343 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8146788990825689, "acc_stderr": 0.016659279700295827, "acc_norm": 0.8146788990825689, "acc_norm_stderr": 0.016659279700295827 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538272, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538272 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7990196078431373, "acc_stderr": 0.028125972265654373, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.028125972265654373 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098823, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098823 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.03957835471980979, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.03957835471980979 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8589743589743589, "acc_stderr": 0.022801382534597528, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.022801382534597528 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8148148148148148, "acc_stderr": 0.013890862162876166, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.013890862162876166 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6994219653179191, "acc_stderr": 0.0246853168672578, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.0246853168672578 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.33854748603351953, "acc_stderr": 0.01582670009648135, "acc_norm": 0.33854748603351953, "acc_norm_stderr": 0.01582670009648135 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6895424836601307, "acc_stderr": 0.026493033225145898, "acc_norm": 0.6895424836601307, "acc_norm_stderr": 0.026493033225145898 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7041800643086816, "acc_stderr": 0.025922371788818763, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.025922371788818763 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7098765432098766, "acc_stderr": 0.025251173936495033, "acc_norm": 0.7098765432098766, "acc_norm_stderr": 0.025251173936495033 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4439374185136897, "acc_stderr": 0.012689708167787686, "acc_norm": 0.4439374185136897, "acc_norm_stderr": 0.012689708167787686 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.625, "acc_stderr": 0.029408372932278746, "acc_norm": 0.625, "acc_norm_stderr": 0.029408372932278746 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6535947712418301, "acc_stderr": 0.01924978569171721, "acc_norm": 0.6535947712418301, "acc_norm_stderr": 0.01924978569171721 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7183673469387755, "acc_stderr": 0.028795185574291296, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291296 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454125, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454125 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness|truthfulqa:mc|0": { "mc1": 0.35006119951040393, "mc1_stderr": 0.01669794942015103, "mc2": 0.5095336916736214, "mc2_stderr": 0.015077729128899325 }, "harness|winogrande|5": { "acc": 0.7861089187056038, "acc_stderr": 0.011524466954090248 }, "harness|gsm8k|5": { "acc": 0.3737680060652009, "acc_stderr": 0.013326342860737021 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

sirCamp/hotpot_as2_pairs_and_triplets

--- dataset_info: features: - name: texts sequence: string splits: - name: pairs num_bytes: 34545896 num_examples: 117828 - name: triplets num_bytes: 34545896 num_examples: 117828 download_size: 32977152 dataset_size: 69091792 --- # Dataset Card for "hotpot_as2_pairs_and_triplets" This dataset is a modified version of HotpotAS2 from [lucadiliello/hotpot_as2](https://huggingface.co/datasets/lucadiliello/hotpot_as2) designed to train/pre-train "passage-retrieval" models. The dataset is divided into two splits: + *pairs* split: Each instance in this split consists of a question-(positive)answer pair. + *triplets* split: Each instance in this split consists of a question-(positive)answer-(hard-negative)answer triplet. For more info check out the original HotpotQA dataset in this [repository](https://github.com/lucadiliello/answer-selection)

beratcmn/parse-gpt-tr

--- language: - tr license: apache-2.0 task_categories: - question-answering - text2text-generation pretty_name: Parse GPT TR dataset_info: features: - name: id dtype: string - name: model dtype: string - name: messages list: - name: index dtype: int64 - name: role dtype: string - name: text dtype: string splits: - name: train num_bytes: 31282 num_examples: 14 download_size: 21619 dataset_size: 31282 configs: - config_name: default data_files: - split: train path: data/train-* --- # ParseGPT Turkish This is a Turkish dataset gathered from various individuals via the [ChatGPT Conversation Parser by beratcmn](https://huggingface.co/spaces/beratcmn/chatgpt-conversation-parser). This dataset gets updated every time someone uses the ChatGPT Conversation Parser to parse a conversation. This dataset contains conversations from various individuals. The conversations are in Turkish and are in the form of a list of messages. Each message has an index, a role (either "user" or "assistant"), and the text of the message. # Contact If you have any questions, please contact beratcmn on [HuggingFace](https://huggingface.co/beratcmn). Website: [beratcimen.com](https://beratcimen.com/) Email: beratcmn@hotmail.com

sagot/lefff_morpho

--- license: lgpl-lr --- # Dataset Card for lefff morpho ## Dataset Description - **Homepage:** [http://almanach.inria.fr/software_and_resources/custom/Alexina-en.html](http://almanach.inria.fr/software_and_resources/custom/Alexina-en.html) - **Repository:** [https://gitlab.inria.fr/almanach/alexina/lefff](https://gitlab.inria.fr/almanach/alexina/lefff) - **Paper:** [http://www.lrec-conf.org/proceedings/lrec2010/pdf/701_Paper.pdf](http://www.lrec-conf.org/proceedings/lrec2010/pdf/701_Paper.pdf) - **Point of Contact:** [Benoît Sagot](benoit.sagot@inria.fr) ### Dataset Summary The Lefff, currently in its 3.5 version, is one of the main morphological and syntactic lexicons for French. This Hugging Face dataset provides an easy access to the extensional morphological information in the Lefff, i.e. to the 4-uples (form, lemma, category, morphosyntactic features) and to the amalgams (e.g. _aux_ = _à_ + _les_) it contains. Category and morphosyntactic features are provided both in the original Lefff format and following the UniMorph guidelines. ### Languages French ## Dataset Creation The main author of the resource is Benoît Sagot (Inria, France). Please refer to the main paper and other Lefff-related papers for details. ## Additional Information ### Licensing Information The dataset, as the whole Lefff, is distributed under the LGPL-LR licence. ### Citation Information The main paper regarding the Lefff can be found [here](https://aclanthology.org/L10-1487/). Here is the BibTeX entry for the paper: ``` @inproceedings{sagot:inria-00521242, TITLE = {{The Lefff, a freely available and large-coverage morphological and syntactic lexicon for French}}, AUTHOR = {Sagot, Beno{\^i}t}, URL = {https://hal.inria.fr/inria-00521242}, BOOKTITLE = {{7th international conference on Language Resources and Evaluation (LREC 2010)}}, ADDRESS = {Valletta, Malta}, YEAR = {2010}, MONTH = May, PDF = {https://hal.inria.fr/inria-00521242/file/lrec10lefff.pdf}, HAL_ID = {inria-00521242}, HAL_VERSION = {v1}, } ``` For specific parts of speech or other parts of the lexicon, please cite the corresponding papers whenever relevant.

hlillemark/flores200_eng_output_scaffolding_mt5

--- dataset_info: features: - name: id dtype: int32 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 10718199156 num_examples: 10240000 - name: val num_bytes: 3827042 num_examples: 5000 - name: test num_bytes: 7670994 num_examples: 10000 download_size: 4669652168 dataset_size: 10729697192 --- # Dataset Card for "flores200_eng_output_scaffolding_mt5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-us_foreign_policy

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: fewshot_context_neg dtype: string splits: - name: dev num_bytes: 4594 num_examples: 5 - name: test num_bytes: 407390 num_examples: 100 download_size: 70800 dataset_size: 411984 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-us_foreign_policy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fuyu-quant/ibl-regression-ver2-linear-pred

--- dataset_info: features: - name: prediction dtype: string - name: 'true' dtype: string - name: index dtype: int64 splits: - name: pred num_bytes: 96424 num_examples: 1000 download_size: 24459 dataset_size: 96424 configs: - config_name: default data_files: - split: pred path: data/pred-* ---

CyberHarem/shikinami_kantaicollection

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of shikinami/敷波/敷波 (Kantai Collection) This is the dataset of shikinami/敷波/敷波 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `brown_hair, ponytail, brown_eyes, short_hair, ribbon, hair_ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 398.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 266.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1057 | 529.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 367.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1057 | 700.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shikinami_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/shikinami_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, brown_sailor_collar, serafuku, solo, looking_at_viewer, blush, short_sleeves, brown_skirt, pleated_skirt, simple_background, upper_body, white_background, twitter_username | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_socks, brown_sailor_collar, brown_skirt, kneehighs, pleated_skirt, serafuku, simple_background, solo, white_background, looking_at_viewer, blush, short_sleeves, sitting, open_mouth | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, black_sailor_collar, black_skirt, black_socks, serafuku, solo, anchor_symbol, kneehighs, pleated_skirt, looking_at_viewer, white_background, short_sleeves, simple_background, wariza | | 3 | 9 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, black_socks, full_body, kneehighs, machinery, pleated_skirt, serafuku, simple_background, solo, smokestack, white_background, black_sailor_collar, black_skirt, looking_at_viewer, torpedo_launcher, adapted_turret, standing, cannon, rigging, short_sleeves, short_ponytail | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, black_socks, brown_sailor_collar, brown_skirt, grey_footwear, kneehighs, pleated_skirt, serafuku, solo, full_body, short_sleeves, anchor_symbol, shoes, blush, open_mouth, outdoors, smile, standing | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | anchor_symbol, black_sailor_collar, kneehighs, pleated_skirt, serafuku, short_sleeves, solo_focus, black_skirt, black_socks, 2girls, long_hair, standing | | 6 | 7 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, black_pantyhose, detached_collar, playboy_bunny, rabbit_ears, small_breasts, solo, strapless_leotard, fake_animal_ears, simple_background, wrist_cuffs, alternate_costume, looking_at_viewer, black_leotard, full_body, grey_background, red_bowtie, red_leotard, sitting, white_background | | 7 | 9 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, solo, cowboy_shot, looking_at_viewer, collarbone, black_one-piece_swimsuit, blue_one-piece_swimsuit, covered_navel, school_swimsuit, small_breasts, standing, blush, gradient_background | | 8 | 11 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, alternate_costume, obi, blush, solo, yukata, looking_at_viewer, upper_body, uchiwa, floral_print | | 9 | 9 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, white_apron, black_dress, enmaided, solo, blush, maid_apron, maid_headdress, simple_background, frilled_apron, looking_at_viewer, open_mouth, puffy_sleeves, short_sleeves, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | brown_sailor_collar | serafuku | solo | looking_at_viewer | blush | short_sleeves | brown_skirt | pleated_skirt | simple_background | upper_body | white_background | twitter_username | black_socks | kneehighs | sitting | open_mouth | black_sailor_collar | black_skirt | anchor_symbol | wariza | full_body | machinery | smokestack | torpedo_launcher | adapted_turret | standing | cannon | rigging | short_ponytail | grey_footwear | shoes | outdoors | smile | solo_focus | 2girls | long_hair | black_pantyhose | detached_collar | playboy_bunny | rabbit_ears | small_breasts | strapless_leotard | fake_animal_ears | wrist_cuffs | alternate_costume | black_leotard | grey_background | red_bowtie | red_leotard | cowboy_shot | collarbone | black_one-piece_swimsuit | blue_one-piece_swimsuit | covered_navel | school_swimsuit | gradient_background | obi | yukata | uchiwa | floral_print | white_apron | black_dress | enmaided | maid_apron | maid_headdress | frilled_apron | puffy_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------------|:-----------|:-------|:--------------------|:--------|:----------------|:--------------|:----------------|:--------------------|:-------------|:-------------------|:-------------------|:--------------|:------------|:----------|:-------------|:----------------------|:--------------|:----------------|:---------|:------------|:------------|:-------------|:-------------------|:-----------------|:-----------|:---------|:----------|:-----------------|:----------------|:--------|:-----------|:--------|:-------------|:---------|:------------|:------------------|:------------------|:----------------|:--------------|:----------------|:--------------------|:-------------------|:--------------|:--------------------|:----------------|:------------------|:-------------|:--------------|:--------------|:-------------|:---------------------------|:--------------------------|:----------------|:------------------|:----------------------|:------|:---------|:---------|:---------------|:--------------|:--------------|:-----------|:-------------|:-----------------|:----------------|:----------------| | 0 | 13 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | X | | X | | X | X | | X | | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 9 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | X | X | | X | | X | X | | X | | X | X | | | X | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | | X | X | X | X | | | | | X | X | | X | | | X | | X | | | | | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | | | X | | | | X | | X | | | | | X | X | | | X | X | X | | | | | | | X | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 7 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | X | X | | | | | X | | X | | | | X | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 7 | 9 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | 8 | 11 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | | | | | | | | | 9 | 9 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | X | X | X | X | | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X |

zuleo/karen-fukuhara

--- license: creativeml-openrail-m tags: - stable-diffusion - embedding - textual-inversion - text-to-image - image-to-image - art - artistic --- # Karen Fukuhara textual inversion This is an embedding of Karen Fukuhara. She plays several different amazing roles from acting to voice acting: (Kimiko Miyashiro, Glimmah, Kipo). ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid1.png) ## Embedding Usage Use the token ```kfukvf-1990``` ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid2.png) --- ## 🎶 Prompt Examples 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, dressed as a queen with glimmering jewelry, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful, dynamic lighting``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DDIM, CFG scale: 7, Seed: 2154893269, Size: 512x768, Model hash: d8691b4d16_ --- 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, sitting near a table having a drink, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 2899587651, Size: 512x768, Model hash: e3cda540bf_ --- 🧾 ```Perfectly-centered portrait-photograph of kfukvf-1990, wearing a dirty jacket near a busy city, lifelike, subsurface scattering, photorealism, 8k resolution, beautiful``` ⛔ Negative prompt: ```(bad_prompt_version2:0.8), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, ((duplicate)), ((morbid)), ((mutilated)), out of frame, (((mutation))), (((deformed))), ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), extra limbs, gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), (fused fingers), (((long neck)))``` _Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7, Seed: 3576359901, Size: 512x768, Model hash: 67abd65708_ ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid3.png) ![Detailed Samples](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/grid4.png) --- ## 🎴 text2img Sampler and Checkpoint grids: It's always great to get a visual of what's going on with sampler using different models with this embedding. See the examples below and tune them to your liking. [Sampling Grid](https://huggingface.co/datasets/zuleo/karen-fukuhara/resolve/main/images/sampler_ckpt_grid.png) --- ☕ If you enjoy this model, buy me a coffee [Buy a coffee](https://ko-fi.com/3eegames) ---

CaliD3f/garbagepailkids

--- license: openrail ---

Nadav/pixel_glue_cola

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 50427044.125 num_examples: 8551 - name: validation num_bytes: 6217509.625 num_examples: 1043 download_size: 46779514 dataset_size: 56644553.75 --- # Dataset Card for "pixel_glue_cola" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

maxidl/LeoLM-ArcChallenge_de-fixed

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices struct: - name: text sequence: string - name: label sequence: string - name: answerKey dtype: string - name: question_de dtype: string - name: choices_de struct: - name: label sequence: string - name: text sequence: string - name: translation_de dtype: string splits: - name: test num_bytes: 1170650 num_examples: 1172 - name: validation num_bytes: 301780 num_examples: 299 download_size: 807886 dataset_size: 1472430 configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* --- The [LeoLM/ArcChallenge_de](https://huggingface.co/datasets/LeoLM/ArcChallenge_de) dataset, but with label errors fixed. The fix applied: ``` import datasets as hfds ds = hfds.load_dataset("LeoLM/ArcChallenge_de") def check_label(row): choices_en = row["choices"] label_en = choices_en["label"] choices_de = row["choices_de"] label_de = choices_de["label"] if label_en != label_de: print(f"\nLabel mismatch:\n{row}\n{label_en}\n{label_de}") choices_de["label"] = label_en return {"choices": choices_en, "choices_de": choices_de} ds = ds.map(lambda row: check_label(row)) ds.push_to_hub("maxidl/LeoLM-ArcChallenge_de-fixed") ``` Output of running this: ``` Label mismatch: {'id': 'NYSEDREGENTS_2014_4_4', 'question': 'Which state of matter has no definite volume and no definite shape?', 'choices': {'text': ['gas', 'liquid', 'solid'], 'label': ['A', 'B', 'C']}, 'answerKey': 'A', 'question_de': 'Welcher Aggregatzustand hat kein bestimmtes Volumen und keine bestimmte Form?', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['Gas', 'Flüssigkeit', 'Feststoff']}, 'translation_de': '{"question": "Welcher Aggregatzustand hat kein bestimmtes Volumen und keine bestimmte Form?", "choices": ["Gas", "Flüssigkeit", "Feststoff"]}'} ['A', 'B', 'C'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'TIMSS_2003_8_pg29', 'question': 'Which of the following organs is NOT situated in the abdomen?', 'choices': {'text': ['liver', 'kidney', 'stomach', 'bladder', 'heart'], 'label': ['A', 'B', 'C', 'D', 'E']}, 'answerKey': 'E', 'question_de': 'Welches der folgenden Organe ist NICHT im Bauchraum?', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['Leber', 'Niere', 'Magen', 'Blase', 'Herz']}, 'translation_de': '{"question": "Welches der folgenden Organe ist NICHT im Bauchraum?", "choices": ["Leber", "Niere", "Magen", "Blase", "Herz"]}'} ['A', 'B', 'C', 'D', 'E'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_4_19', 'question': 'As kittens grow into cats, their body weight usually', 'choices': {'text': ['decreases', 'increases', 'remains the same'], 'label': ['A', 'B', 'C']}, 'answerKey': 'B', 'question_de': 'Wenn Kätzchen zu Katzen heranwachsen, nimmt ihr Körpergewicht normalerweise', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['ab', 'zu', 'bleibt gleich']}, 'translation_de': '{"question": "Wenn Kätzchen zu Katzen heranwachsen, nimmt ihr Körpergewicht normalerweise", "choices": ["ab", "zu", "bleibt gleich"]}'} ['A', 'B', 'C'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_8_12', 'question': "A main function of a plant's seed is to", 'choices': {'text': ['store food to be used during early development', 'attract pollen to be used during development', 'take in light energy to be used during photosynthesis', 'produce chlorophyll to be used during photosynthesis'], 'label': ['1', '2', '3', '4']}, 'answerKey': '1', 'question_de': 'Eine Hauptfunktion des Samens einer Pflanze ist es,', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['Nahrung zu speichern, die während der frühen Entwicklung verwendet wird', 'Pollen anzulocken, der während der Entwicklung verwendet wird', 'Lichtenergie aufzunehmen, die während der Photosynthese verwendet wird', 'Chlorophyll zu produzieren, das während der Photosynthese verwendet wird']}, 'translation_de': '{"question": "Eine Hauptfunktion des Samens einer Pflanze ist es,", "choices": ["Nahrung zu speichern, die während der frühen Entwicklung verwendet wird", "Pollen anzulocken, der während der Entwicklung verwendet wird", "Lichtenergie aufzunehmen, die während der Photosynthese verwendet wird", "Chlorophyll zu produzieren, das während der Photosynthese verwendet wird"]}'} ['1', '2', '3', '4'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_4_28', 'question': 'Large birds have been eating small animals in an area. If all of the large birds died from a disease, the number of small animals in the area would probably', 'choices': {'text': ['decrease', 'increase', 'remain the same'], 'label': ['A', 'B', 'C']}, 'answerKey': 'B', 'question_de': 'Große Vögel haben kleine Tiere in einem Gebiet gefressen. Wenn alle großen Vögel an einer Krankheit sterben würden, würde die Anzahl der kleinen Tiere in der Gegend wahrscheinlich', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['abnehmen', 'zunehmen', 'gleich bleiben']}, 'translation_de': '{"question": "Große Vögel haben kleine Tiere in einem Gebiet gefressen. Wenn alle großen Vögel an einer Krankheit sterben würden, würde die Anzahl der kleinen Tiere in der Gegend wahrscheinlich", "choices": ["abnehmen", "zunehmen", "gleich bleiben"]}'} ['A', 'B', 'C'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_8_38', 'question': 'A substance in the solid phase (state) of matter has', 'choices': {'text': ['a definite shape and a definite volume', 'a definite shape, but no definite volume', 'no definite shape, but a definite volume', 'no definite shape and no definite volume'], 'label': ['1', '2', '3', '4']}, 'answerKey': '1', 'question_de': 'Ein Stoff im festen Aggregatzustand hat', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['eine bestimmte Form und ein bestimmtes Volumen', 'eine bestimmte Form, aber kein bestimmtes Volumen', 'keine bestimmte Form, aber ein bestimmtes Volumen', 'weder eine bestimmte Form noch ein bestimmtes Volumen']}, 'translation_de': '{"question": "Ein Stoff im festen Aggregatzustand hat", "choices": ["eine bestimmte Form und ein bestimmtes Volumen", "eine bestimmte Form, aber kein bestimmtes Volumen", "keine bestimmte Form, aber ein bestimmtes Volumen", "weder eine bestimmte Form noch ein bestimmtes Volumen"]}'} ['1', '2', '3', '4'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_8_20', 'question': 'The equation below shows the products formed when a solution of silver nitrate (AgNO3) reacts with a solution of sodium chloride (NaCl). AgNO3 \x02+ NaCl (Reactants) -> NaNO3 \x02+ AgCl (Products) In this equation, the total mass of the reactants is', 'choices': {'text': ['greater than the total mass of the products', 'equal to the total mass of the products', 'equal to the mass of AgCl', 'less than the mass of AgCl'], 'label': ['1', '2', '3', '4']}, 'answerKey': '2', 'question_de': 'Die Gleichung unten zeigt die Produkte, die entstehen, wenn eine Lösung von Silbernitrat (AgNO3) mit einer Lösung von Natriumchlorid (NaCl) reagiert. AgNO3 \x02+ NaCl (Edukte) -> NaNO3 \x02+ AgCl (Produkte) In dieser Gleichung ist die Gesamtmasse der Edukte', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['größer als die Gesamtmasse der Produkte', 'gleich der Gesamtmasse der Produkte', 'gleich der Masse von AgCl', 'kleiner als die Masse von AgCl']}, 'translation_de': '{"question": "Die Gleichung unten zeigt die Produkte, die entstehen, wenn eine Lösung von Silbernitrat (AgNO3) mit einer Lösung von Natriumchlorid (NaCl) reagiert. AgNO3 \\u0002+ NaCl (Edukte) -> NaNO3 \\u0002+ AgCl (Produkte) In dieser Gleichung ist die Gesamtmasse der Edukte", "choices": ["größer als die Gesamtmasse der Produkte", "gleich der Gesamtmasse der Produkte", "gleich der Masse von AgCl", "kleiner als die Masse von AgCl"]}'} ['1', '2', '3', '4'] ['A', 'B', 'C', 'D'] Label mismatch: {'id': 'NYSEDREGENTS_2014_8_41', 'question': 'In a car accident, a seat belt helps prevent injuries by applying a force', 'choices': {'text': ['less than the force of the moving passenger', 'greater than the force of the moving car', "in the same direction as the car's motion", "in the opposite direction of the passenger's motion"], 'label': ['1', '2', '3', '4']}, 'answerKey': '4', 'question_de': 'Bei einem Autounfall hilft der Sicherheitsgurt Verletzungen zu vermeiden, indem er eine Kraft anwendet', 'choices_de': {'label': ['A', 'B', 'C', 'D'], 'text': ['die kleiner ist als die Kraft des sich bewegenden Passagiers', 'die größer ist als die Kraft des sich bewegenden Autos', 'in die gleiche Richtung wie die Bewegung des Autos', 'in entgegengesetzter Richtung zur Bewegung des Passagiers']}, 'translation_de': '{"question": "Bei einem Autounfall hilft der Sicherheitsgurt Verletzungen zu vermeiden, indem er eine Kraft anwendet", "choices": ["die kleiner ist als die Kraft des sich bewegenden Passagiers", "die größer ist als die Kraft des sich bewegenden Autos", "in die gleiche Richtung wie die Bewegung des Autos", "in entgegengesetzter Richtung zur Bewegung des Passagiers"]}'} ['1', '2', '3', '4'] ['A', 'B', 'C', 'D'] ```

Aditya000001/llamadataset

--- license: wtfpl ---

jtatman/CoT_reformatted_preprocessed_llama2

--- dataset_info: features: - name: text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 10782796585 num_examples: 1851975 - name: eval num_bytes: 180549 num_examples: 25 download_size: 1530950918 dataset_size: 10782977134 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---

trajanson/black-long-sleeve-jersey

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4220210.0 num_examples: 19 download_size: 4215502 dataset_size: 4220210.0 --- # Dataset Card for "black-long-sleeve-jersey" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Princess3/Court_data_1k

--- license: wtfpl language: - en --- Just some New Zealand court decisions data, probably needs some more cleaning. data ranges between 1975 to now and its all randomly selected 1158 cases

tr416/dataset_20231007_033121

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 762696.0 num_examples: 297 - name: test num_bytes: 7704.0 num_examples: 3 download_size: 74119 dataset_size: 770400.0 --- # Dataset Card for "dataset_20231007_033121" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kunishou/hh-rlhf-49k-ja

--- license: mit --- This dataset was created by automatically translating part of "Anthropic/hh-rlhf" into Japanese. This dataset is also included in "mosaicml/dolly_hhrlhf". The "ng_translation" flag indicates that the translation was not successful, and "1" means that the translation failed. Therefore, for data with "1", "instruction" and "instruction_en" contain the same text. 以下の通りに読み込むことで"ng_translation"が"1"（翻訳誤り）のものを除外して使用できます。 ``` pip install datasets ``` ``` from datasets import Dataset, load_dataset dataset = load_dataset("kunishou/hh-rlhf-49k-ja") dataset.set_format(type="pandas") df = dataset["train"][:] df = df[df["ng_translation"]!="1"].drop(["ng_translation", "index"], axis=1).reset_index() dataset = Dataset.from_pandas(df) dataset ``` hh-rlhf repository https://github.com/anthropics/hh-rlhf Anthropic/hh-rlhf https://huggingface.co/datasets/Anthropic/hh-rlhf mosaicml/dolly_hhrlhf https://huggingface.co/datasets/mosaicml/dolly_hhrlhf

Ranjit/displace_dev_data

--- dataset_info: features: - name: audio dtype: audio - name: label dtype: class_label: names: '0': test '1': train - name: language dtype: string splits: - name: train num_bytes: 1788567880.172 num_examples: 30569 - name: test num_bytes: 352004692.0 num_examples: 5560 download_size: 2274720816 dataset_size: 2140572572.172 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Csplk/THE.ASCII.ART.EMPORIUM

--- license: cc-by-nc-sa-4.0 task_categories: - text2text-generation - text-generation language: - en tags: - art pretty_name: THE.ASCII.ART.EMPORIUM source: https://ascii.mozz.us:7070 --- ``` https://asciiartist.com/sitefiles/respectartistscampaign.html (published by Laura Brown aka ldb) Respect ASCII Artists Campaign Most ASCII artists will tag their ASCII creation with their initials. This is not just about signing your art, it shows the original artist. If someone else colours the art, or modifies it in any other way, the artist initials need to be kept with it. Anyone modifying art can add their initials (usually something like ldb/ you) and a note about what they had done to the original art. The original artist initials are left on the art, the initials of the person who modified it are added after the original artist. If you can not find the original artist, or a link to them on a site or social media, include a link to the source where you found the art as something being better than nothing. This helps preserve and archive the art as well as giving the artist credit. This is what the Respect ASCII Artists Campaign is about. Give respect to the original artist and leave their initials on the work. Not all ASCII art is available freely. Don't assume it is there for anyone to take (copy and paste or screenshot). Ask to repost, modify, or otherwise use the ASCII art you find online. Don't sell art you do not own or did not fully create yourself. The ribbon above is from the original Respect ASCII Artists Campaign. Started April 1998, by truffle. ```

yuan-sf63/chenyu_mask_64

--- dataset_info: features: - name: feature dtype: string - name: target dtype: string splits: - name: train num_bytes: 10038428.1 num_examples: 91242 - name: validation num_bytes: 1115380.9 num_examples: 10138 download_size: 0 dataset_size: 11153809.0 --- # Dataset Card for "chenyu_mask_64" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jwigginton/news-sp500

--- dataset_info: features: - name: symbol dtype: string - name: body dtype: string - name: publisher dtype: string - name: publish_time dtype: timestamp[ns, tz=GMT] - name: title dtype: string - name: url dtype: string - name: uuid dtype: string splits: - name: train num_bytes: 12055461 num_examples: 2010 download_size: 5673552 dataset_size: 12055461 configs: - config_name: default data_files: - split: train path: data/train-* ---

metaeval/nli-veridicality-transitivity

--- license: cc task_categories: - text-classification language: - en task_ids: - natural-language-inference --- ```bib @inproceedings{yanaka-etal-2021-exploring, title = "Exploring Transitivity in Neural {NLI} Models through Veridicality", author = "Yanaka, Hitomi and Mineshima, Koji and Inui, Kentaro", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", year = "2021", pages = "920--934", } ```

prithviraj-maurya/safesign-combined-general-ift

--- dataset_info: features: - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 10716758457 num_examples: 2596500 download_size: 5594595999 dataset_size: 10716758457 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_google__recurrentgemma-2b

--- pretty_name: Evaluation run of google/recurrentgemma-2b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [google/recurrentgemma-2b](https://huggingface.co/google/recurrentgemma-2b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_google__recurrentgemma-2b-hf_private\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-09T03:06:15.036120](https://huggingface.co/datasets/open-llm-leaderboard/details_google__recurrentgemma-2b-hf_private/blob/main/results_2024-04-09T03-06-15.036120.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.3466998813893547,\n\ \ \"acc_stderr\": 0.033548531585255545,\n \"acc_norm\": 0.3467198572346389,\n\ \ \"acc_norm_stderr\": 0.034298588914231626,\n \"mc1\": 0.211750305997552,\n\ \ \"mc1_stderr\": 0.014302068353925609,\n \"mc2\": 0.3501381307826391,\n\ \ \"mc2_stderr\": 0.013501544365145366\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.27047781569965873,\n \"acc_stderr\": 0.012980954547659556,\n\ \ \"acc_norm\": 0.28924914675767915,\n \"acc_norm_stderr\": 0.013250012579393443\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.44064927305317664,\n\ \ \"acc_stderr\": 0.004954503606471611,\n \"acc_norm\": 0.569308902609042,\n\ \ \"acc_norm_stderr\": 0.004941609820763589\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3925925925925926,\n\ \ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.3925925925925926,\n\ \ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.3815789473684211,\n \"acc_stderr\": 0.03953173377749194,\n\ \ \"acc_norm\": 0.3815789473684211,\n \"acc_norm_stderr\": 0.03953173377749194\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.38113207547169814,\n \"acc_stderr\": 0.02989060968628663,\n\ \ \"acc_norm\": 0.38113207547169814,\n \"acc_norm_stderr\": 0.02989060968628663\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3472222222222222,\n\ \ \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.3472222222222222,\n\ \ \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n\ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.34104046242774566,\n\ \ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.34104046242774566,\n\ \ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n\ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.2978723404255319,\n \"acc_stderr\": 0.029896145682095462,\n\ \ \"acc_norm\": 0.2978723404255319,\n \"acc_norm_stderr\": 0.029896145682095462\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\ \ \"acc_stderr\": 0.04266339443159393,\n \"acc_norm\": 0.2894736842105263,\n\ \ \"acc_norm_stderr\": 0.04266339443159393\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.43448275862068964,\n \"acc_stderr\": 0.041307408795554966,\n\ \ \"acc_norm\": 0.43448275862068964,\n \"acc_norm_stderr\": 0.041307408795554966\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2698412698412698,\n \"acc_stderr\": 0.022860838309232072,\n \"\ acc_norm\": 0.2698412698412698,\n \"acc_norm_stderr\": 0.022860838309232072\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n\ \ \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n\ \ \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.3741935483870968,\n \"acc_stderr\": 0.027528904299845777,\n \"\ acc_norm\": 0.3741935483870968,\n \"acc_norm_stderr\": 0.027528904299845777\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.2857142857142857,\n \"acc_stderr\": 0.03178529710642749,\n \"\ acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.03178529710642749\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\"\ : 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.22424242424242424,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.22424242424242424,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.4797979797979798,\n \"acc_stderr\": 0.03559443565563919,\n \"\ acc_norm\": 0.4797979797979798,\n \"acc_norm_stderr\": 0.03559443565563919\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.41450777202072536,\n \"acc_stderr\": 0.03555300319557673,\n\ \ \"acc_norm\": 0.41450777202072536,\n \"acc_norm_stderr\": 0.03555300319557673\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.38461538461538464,\n \"acc_stderr\": 0.02466674491518722,\n\ \ \"acc_norm\": 0.38461538461538464,\n \"acc_norm_stderr\": 0.02466674491518722\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2222222222222222,\n \"acc_stderr\": 0.02534809746809783,\n \ \ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.02534809746809783\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136094,\n\ \ \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136094\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526732,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526732\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.3926605504587156,\n \"acc_stderr\": 0.020937505161201093,\n \"\ acc_norm\": 0.3926605504587156,\n \"acc_norm_stderr\": 0.020937505161201093\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.2962962962962963,\n \"acc_stderr\": 0.03114144782353603,\n \"\ acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.03114144782353603\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.24509803921568626,\n \"acc_stderr\": 0.030190282453501936,\n \"\ acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.030190282453501936\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460302,\n \ \ \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460302\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.32286995515695066,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.32286995515695066,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.40458015267175573,\n \"acc_stderr\": 0.043046937953806645,\n\ \ \"acc_norm\": 0.40458015267175573,\n \"acc_norm_stderr\": 0.043046937953806645\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.4214876033057851,\n \"acc_stderr\": 0.04507732278775094,\n \"\ acc_norm\": 0.4214876033057851,\n \"acc_norm_stderr\": 0.04507732278775094\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n\ \ \"acc_stderr\": 0.0443280405529152,\n \"acc_norm\": 0.32142857142857145,\n\ \ \"acc_norm_stderr\": 0.0443280405529152\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.3786407766990291,\n \"acc_stderr\": 0.04802694698258974,\n\ \ \"acc_norm\": 0.3786407766990291,\n \"acc_norm_stderr\": 0.04802694698258974\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.47435897435897434,\n\ \ \"acc_stderr\": 0.03271298896811159,\n \"acc_norm\": 0.47435897435897434,\n\ \ \"acc_norm_stderr\": 0.03271298896811159\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.4329501915708812,\n\ \ \"acc_stderr\": 0.017718469101513982,\n \"acc_norm\": 0.4329501915708812,\n\ \ \"acc_norm_stderr\": 0.017718469101513982\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.3583815028901734,\n \"acc_stderr\": 0.0258167567915842,\n\ \ \"acc_norm\": 0.3583815028901734,\n \"acc_norm_stderr\": 0.0258167567915842\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24022346368715083,\n\ \ \"acc_stderr\": 0.014288343803925302,\n \"acc_norm\": 0.24022346368715083,\n\ \ \"acc_norm_stderr\": 0.014288343803925302\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.027684181883302877,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.027684181883302877\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.40514469453376206,\n\ \ \"acc_stderr\": 0.027882383791325946,\n \"acc_norm\": 0.40514469453376206,\n\ \ \"acc_norm_stderr\": 0.027882383791325946\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.4104938271604938,\n \"acc_stderr\": 0.027371350925124768,\n\ \ \"acc_norm\": 0.4104938271604938,\n \"acc_norm_stderr\": 0.027371350925124768\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.25886524822695034,\n \"acc_stderr\": 0.026129572527180848,\n \ \ \"acc_norm\": 0.25886524822695034,\n \"acc_norm_stderr\": 0.026129572527180848\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.1801470588235294,\n \"acc_stderr\": 0.02334516361654485,\n\ \ \"acc_norm\": 0.1801470588235294,\n \"acc_norm_stderr\": 0.02334516361654485\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.31862745098039214,\n \"acc_stderr\": 0.018850084696468705,\n \ \ \"acc_norm\": 0.31862745098039214,\n \"acc_norm_stderr\": 0.018850084696468705\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.04389311454644286,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.04389311454644286\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.23673469387755103,\n \"acc_stderr\": 0.027212835884073153,\n\ \ \"acc_norm\": 0.23673469387755103,\n \"acc_norm_stderr\": 0.027212835884073153\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.48258706467661694,\n\ \ \"acc_stderr\": 0.03533389234739244,\n \"acc_norm\": 0.48258706467661694,\n\ \ \"acc_norm_stderr\": 0.03533389234739244\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3313253012048193,\n\ \ \"acc_stderr\": 0.03664314777288085,\n \"acc_norm\": 0.3313253012048193,\n\ \ \"acc_norm_stderr\": 0.03664314777288085\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.4619883040935672,\n \"acc_stderr\": 0.03823727092882307,\n\ \ \"acc_norm\": 0.4619883040935672,\n \"acc_norm_stderr\": 0.03823727092882307\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.211750305997552,\n\ \ \"mc1_stderr\": 0.014302068353925609,\n \"mc2\": 0.3501381307826391,\n\ \ \"mc2_stderr\": 0.013501544365145366\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6827150749802684,\n \"acc_stderr\": 0.01308059841133212\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15693707354056102,\n \ \ \"acc_stderr\": 0.010019246595616153\n }\n}\n```" repo_url: https://huggingface.co/google/recurrentgemma-2b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|arc:challenge|25_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|arc:challenge|25_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|arc:challenge|25_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-09T03-06-15.036120.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|gsm8k|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|gsm8k|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|gsm8k|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hellaswag|10_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hellaswag|10_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hellaswag|10_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-09T00-15-01.985047.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-09T00-53-48.304390.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-09T03-06-15.036120.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-management|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-management|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-management|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T03-06-15.036120.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|truthfulqa:mc|0_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|truthfulqa:mc|0_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|truthfulqa:mc|0_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-09T03-06-15.036120.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_09T00_15_01.985047 path: - '**/details_harness|winogrande|5_2024-04-09T00-15-01.985047.parquet' - split: 2024_04_09T00_53_48.304390 path: - '**/details_harness|winogrande|5_2024-04-09T00-53-48.304390.parquet' - split: 2024_04_09T03_06_15.036120 path: - '**/details_harness|winogrande|5_2024-04-09T03-06-15.036120.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-09T03-06-15.036120.parquet' - config_name: results data_files: - split: 2024_04_09T00_15_01.985047 path: - results_2024-04-09T00-15-01.985047.parquet - split: 2024_04_09T00_53_48.304390 path: - results_2024-04-09T00-53-48.304390.parquet - split: 2024_04_09T03_06_15.036120 path: - results_2024-04-09T03-06-15.036120.parquet - split: latest path: - results_2024-04-09T03-06-15.036120.parquet --- # Dataset Card for Evaluation run of google/recurrentgemma-2b  Dataset automatically created during the evaluation run of model [google/recurrentgemma-2b](https://huggingface.co/google/recurrentgemma-2b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_google__recurrentgemma-2b-hf_private", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-09T03:06:15.036120](https://huggingface.co/datasets/open-llm-leaderboard/details_google__recurrentgemma-2b-hf_private/blob/main/results_2024-04-09T03-06-15.036120.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.3466998813893547, "acc_stderr": 0.033548531585255545, "acc_norm": 0.3467198572346389, "acc_norm_stderr": 0.034298588914231626, "mc1": 0.211750305997552, "mc1_stderr": 0.014302068353925609, "mc2": 0.3501381307826391, "mc2_stderr": 0.013501544365145366 }, "harness|arc:challenge|25": { "acc": 0.27047781569965873, "acc_stderr": 0.012980954547659556, "acc_norm": 0.28924914675767915, "acc_norm_stderr": 0.013250012579393443 }, "harness|hellaswag|10": { "acc": 0.44064927305317664, "acc_stderr": 0.004954503606471611, "acc_norm": 0.569308902609042, "acc_norm_stderr": 0.004941609820763589 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3925925925925926, "acc_stderr": 0.04218506215368879, "acc_norm": 0.3925925925925926, "acc_norm_stderr": 0.04218506215368879 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.3815789473684211, "acc_stderr": 0.03953173377749194, "acc_norm": 0.3815789473684211, "acc_norm_stderr": 0.03953173377749194 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.38113207547169814, "acc_stderr": 0.02989060968628663, "acc_norm": 0.38113207547169814, "acc_norm_stderr": 0.02989060968628663 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.3472222222222222, "acc_stderr": 0.039812405437178615, "acc_norm": 0.3472222222222222, "acc_norm_stderr": 0.039812405437178615 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.34104046242774566, "acc_stderr": 0.036146654241808254, "acc_norm": 0.34104046242774566, "acc_norm_stderr": 0.036146654241808254 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364395, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364395 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2978723404255319, "acc_stderr": 0.029896145682095462, "acc_norm": 0.2978723404255319, "acc_norm_stderr": 0.029896145682095462 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159393, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159393 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.43448275862068964, "acc_stderr": 0.041307408795554966, "acc_norm": 0.43448275862068964, "acc_norm_stderr": 0.041307408795554966 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2698412698412698, "acc_stderr": 0.022860838309232072, "acc_norm": 0.2698412698412698, "acc_norm_stderr": 0.022860838309232072 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30952380952380953, "acc_stderr": 0.04134913018303316, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.04134913018303316 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3741935483870968, "acc_stderr": 0.027528904299845777, "acc_norm": 0.3741935483870968, "acc_norm_stderr": 0.027528904299845777 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2857142857142857, "acc_stderr": 0.03178529710642749, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.03178529710642749 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.22424242424242424, "acc_stderr": 0.03256866661681102, "acc_norm": 0.22424242424242424, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.4797979797979798, "acc_stderr": 0.03559443565563919, "acc_norm": 0.4797979797979798, "acc_norm_stderr": 0.03559443565563919 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.41450777202072536, "acc_stderr": 0.03555300319557673, "acc_norm": 0.41450777202072536, "acc_norm_stderr": 0.03555300319557673 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.38461538461538464, "acc_stderr": 0.02466674491518722, "acc_norm": 0.38461538461538464, "acc_norm_stderr": 0.02466674491518722 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2222222222222222, "acc_stderr": 0.02534809746809783, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.02534809746809783 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.28991596638655465, "acc_stderr": 0.029472485833136094, "acc_norm": 0.28991596638655465, "acc_norm_stderr": 0.029472485833136094 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526732, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526732 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.3926605504587156, "acc_stderr": 0.020937505161201093, "acc_norm": 0.3926605504587156, "acc_norm_stderr": 0.020937505161201093 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.03114144782353603, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.03114144782353603 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.24509803921568626, "acc_stderr": 0.030190282453501936, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.030190282453501936 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.25738396624472576, "acc_stderr": 0.028458820991460302, "acc_norm": 0.25738396624472576, "acc_norm_stderr": 0.028458820991460302 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.32286995515695066, "acc_stderr": 0.03138147637575499, "acc_norm": 0.32286995515695066, "acc_norm_stderr": 0.03138147637575499 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.40458015267175573, "acc_stderr": 0.043046937953806645, "acc_norm": 0.40458015267175573, "acc_norm_stderr": 0.043046937953806645 }, "harness|hendrycksTest-international_law|5": { "acc": 0.4214876033057851, "acc_stderr": 0.04507732278775094, "acc_norm": 0.4214876033057851, "acc_norm_stderr": 0.04507732278775094 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04557239513497751, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04557239513497751 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.25766871165644173, "acc_stderr": 0.03436150827846917, "acc_norm": 0.25766871165644173, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.32142857142857145, "acc_stderr": 0.0443280405529152, "acc_norm": 0.32142857142857145, "acc_norm_stderr": 0.0443280405529152 }, "harness|hendrycksTest-management|5": { "acc": 0.3786407766990291, "acc_stderr": 0.04802694698258974, "acc_norm": 0.3786407766990291, "acc_norm_stderr": 0.04802694698258974 }, "harness|hendrycksTest-marketing|5": { "acc": 0.47435897435897434, "acc_stderr": 0.03271298896811159, "acc_norm": 0.47435897435897434, "acc_norm_stderr": 0.03271298896811159 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.4329501915708812, "acc_stderr": 0.017718469101513982, "acc_norm": 0.4329501915708812, "acc_norm_stderr": 0.017718469101513982 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.3583815028901734, "acc_stderr": 0.0258167567915842, "acc_norm": 0.3583815028901734, "acc_norm_stderr": 0.0258167567915842 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24022346368715083, "acc_stderr": 0.014288343803925302, "acc_norm": 0.24022346368715083, "acc_norm_stderr": 0.014288343803925302 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.37254901960784315, "acc_stderr": 0.027684181883302877, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.027684181883302877 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.40514469453376206, "acc_stderr": 0.027882383791325946, "acc_norm": 0.40514469453376206, "acc_norm_stderr": 0.027882383791325946 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4104938271604938, "acc_stderr": 0.027371350925124768, "acc_norm": 0.4104938271604938, "acc_norm_stderr": 0.027371350925124768 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.25886524822695034, "acc_stderr": 0.026129572527180848, "acc_norm": 0.25886524822695034, "acc_norm_stderr": 0.026129572527180848 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.1801470588235294, "acc_stderr": 0.02334516361654485, "acc_norm": 0.1801470588235294, "acc_norm_stderr": 0.02334516361654485 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.31862745098039214, "acc_stderr": 0.018850084696468705, "acc_norm": 0.31862745098039214, "acc_norm_stderr": 0.018850084696468705 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.3, "acc_stderr": 0.04389311454644286, "acc_norm": 0.3, "acc_norm_stderr": 0.04389311454644286 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.23673469387755103, "acc_stderr": 0.027212835884073153, "acc_norm": 0.23673469387755103, "acc_norm_stderr": 0.027212835884073153 }, "harness|hendrycksTest-sociology|5": { "acc": 0.48258706467661694, "acc_stderr": 0.03533389234739244, "acc_norm": 0.48258706467661694, "acc_norm_stderr": 0.03533389234739244 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-virology|5": { "acc": 0.3313253012048193, "acc_stderr": 0.03664314777288085, "acc_norm": 0.3313253012048193, "acc_norm_stderr": 0.03664314777288085 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.4619883040935672, "acc_stderr": 0.03823727092882307, "acc_norm": 0.4619883040935672, "acc_norm_stderr": 0.03823727092882307 }, "harness|truthfulqa:mc|0": { "mc1": 0.211750305997552, "mc1_stderr": 0.014302068353925609, "mc2": 0.3501381307826391, "mc2_stderr": 0.013501544365145366 }, "harness|winogrande|5": { "acc": 0.6827150749802684, "acc_stderr": 0.01308059841133212 }, "harness|gsm8k|5": { "acc": 0.15693707354056102, "acc_stderr": 0.010019246595616153 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

mask-distilled-onesec-cv12-each-chunk-uniq/chunk_245

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 753290112.0 num_examples: 147936 download_size: 767852826 dataset_size: 753290112.0 --- # Dataset Card for "chunk_245" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-futin__guess-en_3-fcaae9-2012466610

--- type: predictions tags: - autotrain - evaluation datasets: - futin/guess eval_info: task: text_zero_shot_classification model: facebook/opt-66b metrics: [] dataset_name: futin/guess dataset_config: en_3 dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-66b * Dataset: futin/guess * Config: en_3 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

fleurylol/cfacatering

--- license: apache-2.0 ---

MITCriticalData/L7_Dataset_2008_2015

--- license: mit ---

lenmon666/lentest

--- license: apache-2.0 --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Markuin/Ia_vozes

--- license: unknown ---

open-llm-leaderboard/details_CausalLM__72B-preview

--- pretty_name: Evaluation run of CausalLM/72B-preview dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CausalLM/72B-preview](https://huggingface.co/CausalLM/72B-preview) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CausalLM__72B-preview\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-09T21:42:26.382618](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__72B-preview/blob/main/results_2023-12-09T21-42-26.382618.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7667362936260237,\n\ \ \"acc_stderr\": 0.027929321227362417,\n \"acc_norm\": 0.7704368351697709,\n\ \ \"acc_norm_stderr\": 0.028461947646281283,\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5257567284522894,\n\ \ \"mc2_stderr\": 0.014743557767765337\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.606655290102389,\n \"acc_stderr\": 0.014275101465693024,\n\ \ \"acc_norm\": 0.6518771331058021,\n \"acc_norm_stderr\": 0.013921008595179347\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6468830910177256,\n\ \ \"acc_stderr\": 0.004769618829196502,\n \"acc_norm\": 0.8323043218482374,\n\ \ \"acc_norm_stderr\": 0.0037283229688748914\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.9144736842105263,\n \"acc_stderr\": 0.02275867713088861,\n\ \ \"acc_norm\": 0.9144736842105263,\n \"acc_norm_stderr\": 0.02275867713088861\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8301886792452831,\n \"acc_stderr\": 0.023108393799841326,\n\ \ \"acc_norm\": 0.8301886792452831,\n \"acc_norm_stderr\": 0.023108393799841326\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8958333333333334,\n\ \ \"acc_stderr\": 0.025545239210256917,\n \"acc_norm\": 0.8958333333333334,\n\ \ \"acc_norm_stderr\": 0.025545239210256917\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n\ \ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7803468208092486,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.7803468208092486,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5392156862745098,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n\ \ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.026148818018424502,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.026148818018424502\n \ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5701754385964912,\n\ \ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.5701754385964912,\n\ \ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\ \ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.6798941798941799,\n\ \ \"acc_stderr\": 0.024026846392873506,\n \"acc_norm\": 0.6798941798941799,\n\ \ \"acc_norm_stderr\": 0.024026846392873506\n },\n \"harness|hendrycksTest-formal_logic|5\"\ : {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.04444444444444449,\n\ \ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.04444444444444449\n\ \ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.54,\n\ \ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_biology|5\"\ : {\n \"acc\": 0.8903225806451613,\n \"acc_stderr\": 0.017776778700485173,\n\ \ \"acc_norm\": 0.8903225806451613,\n \"acc_norm_stderr\": 0.017776778700485173\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.6600985221674877,\n \"acc_stderr\": 0.033327690684107895,\n \"\ acc_norm\": 0.6600985221674877,\n \"acc_norm_stderr\": 0.033327690684107895\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\"\ : 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.0270459488258654,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.0270459488258654\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9444444444444444,\n \"acc_stderr\": 0.0163199507007674,\n \"acc_norm\"\ : 0.9444444444444444,\n \"acc_norm_stderr\": 0.0163199507007674\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.9896373056994818,\n \"acc_stderr\": 0.007308424386792194,\n\ \ \"acc_norm\": 0.9896373056994818,\n \"acc_norm_stderr\": 0.007308424386792194\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8076923076923077,\n \"acc_stderr\": 0.019982347208637282,\n\ \ \"acc_norm\": 0.8076923076923077,\n \"acc_norm_stderr\": 0.019982347208637282\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.5296296296296297,\n \"acc_stderr\": 0.030431963547936584,\n \ \ \"acc_norm\": 0.5296296296296297,\n \"acc_norm_stderr\": 0.030431963547936584\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8319327731092437,\n \"acc_stderr\": 0.024289102115692275,\n\ \ \"acc_norm\": 0.8319327731092437,\n \"acc_norm_stderr\": 0.024289102115692275\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.543046357615894,\n \"acc_stderr\": 0.040673251742474416,\n \"\ acc_norm\": 0.543046357615894,\n \"acc_norm_stderr\": 0.040673251742474416\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9284403669724771,\n \"acc_stderr\": 0.011051255247815481,\n \"\ acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.011051255247815481\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6759259259259259,\n \"acc_stderr\": 0.03191923445686186,\n \"\ acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.03191923445686186\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.01886951464665892,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.01886951464665892\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8945147679324894,\n \"acc_stderr\": 0.019995560723758535,\n \ \ \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.019995560723758535\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8116591928251121,\n\ \ \"acc_stderr\": 0.026241132996407252,\n \"acc_norm\": 0.8116591928251121,\n\ \ \"acc_norm_stderr\": 0.026241132996407252\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8778625954198473,\n \"acc_stderr\": 0.02871877688934232,\n\ \ \"acc_norm\": 0.8778625954198473,\n \"acc_norm_stderr\": 0.02871877688934232\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8677685950413223,\n \"acc_stderr\": 0.0309227883204458,\n \"acc_norm\"\ : 0.8677685950413223,\n \"acc_norm_stderr\": 0.0309227883204458\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8518518518518519,\n\ \ \"acc_stderr\": 0.03434300243630999,\n \"acc_norm\": 0.8518518518518519,\n\ \ \"acc_norm_stderr\": 0.03434300243630999\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n\ \ \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6785714285714286,\n\ \ \"acc_stderr\": 0.04432804055291518,\n \"acc_norm\": 0.6785714285714286,\n\ \ \"acc_norm_stderr\": 0.04432804055291518\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761011,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761011\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.015537514263253878,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.015537514263253878\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.032659863237109066,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.032659863237109066\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9195402298850575,\n\ \ \"acc_stderr\": 0.009726831316141866,\n \"acc_norm\": 0.9195402298850575,\n\ \ \"acc_norm_stderr\": 0.009726831316141866\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.846820809248555,\n \"acc_stderr\": 0.019390370108969934,\n\ \ \"acc_norm\": 0.846820809248555,\n \"acc_norm_stderr\": 0.019390370108969934\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5642458100558659,\n\ \ \"acc_stderr\": 0.016583881958602397,\n \"acc_norm\": 0.5642458100558659,\n\ \ \"acc_norm_stderr\": 0.016583881958602397\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8562091503267973,\n \"acc_stderr\": 0.020091188936043714,\n\ \ \"acc_norm\": 0.8562091503267973,\n \"acc_norm_stderr\": 0.020091188936043714\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8456591639871383,\n\ \ \"acc_stderr\": 0.02051905034208471,\n \"acc_norm\": 0.8456591639871383,\n\ \ \"acc_norm_stderr\": 0.02051905034208471\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8827160493827161,\n \"acc_stderr\": 0.017903112615281123,\n\ \ \"acc_norm\": 0.8827160493827161,\n \"acc_norm_stderr\": 0.017903112615281123\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6276595744680851,\n \"acc_stderr\": 0.028838921471251455,\n \ \ \"acc_norm\": 0.6276595744680851,\n \"acc_norm_stderr\": 0.028838921471251455\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6258148631029987,\n\ \ \"acc_stderr\": 0.012359335618172063,\n \"acc_norm\": 0.6258148631029987,\n\ \ \"acc_norm_stderr\": 0.012359335618172063\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8272058823529411,\n \"acc_stderr\": 0.02296606758558181,\n\ \ \"acc_norm\": 0.8272058823529411,\n \"acc_norm_stderr\": 0.02296606758558181\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8202614379084967,\n \"acc_stderr\": 0.01553374508338279,\n \ \ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.01553374508338279\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7545454545454545,\n\ \ \"acc_stderr\": 0.04122066502878285,\n \"acc_norm\": 0.7545454545454545,\n\ \ \"acc_norm_stderr\": 0.04122066502878285\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7959183673469388,\n \"acc_stderr\": 0.0258012834750905,\n\ \ \"acc_norm\": 0.7959183673469388,\n \"acc_norm_stderr\": 0.0258012834750905\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824667,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824667\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.96,\n \"acc_stderr\": 0.01969463855669321,\n \ \ \"acc_norm\": 0.96,\n \"acc_norm_stderr\": 0.01969463855669321\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685515\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.02464806896136616,\n\ \ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.02464806896136616\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5257567284522894,\n\ \ \"mc2_stderr\": 0.014743557767765337\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.824782951854775,\n \"acc_stderr\": 0.010684179227706167\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7210007581501138,\n \ \ \"acc_stderr\": 0.012354115779970311\n }\n}\n```" repo_url: https://huggingface.co/CausalLM/72B-preview leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|arc:challenge|25_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|arc:challenge|25_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-09T21-42-26.382618.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|gsm8k|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|gsm8k|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hellaswag|10_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hellaswag|10_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-37-44.242475.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-09T21-42-26.382618.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T21-42-26.382618.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-09T21-42-26.382618.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_09T20_37_44.242475 path: - '**/details_harness|winogrande|5_2023-12-09T20-37-44.242475.parquet' - split: 2023_12_09T21_42_26.382618 path: - '**/details_harness|winogrande|5_2023-12-09T21-42-26.382618.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-09T21-42-26.382618.parquet' - config_name: results data_files: - split: 2023_12_09T20_37_44.242475 path: - results_2023-12-09T20-37-44.242475.parquet - split: 2023_12_09T21_42_26.382618 path: - results_2023-12-09T21-42-26.382618.parquet - split: latest path: - results_2023-12-09T21-42-26.382618.parquet --- # Dataset Card for Evaluation run of CausalLM/72B-preview ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CausalLM/72B-preview - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [CausalLM/72B-preview](https://huggingface.co/CausalLM/72B-preview) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CausalLM__72B-preview", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-09T21:42:26.382618](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__72B-preview/blob/main/results_2023-12-09T21-42-26.382618.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7667362936260237, "acc_stderr": 0.027929321227362417, "acc_norm": 0.7704368351697709, "acc_norm_stderr": 0.028461947646281283, "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5257567284522894, "mc2_stderr": 0.014743557767765337 }, "harness|arc:challenge|25": { "acc": 0.606655290102389, "acc_stderr": 0.014275101465693024, "acc_norm": 0.6518771331058021, "acc_norm_stderr": 0.013921008595179347 }, "harness|hellaswag|10": { "acc": 0.6468830910177256, "acc_stderr": 0.004769618829196502, "acc_norm": 0.8323043218482374, "acc_norm_stderr": 0.0037283229688748914 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.9144736842105263, "acc_stderr": 0.02275867713088861, "acc_norm": 0.9144736842105263, "acc_norm_stderr": 0.02275867713088861 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8301886792452831, "acc_stderr": 0.023108393799841326, "acc_norm": 0.8301886792452831, "acc_norm_stderr": 0.023108393799841326 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8958333333333334, "acc_stderr": 0.025545239210256917, "acc_norm": 0.8958333333333334, "acc_norm_stderr": 0.025545239210256917 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7803468208092486, "acc_stderr": 0.031568093627031744, "acc_norm": 0.7803468208092486, "acc_norm_stderr": 0.031568093627031744 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5392156862745098, "acc_stderr": 0.04959859966384181, "acc_norm": 0.5392156862745098, "acc_norm_stderr": 0.04959859966384181 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.8, "acc_stderr": 0.026148818018424502, "acc_norm": 0.8, "acc_norm_stderr": 0.026148818018424502 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5701754385964912, "acc_stderr": 0.04657047260594963, "acc_norm": 0.5701754385964912, "acc_norm_stderr": 0.04657047260594963 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.8, "acc_stderr": 0.0333333333333333, "acc_norm": 0.8, "acc_norm_stderr": 0.0333333333333333 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6798941798941799, "acc_stderr": 0.024026846392873506, "acc_norm": 0.6798941798941799, "acc_norm_stderr": 0.024026846392873506 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04444444444444449, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8903225806451613, "acc_stderr": 0.017776778700485173, "acc_norm": 0.8903225806451613, "acc_norm_stderr": 0.017776778700485173 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6600985221674877, "acc_stderr": 0.033327690684107895, "acc_norm": 0.6600985221674877, "acc_norm_stderr": 0.033327690684107895 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8606060606060606, "acc_stderr": 0.0270459488258654, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.0270459488258654 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9444444444444444, "acc_stderr": 0.0163199507007674, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.0163199507007674 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9896373056994818, "acc_stderr": 0.007308424386792194, "acc_norm": 0.9896373056994818, "acc_norm_stderr": 0.007308424386792194 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8076923076923077, "acc_stderr": 0.019982347208637282, "acc_norm": 0.8076923076923077, "acc_norm_stderr": 0.019982347208637282 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.5296296296296297, "acc_stderr": 0.030431963547936584, "acc_norm": 0.5296296296296297, "acc_norm_stderr": 0.030431963547936584 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8319327731092437, "acc_stderr": 0.024289102115692275, "acc_norm": 0.8319327731092437, "acc_norm_stderr": 0.024289102115692275 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.543046357615894, "acc_stderr": 0.040673251742474416, "acc_norm": 0.543046357615894, "acc_norm_stderr": 0.040673251742474416 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9284403669724771, "acc_stderr": 0.011051255247815481, "acc_norm": 0.9284403669724771, "acc_norm_stderr": 0.011051255247815481 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6759259259259259, "acc_stderr": 0.03191923445686186, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.03191923445686186 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9215686274509803, "acc_stderr": 0.01886951464665892, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.01886951464665892 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8945147679324894, "acc_stderr": 0.019995560723758535, "acc_norm": 0.8945147679324894, "acc_norm_stderr": 0.019995560723758535 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8116591928251121, "acc_stderr": 0.026241132996407252, "acc_norm": 0.8116591928251121, "acc_norm_stderr": 0.026241132996407252 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8778625954198473, "acc_stderr": 0.02871877688934232, "acc_norm": 0.8778625954198473, "acc_norm_stderr": 0.02871877688934232 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8677685950413223, "acc_stderr": 0.0309227883204458, "acc_norm": 0.8677685950413223, "acc_norm_stderr": 0.0309227883204458 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8518518518518519, "acc_stderr": 0.03434300243630999, "acc_norm": 0.8518518518518519, "acc_norm_stderr": 0.03434300243630999 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6785714285714286, "acc_stderr": 0.04432804055291518, "acc_norm": 0.6785714285714286, "acc_norm_stderr": 0.04432804055291518 }, "harness|hendrycksTest-management|5": { "acc": 0.8640776699029126, "acc_stderr": 0.03393295729761011, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.03393295729761011 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253878, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253878 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.88, "acc_stderr": 0.032659863237109066, "acc_norm": 0.88, "acc_norm_stderr": 0.032659863237109066 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9195402298850575, "acc_stderr": 0.009726831316141866, "acc_norm": 0.9195402298850575, "acc_norm_stderr": 0.009726831316141866 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.846820809248555, "acc_stderr": 0.019390370108969934, "acc_norm": 0.846820809248555, "acc_norm_stderr": 0.019390370108969934 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5642458100558659, "acc_stderr": 0.016583881958602397, "acc_norm": 0.5642458100558659, "acc_norm_stderr": 0.016583881958602397 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8562091503267973, "acc_stderr": 0.020091188936043714, "acc_norm": 0.8562091503267973, "acc_norm_stderr": 0.020091188936043714 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8456591639871383, "acc_stderr": 0.02051905034208471, "acc_norm": 0.8456591639871383, "acc_norm_stderr": 0.02051905034208471 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8827160493827161, "acc_stderr": 0.017903112615281123, "acc_norm": 0.8827160493827161, "acc_norm_stderr": 0.017903112615281123 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6276595744680851, "acc_stderr": 0.028838921471251455, "acc_norm": 0.6276595744680851, "acc_norm_stderr": 0.028838921471251455 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6258148631029987, "acc_stderr": 0.012359335618172063, "acc_norm": 0.6258148631029987, "acc_norm_stderr": 0.012359335618172063 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8272058823529411, "acc_stderr": 0.02296606758558181, "acc_norm": 0.8272058823529411, "acc_norm_stderr": 0.02296606758558181 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8202614379084967, "acc_stderr": 0.01553374508338279, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.01553374508338279 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7545454545454545, "acc_stderr": 0.04122066502878285, "acc_norm": 0.7545454545454545, "acc_norm_stderr": 0.04122066502878285 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7959183673469388, "acc_stderr": 0.0258012834750905, "acc_norm": 0.7959183673469388, "acc_norm_stderr": 0.0258012834750905 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824667, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824667 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.96, "acc_stderr": 0.01969463855669321, "acc_norm": 0.96, "acc_norm_stderr": 0.01969463855669321 }, "harness|hendrycksTest-virology|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8830409356725146, "acc_stderr": 0.02464806896136616, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.02464806896136616 }, "harness|truthfulqa:mc|0": { "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5257567284522894, "mc2_stderr": 0.014743557767765337 }, "harness|winogrande|5": { "acc": 0.824782951854775, "acc_stderr": 0.010684179227706167 }, "harness|gsm8k|5": { "acc": 0.7210007581501138, "acc_stderr": 0.012354115779970311 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

RealTimeData/arxiv_july_week2_2023

--- dataset_info: features: - name: entry_id dtype: string - name: published dtype: string - name: title dtype: string - name: authors sequence: string - name: primary_category dtype: string - name: categories sequence: string - name: text dtype: string splits: - name: train num_bytes: 131449614 num_examples: 3069 download_size: 64855895 dataset_size: 131449614 --- # Dataset Card for "arxiv_july_week2_2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ad019el/ar_data

--- dataset_info: features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 40579164.0 num_examples: 1500 - name: test num_bytes: 15846990.0 num_examples: 500 download_size: 55259208 dataset_size: 56426154.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "ar_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ShenaoZhang/0.001_idpo_same_nodpo_dataset

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: is_better dtype: bool splits: - name: train_prefs_1 num_bytes: 154691950 num_examples: 20378 - name: test_prefs_1 num_bytes: 15221730 num_examples: 2000 - name: train_prefs_2 num_bytes: 188487562 num_examples: 20378 - name: test_prefs_2 num_bytes: 18441786 num_examples: 2000 - name: train_prefs_3 num_bytes: 174781446 num_examples: 20378 - name: test_prefs_3 num_bytes: 16959398 num_examples: 2000 download_size: 309771174 dataset_size: 568583872 configs: - config_name: default data_files: - split: train_prefs_1 path: data/train_prefs_1-* - split: test_prefs_1 path: data/test_prefs_1-* - split: train_prefs_2 path: data/train_prefs_2-* - split: test_prefs_2 path: data/test_prefs_2-* - split: train_prefs_3 path: data/train_prefs_3-* - split: test_prefs_3 path: data/test_prefs_3-* --- # Dataset Card for "0.001_idpo_same_nodpo_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

maghwa/OpenHermes-2-AR-10K-29-710k-720k

--- dataset_info: features: - name: source dtype: string - name: hash dtype: 'null' - name: category dtype: 'null' - name: system_prompt dtype: 'null' - name: model_name dtype: 'null' - name: language dtype: 'null' - name: views dtype: float64 - name: conversations dtype: string - name: topic dtype: 'null' - name: id dtype: 'null' - name: avatarUrl dtype: 'null' - name: custom_instruction dtype: 'null' - name: skip_prompt_formatting dtype: 'null' - name: idx dtype: 'null' - name: title dtype: 'null' - name: model dtype: 'null' splits: - name: train num_bytes: 25440345 num_examples: 10001 download_size: 11528460 dataset_size: 25440345 configs: - config_name: default data_files: - split: train path: data/train-* ---

AdapterOcean/med_alpaca_standardized_cluster_34_std

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 14112922 num_examples: 22085 download_size: 6935670 dataset_size: 14112922 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_34_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Vitrola40/RobertoCarlos9091

--- license: openrail ---

poopat/gad

--- license: unknown ---

autoevaluate/autoeval-staging-eval-project-cnn_dailymail-ca1f103f-12035606

--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: nbroad/longt5-base-global-mediasum metrics: [] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: nbroad/longt5-base-global-mediasum * Dataset: cnn_dailymail * Config: 3.0.0 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model.

sidhellman/test

--- license: mit ---

Chidvi201/Twitter_Data.csv

--- license: unknown ---

result-muse256-muse512-wuerst-sdv15/c3f0d903

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1372 dataset_size: 180 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "c3f0d903" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 84344156.125 num_examples: 5647 - name: fewshot_1_bs_16 num_bytes: 85792185.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 88692887.125 num_examples: 5647 - name: fewshot_5_bs_16 num_bytes: 91584891.125 num_examples: 5647 - name: fewshot_8_bs_16 num_bytes: 95914176.125 num_examples: 5647 download_size: 416469739 dataset_size: 446328295.625 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_125m_Attributes_Caption_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

chirunder/text_messages

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 786735647 num_examples: 11615290 download_size: 563363348 dataset_size: 786735647 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "text_messages" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Dewa/Dog_Emotion_Dataset_v2

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: label dtype: int64 - name: emotion dtype: string - name: image dtype: image splits: - name: train num_bytes: 128018890.4 num_examples: 3200 - name: test num_bytes: 31722930.4 num_examples: 800 download_size: 162369679 dataset_size: 159741820.8 license: creativeml-openrail-m task_categories: - image-classification size_categories: - 1K<n<10K --- # Dataset Card for "Dog_Emotion_Dataset_v2" - The Dataset is based on a `kaggle` dataset # Label and its Meaning - `0 : sad"` - `1 : angry"` - `2 : relaxed"` - `3 : happy"`

intfloat/wikipedia

--- size_categories: - 100M<n<1B --- ### Dataset Summary This dataset is based on [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia). The main difference is that we add `Section::::` prefix to each section title to keep the section structure information. We also use `:` to join the hierarchical section titles. Following is an example. ```text Alison Jane Horner (born June 1966) is a British businesswoman, and, until it was sold in 2020, was the CEO of the Asian arm of the Tesco supermarket chain. Section::::Early life Alison Jane Horner was born in June 1966. She earned a bachelor's degree in chemistry from the University of Manchester, and an MBA from Manchester Business School. Section::::Career Section::::Career:Tesco Horner joined Tesco as a personnel manager in 1999 and was on Tesco's executive committee from 2011. In October 2013, Horner became a founding member of The Guardian's Women in Leadership network. in 2015, she became a member of Alliance Manchester Business School's advisory board. Horner was Tesco' chief people officer (chief human resources officer) of Tesco until May 2018, when she was promoted to be chief executive of Tesco's Asia business in Malaysia and Thailand, until it was sold in late 2020. She was set to step down in February 2021 after 22 years with Tesco. Section::::Career:Carillion non-executive role Horner was a non-executive director of Carillion from December 2013, chairing the remuneration committee from June 2014. As of 30 December 2016 her basic compensation was £61,000. After the company went into liquidation in January 2018, Horner was one of the non-executive directors who gave evidence to the House of Commons Business and Work and Pensions select committees on 6 February 2018. In the final report of the Parliamentary Inquiry, published on 16 May 2018, Horner was criticised by MPs; the report concluded: "... Alison Horner presided over growing salaries and bonuses at the top of the company as its performance faltered. In her evidence to us, she sought to justify her approach by pointing to industry standards, the guidance of advisors, and conversations with shareholders. She failed to demonstrate to us any sense of challenge to the advice she was given, any concern about the views of stakeholders, or any regret at the largesse at the top of Carillion. Ms Horner continues to hold the role of Chief People Officer of Tesco, where she has responsibilities to more than half a million employees. We hope that, in that post, she will reflect on the lessons learned from Carillion and her role in its collapse." In January 2021, the Insolvency Service said it would seek to ban eight former Carillion directors, including Horner, from holding senior boardroom positions. Section::::References Living people 1966 births British businesspeople in retailing Tesco people Alumni of the University of Manchester Alumni of the Manchester Business School Carillion people ``` ### Data Fields - `title`: a `string` feature. - `text`: a `string` feature. ### How to use this dataset To load this dataset you need to install these first: ```shell pip install mwparserfromhell==0.6.4 multiprocess==0.70.13 ``` Then, you can load any subset of Wikipedia per language and per date this way: ```python from datasets import load_dataset dataset = load_dataset("intfloat/wikipedia", language="en", date="20230401") ``` For more information, please check out [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia). ## Supported Languages ``` aa ab ace ady af ak als am an ang ar arc arz as ast atj av ay az azb ba bar bat-smg bcl be be-x-old bg bh bi bjn bm bn bo bpy br bs bug bxr ca cbk-zam cdo ce ceb ch cho chr chy ckb co cr crh cs csb cu cv cy da de din diq dsb dty dv dz ee el eml en eo es et eu ext fa ff fi fiu-vro fj fo fr frp frr fur fy ga gag gan gd gl glk gn gom gor got gu gv ha hak haw he hi hif ho hr hsb ht hu hy ia id ie ig ii ik ilo inh io is it iu ja jam jbo jv ka kaa kab kbd kbp kg ki kj kk kl km kn ko koi krc ks ksh ku kv kw ky la lad lb lbe lez lfn lg li lij lmo ln lo lrc lt ltg lv mai map-bms mdf mg mh mhr mi min mk ml mn mr mrj ms mt mus mwl my myv mzn na nah nap nds nds-nl ne new ng nl nn no nov nrm nso nv ny oc olo om or os pa pag pam pap pcd pdc pfl pi pih pl pms pnb pnt ps pt qu rm rmy rn ro roa-rup roa-tara ru rue rw sa sah sat sc scn sco sd se sg sh si simple sk sl sm sn so sq sr srn ss st stq su sv sw szl ta tcy te tet tg th ti tk tl tn to tpi tr ts tt tum tw ty tyv udm ug uk ur uz ve vec vep vi vls vo wa war wo wuu xal xh xmf yi yo za zea zh zh-classical zh-min-nan zh-yue zu ```

gymprathap/Handwriting-Recognition-Dataset

--- license: apache-2.0 task_categories: - token-classification language: - en tags: - code pretty_name: Handwriting Recognition Dataset size_categories: - 1K<n<10K --- The dataset comprises over four hundred thousand handwritten names obtained from charitable initiatives. Character Recognition employs image processing techniques to transform characters present on scanned documents into digital formats. It generally exhibits good performance with machine-printed fonts. Nonetheless, machines still encounter formidable obstacles in accurately identifying handwritten characters due to the vast diversity in individual writing styles. The total number of first names is 206,799, while the total number of surnames is 207,024. The data was partitioned into a training set (331,059 samples), testing set (41,382 samples), and validation set (41,382 samples) respectively. FYI: I am not the owner of this dataset. I took this dataset from kaggle. It is very interesting and useful dataset for many computer vision application. <a href="http://projectcentersinchennai.co.in" title="Project Centers in Chennai">Project Centers in Chennai</a>

ilsp/truthful_qa_greek

--- language: el license: cc-by-nc-sa-4.0 multilinguality: monolingual size_categories: 1K<n<10K task_categories: - multiple-choice - text-generation pretty_name: Truthful QA Greek dataset_info: - config_name: default splits: - name: generation num_examples: 817 - name: multiple_choice num_examples: 817 - config_name: generation features: - name: type dtype: string - name: category dtype: string - name: question dtype: string - name: best_answer dtype: string - name: correct_answers sequence: string - name: incorrect_answers sequence: string - name: source dtype: string - name: question_en dtype: string - name: best_answer_en dtype: string - name: correct_answers_en sequence: string - name: incorrect_answers_en sequence: string - name: question_mt dtype: string - name: best_answer_mt dtype: string - name: correct_answers_mt sequence: string - name: incorrect_answers_mt sequence: string splits: - name: train num_bytes: 2102161 num_examples: 817 download_size: 0 dataset_size: 2102161 - config_name: multiple_choice features: - name: question dtype: string - name: mc1_targets struct: - name: choices sequence: string - name: labels sequence: int32 - name: mc2_targets struct: - name: choices sequence: string - name: labels sequence: int32 - name: question_en dtype: string - name: mc1_targets_en struct: - name: choices sequence: string - name: labels sequence: int32 - name: mc2_targets_en struct: - name: choices sequence: string - name: labels sequence: int32 - name: question_mt dtype: string - name: mc1_targets_mt struct: - name: choices sequence: string - name: labels sequence: int32 - name: mc2_targets_mt struct: - name: choices sequence: string - name: labels sequence: int32 splits: - name: train num_bytes: 3034225 num_examples: 817 download_size: 0 dataset_size: 3034225 configs: - config_name: generation data_files: - split: train path: generation/train-* - config_name: multiple_choice data_files: - split: train path: multiple_choice/train-* --- # Dataset Card for Truthful QA Greek The Truthful QA Greek dataset is a set of 817 questions from the [Truthful QA](https://huggingface.co/datasets/truthful_qa) dataset, translated into Greek. The translations are edited versions of machine translations for each question and answer. The machine translations are also provided. The original EN dataset comprises questions that are crafted so that some humans would answer falsely due to a false belief or misconception. ## Dataset Details ### Dataset Description  - **Curated by:** ILSP/Athena RC   - **Language(s) (NLP):** el - **License:** cc-by-nc-sa-4.0                                        ## Bias, Risks, and Limitations  This dataset is the result of editing machine translation.                 ## Dataset Card Contact https://www.athenarc.gr/en/ilsp

niicovila/law_small

--- license: artistic-2.0 ---

centroIA/ZephyrFormat

--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 2664771 num_examples: 967 download_size: 706398 dataset_size: 2664771 configs: - config_name: default data_files: - split: train path: data/train-* ---

hugginglearners/reddit-depression-cleaned

--- license: - cc0-1.0 kaggle_id: infamouscoder/depression-reddit-cleaned --- # Dataset Card for Depression: Reddit Dataset (Cleaned) ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://kaggle.com/datasets/infamouscoder/depression-reddit-cleaned - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The raw data is collected through web scrapping Subreddits and is cleaned using multiple NLP techniques. The data is only in English language. It mainly targets mental health classification. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was shared by [@infamouscoder](https://kaggle.com/infamouscoder) ### Licensing Information The license for this dataset is cc0-1.0 ### Citation Information ```bibtex [More Information Needed] ``` ### Contributions [More Information Needed]

ares1123/celebrity_dataset

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Aaron Eckhart '1': Aaron Paul '2': Aaron Rodgers '3': Aaron Taylor-Johnson '4': Abbi Jacobson '5': Abhishek Bachchan '6': Abigail Breslin '7': Abigail Spencer '8': Adam Brody '9': Adam Devine '10': Adam Driver '11': Adam Lambert '12': Adam Levine '13': Adam Sandler '14': Adam Scott '15': Adele '16': Adrian Grenier '17': Adèle Exarchopoulos '18': Aidan Gillen '19': Aidan Turner '20': Aishwarya Rai '21': Aja Naomi King '22': Alden Ehrenreich '23': Aldis Hodge '24': Alec Baldwin '25': Alex Morgan '26': Alex Pettyfer '27': Alex Rodriguez '28': Alexander Skarsgård '29': Alexandra Daddario '30': Alfre Woodard '31': Alia Shawkat '32': Alice Braga '33': Alice Eve '34': Alicia Keys '35': Alicia Vikander '36': Alison Brie '37': Allison Janney '38': Allison Williams '39': Alyson Hannigan '40': Amanda Peet '41': Amanda Seyfried '42': Amandla Stenberg '43': Amber Heard '44': America Ferrera '45': Amy Adams '46': Amy Poehler '47': Amy Schumer '48': Ana de Armas '49': Andie MacDowell '50': Andrew Garfield '51': Andrew Lincoln '52': Andrew Scott '53': Andy Garcia '54': Andy Samberg '55': Andy Serkis '56': Angela Bassett '57': Angelina Jolie '58': Anna Camp '59': Anna Faris '60': Anna Kendrick '61': Anna Paquin '62': AnnaSophia Robb '63': Annabelle Wallis '64': Anne Hathaway '65': Anne Marie '66': Anne-Marie '67': Ansel Elgort '68': Anson Mount '69': Anthony Hopkins '70': Anthony Joshua '71': Anthony Mackie '72': Antonio Banderas '73': Anya Taylor-Joy '74': Ariana Grande '75': Armie Hammer '76': Ashley Judd '77': Ashton Kutcher '78': Aubrey Plaza '79': Auli'i Cravalho '80': Awkwafina '81': Barack Obama '82': Bella Hadid '83': Bella Thorne '84': Ben Barnes '85': Ben Mendelsohn '86': Ben Stiller '87': Ben Whishaw '88': Benedict Cumberbatch '89': Benedict Wong '90': Benicio del Toro '91': Bill Gates '92': Bill Hader '93': Bill Murray '94': Bill Pullman '95': Bill Skarsgård '96': Billie Eilish '97': Billie Lourd '98': Billy Crudup '99': Billy Porter '100': Blake Lively '101': Bob Odenkirk '102': Bonnie Wright '103': Boyd Holbrook '104': Brad Pitt '105': Bradley Cooper '106': Brendan Fraser '107': Brian Cox '108': Brie Larson '109': Brittany Snow '110': Bryan Cranston '111': Bryce Dallas Howard '112': Busy Philipps '113': Caitriona Balfe '114': Cameron Diaz '115': Camila Cabello '116': Camila Mendes '117': Cardi B '118': Carey Mulligan '119': Carla Gugino '120': Carrie Underwood '121': Casey Affleck '122': Cate Blanchett '123': Catherine Keener '124': Catherine Zeta-Jones '125': Celine Dion '126': Chace Crawford '127': Chadwick Boseman '128': Channing Tatum '129': Charlie Cox '130': Charlie Day '131': Charlie Hunnam '132': Charlie Plummer '133': Charlize Theron '134': Chiara Ferragni '135': Chiwetel Ejiofor '136': Chloe Bennet '137': Chloe Grace Moretz '138': Chloe Sevigny '139': Chloë Grace Moretz '140': Chloë Sevigny '141': Chris Cooper '142': Chris Evans '143': Chris Hemsworth '144': Chris Martin '145': Chris Messina '146': Chris Noth '147': Chris O'Dowd '148': Chris Pine '149': Chris Pratt '150': Chris Tucker '151': Chrissy Teigen '152': Christian Bale '153': Christian Slater '154': Christina Aguilera '155': Christina Applegate '156': Christina Hendricks '157': Christina Milian '158': Christina Ricci '159': Christine Baranski '160': Christoph Waltz '161': Christopher Plummer '162': Christopher Walken '163': Cillian Murphy '164': Claire Foy '165': Clive Owen '166': Clive Standen '167': Cobie Smulders '168': Colin Farrell '169': Colin Firth '170': Colin Hanks '171': Connie Britton '172': Conor McGregor '173': Constance Wu '174': Constance Zimmer '175': Courteney Cox '176': Cristiano Ronaldo '177': Daisy Ridley '178': Dak Prescott '179': Dakota Fanning '180': Dakota Johnson '181': Damian Lewis '182': Dan Stevens '183': Danai Gurira '184': Dane DeHaan '185': Daniel Craig '186': Daniel Dae Kim '187': Daniel Day-Lewis '188': Daniel Gillies '189': Daniel Kaluuya '190': Daniel Mays '191': Daniel Radcliffe '192': Danny DeVito '193': Darren Criss '194': Dave Bautista '195': Dave Franco '196': Dave Grohl '197': Daveed Diggs '198': David Attenborough '199': David Beckham '200': David Duchovny '201': David Harbour '202': David Oyelowo '203': David Schwimmer '204': David Tennant '205': David Thewlis '206': Dax Shepard '207': Debra Messing '208': Demi Lovato '209': Dennis Quaid '210': Denzel Washington '211': Dermot Mulroney '212': Dev Patel '213': Diane Keaton '214': Diane Kruger '215': Diane Lane '216': Diego Boneta '217': Diego Luna '218': Djimon Hounsou '219': Dolly Parton '220': Domhnall Gleeson '221': Dominic Cooper '222': Dominic Monaghan '223': Dominic West '224': Don Cheadle '225': Donald Glover '226': Donald Sutherland '227': Donald Trump '228': Dua Lipa '229': Dwayne "The Rock" Johnson '230': Dwayne Johnson '231': Dylan O'Brien '232': Ed Harris '233': Ed Helms '234': Ed Sheeran '235': Eddie Murphy '236': Eddie Redmayne '237': Edgar Ramirez '238': Edward Norton '239': Eiza Gonzalez '240': Eiza González '241': Elijah Wood '242': Elisabeth Moss '243': Elisha Cuthbert '244': Eliza Coupe '245': Elizabeth Banks '246': Elizabeth Debicki '247': Elizabeth Lail '248': Elizabeth McGovern '249': Elizabeth Moss '250': Elizabeth Olsen '251': Elle Fanning '252': Ellen DeGeneres '253': Ellen Page '254': Ellen Pompeo '255': Ellie Goulding '256': Elon Musk '257': Emile Hirsch '258': Emilia Clarke '259': Emilia Fox '260': Emily Beecham '261': Emily Blunt '262': Emily Browning '263': Emily Deschanel '264': Emily Hampshire '265': Emily Mortimer '266': Emily Ratajkowski '267': Emily VanCamp '268': Emily Watson '269': Emma Bunton '270': Emma Chamberlain '271': Emma Corrin '272': Emma Mackey '273': Emma Roberts '274': Emma Stone '275': Emma Thompson '276': Emma Watson '277': Emmanuelle Chriqui '278': Emmy Rossum '279': Eoin Macken '280': Eric Bana '281': Ethan Hawke '282': Eva Green '283': Eva Longoria '284': Eva Mendes '285': Evan Peters '286': Evan Rachel Wood '287': Evangeline Lilly '288': Ewan McGregor '289': Ezra Miller '290': Felicity Huffman '291': Felicity Jones '292': Finn Wolfhard '293': Florence Pugh '294': Florence Welch '295': Forest Whitaker '296': Freddie Highmore '297': Freddie Prinze Jr. '298': Freema Agyeman '299': Freida Pinto '300': Freya Allan '301': Gabrielle Union '302': Gael Garcia Bernal '303': Gael García Bernal '304': Gal Gadot '305': Garrett Hedlund '306': Gary Oldman '307': Gemma Arterton '308': Gemma Chan '309': Gemma Whelan '310': George Clooney '311': George Lucas '312': Gerard Butler '313': Giancarlo Esposito '314': Giannis Antetokounmpo '315': Gigi Hadid '316': Gillian Anderson '317': Gillian Jacobs '318': Gina Carano '319': Gina Gershon '320': Gina Rodriguez '321': Ginnifer Goodwin '322': Gisele Bundchen '323': Glenn Close '324': Grace Kelly '325': Greg Kinnear '326': Greta Gerwig '327': Greta Scacchi '328': Greta Thunberg '329': Gugu Mbatha-Raw '330': Guy Ritchie '331': Gwen Stefani '332': Gwendoline Christie '333': Gwyneth Paltrow '334': Hafthor Bjornsson '335': Hailee Steinfeld '336': Hailey Bieber '337': Haley Joel Osment '338': Halle Berry '339': Hannah Simone '340': Harrison Ford '341': Harry Styles '342': Harvey Weinstein '343': Hayden Panettiere '344': Hayley Atwell '345': Helen Hunt '346': Helen Mirren '347': Helena Bonham Carter '348': Henry Cavill '349': Henry Golding '350': Hilary Swank '351': Himesh Patel '352': Hozier '353': Hugh Bonneville '354': Hugh Dancy '355': Hugh Grant '356': Hugh Jackman '357': Hugh Laurie '358': Ian Somerhalder '359': Idris Elba '360': Imelda Staunton '361': Imogen Poots '362': Ioan Gruffudd '363': Isabella Rossellini '364': Isabelle Huppert '365': Isla Fisher '366': Issa Rae '367': Iwan Rheon '368': J.K. Rowling '369': J.K. Simmons '370': Jack Black '371': Jack Reynor '372': Jack Whitehall '373': Jackie Chan '374': Jada Pinkett Smith '375': Jaden Smith '376': Jaimie Alexander '377': Jake Gyllenhaal '378': Jake Johnson '379': Jake T. Austin '380': James Cameron '381': James Corden '382': James Franco '383': James Marsden '384': James McAvoy '385': James Norton '386': Jamie Bell '387': Jamie Chung '388': Jamie Dornan '389': Jamie Foxx '390': Jamie Lee Curtis '391': Jamie Oliver '392': Jane Fonda '393': Jane Krakowski '394': Jane Levy '395': Jane Lynch '396': Jane Seymour '397': Janelle Monáe '398': January Jones '399': Jared Leto '400': Jason Bateman '401': Jason Clarke '402': Jason Derulo '403': Jason Isaacs '404': Jason Momoa '405': Jason Mraz '406': Jason Schwartzman '407': Jason Segel '408': Jason Statham '409': Jason Sudeikis '410': Javier Bardem '411': Jay Baruchel '412': Jay-Z '413': Jeff Bezos '414': Jeff Bridges '415': Jeff Daniels '416': Jeff Goldblum '417': Jeffrey Dean Morgan '418': Jeffrey Donovan '419': Jeffrey Wright '420': Jemima Kirke '421': Jenna Coleman '422': Jenna Fischer '423': Jenna Ortega '424': Jennifer Aniston '425': Jennifer Connelly '426': Jennifer Coolidge '427': Jennifer Esposito '428': Jennifer Garner '429': Jennifer Hudson '430': Jennifer Lawrence '431': Jennifer Lopez '432': Jennifer Love Hewitt '433': Jenny Slate '434': Jeremy Irons '435': Jeremy Renner '436': Jeremy Strong '437': Jerry Seinfeld '438': Jesse Eisenberg '439': Jesse Metcalfe '440': Jesse Plemons '441': Jesse Tyler Ferguson '442': Jesse Williams '443': Jessica Alba '444': Jessica Biel '445': Jessica Chastain '446': Jessica Lange '447': Jessie Buckley '448': Jim Carrey '449': Jim Parsons '450': Joan Collins '451': Joan Cusack '452': Joanne Froggatt '453': Joaquin Phoenix '454': Jodie Comer '455': Jodie Foster '456': Joe Jonas '457': Joe Keery '458': Joel Edgerton '459': Joel Kinnaman '460': Joel McHale '461': John Boyega '462': John C. Reilly '463': John Cena '464': John Cho '465': John Cleese '466': John Corbett '467': John David Washington '468': John Goodman '469': John Hawkes '470': John Krasinski '471': John Legend '472': John Leguizamo '473': John Lithgow '474': John Malkovich '475': John Mayer '476': John Mulaney '477': John Oliver '478': John Slattery '479': John Travolta '480': John Turturro '481': Johnny Depp '482': Johnny Knoxville '483': Jon Bernthal '484': Jon Favreau '485': Jon Hamm '486': Jonah Hill '487': Jonathan Groff '488': Jonathan Majors '489': Jonathan Pryce '490': Jonathan Rhys Meyers '491': Jordan Peele '492': Jordana Brewster '493': Joseph Fiennes '494': Joseph Gordon-Levitt '495': Josh Allen '496': Josh Brolin '497': Josh Gad '498': Josh Hartnett '499': Josh Hutcherson '500': Josh Radnor '501': Jude Law '502': Judy Dench '503': Judy Greer '504': Julia Garner '505': Julia Louis-Dreyfus '506': Julia Roberts '507': Julia Stiles '508': Julian Casablancas '509': Julian McMahon '510': Julianna Margulies '511': Julianne Hough '512': Julianne Moore '513': Julianne Nicholson '514': Juliette Binoche '515': Juliette Lewis '516': Juno Temple '517': Jurnee Smollett '518': Justin Bartha '519': Justin Bieber '520': Justin Hartley '521': Justin Herbert '522': Justin Long '523': Justin Theroux '524': Justin Timberlake '525': KJ Apa '526': Kaitlyn Dever '527': Kaley Cuoco '528': Kanye West '529': Karl Urban '530': Kat Dennings '531': Kate Beckinsale '532': Kate Bosworth '533': Kate Hudson '534': Kate Mara '535': Kate Middleton '536': Kate Upton '537': Kate Walsh '538': Kate Winslet '539': Katee Sackhoff '540': Katherine Heigl '541': Katherine Langford '542': Katherine Waterston '543': Kathryn Hahn '544': Katie Holmes '545': Katie McGrath '546': Katy Perry '547': Kaya Scodelario '548': Keanu Reeves '549': Keegan-Michael Key '550': Keira Knightley '551': Keke Palmer '552': Kelly Clarkson '553': Kelly Macdonald '554': Kelly Marie Tran '555': Kelly Reilly '556': Kelly Ripa '557': Kelvin Harrison Jr. '558': Keri Russell '559': Kerry Washington '560': Kevin Bacon '561': Kevin Costner '562': Kevin Hart '563': Kevin Spacey '564': Ki Hong Lee '565': Kiefer Sutherland '566': Kieran Culkin '567': Kiernan Shipka '568': Kim Dickens '569': Kim Kardashian '570': Kirsten Dunst '571': Kit Harington '572': Kourtney Kardashian '573': Kristen Bell '574': Kristen Stewart '575': Kristen Wiig '576': Kristin Davis '577': Krysten Ritter '578': Kyle Chandler '579': Kylie Jenner '580': Kylie Minogue '581': Lady Gaga '582': Lake Bell '583': Lakeith Stanfield '584': Lamar Jackson '585': Lana Del Rey '586': Laura Dern '587': Laura Harrier '588': Laura Linney '589': Laura Prepon '590': Laurence Fishburne '591': Laverne Cox '592': LeBron James '593': Lea Michele '594': Lea Seydoux '595': Lee Pace '596': Leighton Meester '597': Lena Headey '598': Leonardo Da Vinci '599': Leonardo DiCaprio '600': Leslie Mann '601': Leslie Odom Jr. '602': Lewis Hamilton '603': Liam Hemsworth '604': Liam Neeson '605': Lili Reinhart '606': Lily Aldridge '607': Lily Allen '608': Lily Collins '609': Lily James '610': Lily Rabe '611': Lily Tomlin '612': Lin-Manuel Miranda '613': Linda Cardellini '614': Lionel Messi '615': Lisa Bonet '616': Lisa Kudrow '617': Liv Tyler '618': Lizzo '619': Logan Lerman '620': Lorde '621': Lucy Boynton '622': Lucy Hale '623': Lucy Lawless '624': Lucy Liu '625': Luke Evans '626': Luke Perry '627': Luke Wilson '628': Lupita Nyong'o '629': Léa Seydoux '630': Mackenzie Davis '631': Madelaine Petsch '632': Mads Mikkelsen '633': Mae Whitman '634': Maggie Gyllenhaal '635': Maggie Q '636': Maggie Siff '637': Maggie Smith '638': Mahershala Ali '639': Mahira Khan '640': Maisie Richardson-Sellers '641': Maisie Williams '642': Mandy Moore '643': Mandy Patinkin '644': Marc Anthony '645': Margaret Qualley '646': Margot Robbie '647': Maria Sharapova '648': Marion Cotillard '649': Marisa Tomei '650': Mariska Hargitay '651': Mark Hamill '652': Mark Ruffalo '653': Mark Strong '654': Mark Wahlberg '655': Mark Zuckerberg '656': Marlon Brando '657': Martin Freeman '658': Martin Scorsese '659': Mary Elizabeth Winstead '660': Mary J. Blige '661': Mary Steenburgen '662': Mary-Louise Parker '663': Matt Bomer '664': Matt Damon '665': Matt LeBlanc '666': Matt Smith '667': Matthew Fox '668': Matthew Goode '669': Matthew Macfadyen '670': Matthew McConaughey '671': Matthew Perry '672': Matthew Rhys '673': Matthew Stafford '674': Max Minghella '675': Maya Angelou '676': Maya Hawke '677': Maya Rudolph '678': Megan Fox '679': Megan Rapinoe '680': Meghan Markle '681': Mel Gibson '682': Melanie Lynskey '683': Melissa Benoist '684': Melissa McCarthy '685': Melonie Diaz '686': Meryl Streep '687': Mia Wasikowska '688': Michael B. Jordan '689': Michael C. Hall '690': Michael Caine '691': Michael Cera '692': Michael Cudlitz '693': Michael Douglas '694': Michael Ealy '695': Michael Fassbender '696': Michael Jordan '697': Michael Keaton '698': Michael Pena '699': Michael Peña '700': Michael Phelps '701': Michael Shannon '702': Michael Sheen '703': Michael Stuhlbarg '704': Michelle Dockery '705': Michelle Monaghan '706': Michelle Obama '707': Michelle Pfeiffer '708': Michelle Rodriguez '709': Michelle Williams '710': Michelle Yeoh '711': Michiel Huisman '712': Mila Kunis '713': Miles Teller '714': Milla Jovovich '715': Millie Bobby Brown '716': Milo Ventimiglia '717': Mindy Kaling '718': Miranda Cosgrove '719': Miranda Kerr '720': Mireille Enos '721': Molly Ringwald '722': Morgan Freeman '723': Mélanie Laurent '724': Naomi Campbell '725': Naomi Harris '726': Naomi Scott '727': Naomi Watts '728': Naomie Harris '729': Nas '730': Natalie Dormer '731': Natalie Imbruglia '732': Natalie Morales '733': Natalie Portman '734': Nathalie Emmanuel '735': Nathalie Portman '736': Nathan Fillion '737': Naya Rivera '738': Neil Patrick Harris '739': Neil deGrasse Tyson '740': Neve Campbell '741': Neymar Jr. '742': Nicholas Braun '743': Nicholas Hoult '744': Nick Jonas '745': Nick Kroll '746': Nick Offerman '747': Nick Robinson '748': Nicole Kidman '749': Nikolaj Coster-Waldau '750': Nina Dobrev '751': Noah Centineo '752': Noomi Rapace '753': Norman Reedus '754': Novak Djokovic '755': Octavia Spencer '756': Odessa Young '757': Odette Annable '758': Olivia Colman '759': Olivia Cooke '760': Olivia Holt '761': Olivia Munn '762': Olivia Wilde '763': Oprah Winfrey '764': Orlando Bloom '765': Oscar Isaac '766': Owen Wilson '767': Pablo Picasso '768': Patrick Dempsey '769': Patrick Mahomes '770': Patrick Stewart '771': Patrick Wilson '772': Paul Bettany '773': Paul Dano '774': Paul Giamatti '775': Paul McCartney '776': Paul Rudd '777': Paul Wesley '778': Paula Patton '779': Pedro Almodóvar '780': Pedro Pascal '781': Penelope Cruz '782': Penélope Cruz '783': Pete Davidson '784': Peter Dinklage '785': Phoebe Dynevor '786': Phoebe Waller-Bridge '787': Pierce Brosnan '788': Portia de Rossi '789': Priyanka Chopra '790': Quentin Tarantino '791': Rachel Bilson '792': Rachel Brosnahan '793': Rachel McAdams '794': Rachel Weisz '795': Rafe Spall '796': Rainn Wilson '797': Ralph Fiennes '798': Rami Malek '799': Rashida Jones '800': Ray Liotta '801': Ray Romano '802': Rebecca Ferguson '803': Rebecca Hall '804': Reese Witherspoon '805': Regina Hall '806': Regina King '807': Renee Zellweger '808': Renée Zellweger '809': Rhys Ifans '810': Ricardo Montalban '811': Richard Armitage '812': Richard Gere '813': Richard Jenkins '814': Richard Madden '815': Ricky Gervais '816': Ricky Martin '817': Rihanna '818': Riley Keough '819': Rita Ora '820': River Phoenix '821': Riz Ahmed '822': Rob Lowe '823': Robert Carlyle '824': Robert De Niro '825': Robert Downey Jr. '826': Robert Pattinson '827': Robert Sheehan '828': Robin Tunney '829': Robin Williams '830': Roger Federer '831': Rooney Mara '832': Rosamund Pike '833': Rosario Dawson '834': Rose Byrne '835': Rose Leslie '836': Roselyn Sanchez '837': Ruby Rose '838': Rupert Grint '839': Russell Brand '840': Russell Crowe '841': Russell Wilson '842': Ruth Bader Ginsburg '843': Ruth Wilson '844': Ryan Eggold '845': Ryan Gosling '846': Ryan Murphy '847': Ryan Phillippe '848': Ryan Reynolds '849': Ryan Seacrest '850': Salma Hayek '851': Sam Claflin '852': Sam Heughan '853': Sam Rockwell '854': Sam Smith '855': Samara Weaving '856': Samuel L. Jackson '857': Sandra Bullock '858': Sandra Oh '859': Saoirse Ronan '860': Sarah Gadon '861': Sarah Hyland '862': Sarah Jessica Parker '863': Sarah Michelle Gellar '864': Sarah Paulson '865': Sarah Silverman '866': Sarah Wayne Callies '867': Sasha Alexander '868': Scarlett Johansson '869': Scott Speedman '870': Sean Bean '871': Sebastian Stan '872': Selena Gomez '873': Selma Blair '874': Serena Williams '875': Seth MacFarlane '876': Seth Meyers '877': Seth Rogen '878': Shailene Woodley '879': Shakira '880': Shania Twain '881': Sharlto Copley '882': Shawn Mendes '883': Shia LaBeouf '884': Shiri Appleby '885': Shohreh Aghdashloo '886': Shonda Rhimes '887': Sienna Miller '888': Sigourney Weaver '889': Simon Baker '890': Simon Cowell '891': Simon Pegg '892': Simone Biles '893': Sofia Boutella '894': Sofia Vergara '895': Sophie Turner '896': Sophie Wessex '897': Stanley Tucci '898': Stephen Amell '899': Stephen Colbert '900': Stephen Curry '901': Stephen Dorff '902': Sterling K. Brown '903': Sterling Knight '904': Steve Carell '905': Steven Yeun '906': Susan Sarandon '907': Taika Waititi '908': Taraji P. Henson '909': Taron Egerton '910': Taylor Hill '911': Taylor Kitsch '912': Taylor Lautner '913': Taylor Schilling '914': Taylor Swift '915': Teresa Palmer '916': Terrence Howard '917': Tessa Thompson '918': Thandie Newton '919': The Weeknd '920': Theo James '921': Thomas Brodie-Sangster '922': Thomas Jane '923': Tiger Woods '924': Tilda Swinton '925': Tim Burton '926': Tim Cook '927': Timothee Chalamet '928': Timothy Olyphant '929': Timothy Spall '930': Timothée Chalamet '931': Tina Fey '932': Tobey Maguire '933': Toby Jones '934': Toby Kebbell '935': Toby Regbo '936': Tom Brady '937': Tom Brokaw '938': Tom Cavanagh '939': Tom Cruise '940': Tom Ellis '941': Tom Felton '942': Tom Hanks '943': Tom Hardy '944': Tom Hiddleston '945': Tom Holland '946': Tom Hollander '947': Tom Hopper '948': Tom Selleck '949': Toni Collette '950': Tony Hale '951': Topher Grace '952': Tracee Ellis Ross '953': Tyra Banks '954': Tyrese Gibson '955': Uma Thurman '956': Usain Bolt '957': Uzo Aduba '958': Vanessa Hudgens '959': Vanessa Kirby '960': Vera Farmiga '961': Victoria Pedretti '962': Viggo Mortensen '963': Vin Diesel '964': Vince Vaughn '965': Vincent Cassel '966': Vincent D'Onofrio '967': Vincent Kartheiser '968': Viola Davis '969': Walton Goggins '970': Wes Anderson '971': Wes Bentley '972': Whoopi Goldberg '973': Will Ferrell '974': Will Poulter '975': Willem Dafoe '976': William Jackson Harper '977': William Shatner '978': Winona Ryder '979': Woody Harrelson '980': Yara Shahidi '981': Yvonne Strahovski '982': Zac Efron '983': Zach Braff '984': Zach Galifianakis '985': Zachary Levi '986': Zachary Quinto '987': Zayn Malik '988': Zazie Beetz '989': Zendaya '990': Zoe Kazan '991': Zoe Kravitz '992': Zoe Saldana '993': Zoey Deutch '994': Zooey Deschanel '995': Zoë Kravitz '996': Zoë Saldana splits: - name: train num_bytes: 193671657.464 num_examples: 18184 download_size: 190510261 dataset_size: 193671657.464 configs: - config_name: default data_files: - split: train path: data/train-* --- # Celebrity 1000 Top 1000 celebrities. 18,184 images. 256x256. Square cropped to face.

EleutherAI/quirky_subtraction_increment0_alice

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 12663979.0 num_examples: 192000 - name: validation num_bytes: 263906.0 num_examples: 4000 - name: test num_bytes: 263762.0 num_examples: 4000 download_size: 4096814 dataset_size: 13191647.0 --- # Dataset Card for "quirky_subtraction_increment0_alice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

PommesPeter/imelodist-sft

--- language: - en license: apache-2.0 size_categories: - 100M<n<1B task_categories: - text-generation dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: src dtype: string splits: - name: train num_bytes: 13588597055 num_examples: 5188802 download_size: 7800945420 dataset_size: 13588597055 configs: - config_name: default data_files: - split: train path: data/train-* tags: - music --- # Imelodist-sft dataset for Imelodist Supervised Finetuning

gigant/tib_wip

--- dataset_info: features: - name: doi dtype: string - name: title dtype: string - name: url dtype: string - name: video_url dtype: string - name: license dtype: string - name: subject dtype: string - name: genre dtype: string - name: release_year dtype: string - name: author dtype: string - name: contributors dtype: string - name: abstract dtype: string - name: transcript dtype: string - name: transcript_segments sequence: - name: id dtype: int32 - name: seek dtype: int32 - name: start dtype: float32 - name: end dtype: float32 - name: text dtype: string - name: tokens sequence: int32 - name: temperature dtype: float32 - name: avg_logprob dtype: float32 - name: compression_ratio dtype: float32 - name: no_speech_prob dtype: float32 - name: keyframes sequence: - name: slide dtype: string - name: frames sequence: int32 - name: timestamp sequence: float32 - name: language dtype: string splits: - name: train num_bytes: 1262918268 num_examples: 11043 download_size: 607894050 dataset_size: 1262918268 --- # Dataset Card for "tib_wip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nuprl/MultiPL-E

--- annotations_creators: - machine-generated language: - en language_creators: - machine-generated - expert-generated license: - mit multilinguality: - monolingual pretty_name: MultiPLE-E size_categories: - 1K<n<10K source_datasets: - original - extended|openai_humaneval - extended|mbpp tags: [] task_categories: [] task_ids: [] dataset_info: - config_name: cpp-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 217792 num_examples: 161 download_size: 248493 dataset_size: 217792 - config_name: cpp-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 239517 num_examples: 161 download_size: 270773 dataset_size: 239517 - config_name: cpp-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 239767 num_examples: 161 download_size: 271023 dataset_size: 239767 - config_name: cpp-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 198566 num_examples: 158 download_size: 227555 dataset_size: 198566 - config_name: cs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 259874 num_examples: 158 download_size: 291137 dataset_size: 259874 - config_name: cs-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 283738 num_examples: 158 download_size: 315563 dataset_size: 283738 - config_name: cs-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 283673 num_examples: 158 download_size: 315498 dataset_size: 283673 - config_name: cs-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 237663 num_examples: 155 download_size: 267251 dataset_size: 237663 - config_name: d-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 175592 num_examples: 156 download_size: 209568 dataset_size: 175592 - config_name: d-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181121 num_examples: 156 download_size: 215649 dataset_size: 181121 - config_name: d-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181296 num_examples: 156 download_size: 215824 dataset_size: 181296 - config_name: d-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 157938 num_examples: 153 download_size: 190211 dataset_size: 157938 - config_name: go-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 241130 num_examples: 154 download_size: 280424 dataset_size: 241130 - config_name: go-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 247448 num_examples: 154 download_size: 287275 dataset_size: 247448 - config_name: go-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 247354 num_examples: 154 download_size: 287181 dataset_size: 247354 - config_name: go-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 221519 num_examples: 151 download_size: 258980 dataset_size: 221519 - config_name: java-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 259836 num_examples: 158 download_size: 291099 dataset_size: 259836 - config_name: java-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 286548 num_examples: 158 download_size: 318373 dataset_size: 286548 - config_name: java-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 288031 num_examples: 158 download_size: 319856 dataset_size: 288031 - config_name: java-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 237672 num_examples: 155 download_size: 267260 dataset_size: 237672 - config_name: jl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 163708 num_examples: 159 download_size: 198696 dataset_size: 163708 - config_name: jl-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 167969 num_examples: 159 download_size: 203514 dataset_size: 167969 - config_name: jl-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 168251 num_examples: 159 download_size: 203796 dataset_size: 168251 - config_name: jl-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 145913 num_examples: 156 download_size: 179158 dataset_size: 145913 - config_name: js-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177635 num_examples: 161 download_size: 211822 dataset_size: 177635 - config_name: js-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181987 num_examples: 161 download_size: 216729 dataset_size: 181987 - config_name: js-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182171 num_examples: 161 download_size: 216913 dataset_size: 182171 - config_name: js-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158619 num_examples: 158 download_size: 191028 dataset_size: 158619 - config_name: lua-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 180398 num_examples: 161 download_size: 212511 dataset_size: 180398 - config_name: lua-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 184763 num_examples: 161 download_size: 216595 dataset_size: 184763 - config_name: lua-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 184853 num_examples: 161 download_size: 216685 dataset_size: 184853 - config_name: lua-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 161339 num_examples: 158 download_size: 191690 dataset_size: 161339 - config_name: php-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 219526 num_examples: 161 download_size: 256134 dataset_size: 219526 - config_name: php-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 225575 num_examples: 161 download_size: 262738 dataset_size: 225575 - config_name: php-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 225730 num_examples: 161 download_size: 262893 dataset_size: 225730 - config_name: php-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 200047 num_examples: 158 download_size: 234848 dataset_size: 200047 - config_name: pl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 239874 num_examples: 161 download_size: 279351 dataset_size: 239874 - config_name: pl-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 243611 num_examples: 161 download_size: 283767 dataset_size: 243611 - config_name: pl-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 243661 num_examples: 161 download_size: 283817 dataset_size: 243661 - config_name: pl-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 220817 num_examples: 158 download_size: 258463 dataset_size: 220817 - config_name: py-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 173537 num_examples: 161 download_size: 207009 dataset_size: 173537 - config_name: py-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177787 num_examples: 161 download_size: 210975 dataset_size: 177787 - config_name: py-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177787 num_examples: 161 download_size: 210975 dataset_size: 177787 - config_name: py-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 155389 num_examples: 158 download_size: 187068 dataset_size: 155389 - config_name: r-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186803 num_examples: 161 download_size: 215857 dataset_size: 186803 - config_name: r-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 191732 num_examples: 161 download_size: 220505 dataset_size: 191732 - config_name: r-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 191747 num_examples: 161 download_size: 220520 dataset_size: 191747 - config_name: r-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 168422 num_examples: 158 download_size: 195771 dataset_size: 168422 - config_name: rb-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181999 num_examples: 161 download_size: 216186 dataset_size: 181999 - config_name: rb-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188317 num_examples: 161 download_size: 223059 dataset_size: 188317 - config_name: rb-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188457 num_examples: 161 download_size: 223199 dataset_size: 188457 - config_name: rb-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 163569 num_examples: 158 download_size: 195978 dataset_size: 163569 - config_name: rkt-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177757 num_examples: 161 download_size: 212266 dataset_size: 177757 - config_name: rkt-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182937 num_examples: 161 download_size: 218001 dataset_size: 182937 - config_name: rkt-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182754 num_examples: 161 download_size: 217818 dataset_size: 182754 - config_name: rkt-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158729 num_examples: 158 download_size: 191454 dataset_size: 158729 - config_name: rs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177191 num_examples: 156 download_size: 206604 dataset_size: 177191 - config_name: rs-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188587 num_examples: 156 download_size: 218555 dataset_size: 188587 - config_name: rs-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188841 num_examples: 156 download_size: 218809 dataset_size: 188841 - config_name: rs-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158191 num_examples: 153 download_size: 185991 dataset_size: 158191 - config_name: scala-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 222118 num_examples: 160 download_size: 253027 dataset_size: 222118 - config_name: scala-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240540 num_examples: 160 download_size: 272012 dataset_size: 240540 - config_name: scala-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240466 num_examples: 160 download_size: 271938 dataset_size: 240466 - config_name: scala-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 200261 num_examples: 157 download_size: 229477 dataset_size: 200261 - config_name: sh-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158460 num_examples: 158 download_size: 193268 dataset_size: 158460 - config_name: sh-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164552 num_examples: 158 download_size: 201631 dataset_size: 164552 - config_name: sh-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164521 num_examples: 158 download_size: 201600 dataset_size: 164521 - config_name: sh-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 140720 num_examples: 155 download_size: 173767 dataset_size: 140720 - config_name: swift-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 201798 num_examples: 161 download_size: 233903 dataset_size: 201798 - config_name: swift-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 204760 num_examples: 158 download_size: 236660 dataset_size: 204760 - config_name: swift-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 204920 num_examples: 158 download_size: 236820 dataset_size: 204920 - config_name: swift-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181681 num_examples: 158 download_size: 212047 dataset_size: 181681 - config_name: ts-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181763 num_examples: 159 download_size: 215589 dataset_size: 181763 - config_name: ts-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186037 num_examples: 159 download_size: 220423 dataset_size: 186037 - config_name: ts-reworded features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186215 num_examples: 159 download_size: 220601 dataset_size: 186215 - config_name: ts-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 162881 num_examples: 156 download_size: 194985 dataset_size: 162881 - config_name: cpp features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 239767 num_examples: 161 download_size: 271023 dataset_size: 239767 - config_name: cs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 283673 num_examples: 158 download_size: 315498 dataset_size: 283673 - config_name: d features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181296 num_examples: 156 download_size: 215824 dataset_size: 181296 - config_name: go features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 247354 num_examples: 154 download_size: 287181 dataset_size: 247354 - config_name: java features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 288031 num_examples: 158 download_size: 319856 dataset_size: 288031 - config_name: jl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 168251 num_examples: 159 download_size: 203796 dataset_size: 168251 - config_name: js features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182171 num_examples: 161 download_size: 216913 dataset_size: 182171 - config_name: lua features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 184853 num_examples: 161 download_size: 216685 dataset_size: 184853 - config_name: php features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 225730 num_examples: 161 download_size: 262893 dataset_size: 225730 - config_name: pl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 243661 num_examples: 161 download_size: 283817 dataset_size: 243661 - config_name: py features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 177787 num_examples: 161 download_size: 210975 dataset_size: 177787 - config_name: r features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 191747 num_examples: 161 download_size: 220520 dataset_size: 191747 - config_name: rb features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188457 num_examples: 161 download_size: 223199 dataset_size: 188457 - config_name: rkt features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182754 num_examples: 161 download_size: 217818 dataset_size: 182754 - config_name: rs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 188841 num_examples: 156 download_size: 218809 dataset_size: 188841 - config_name: scala features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240466 num_examples: 160 download_size: 271938 dataset_size: 240466 - config_name: sh features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164521 num_examples: 158 download_size: 201600 dataset_size: 164521 - config_name: swift features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 204920 num_examples: 158 download_size: 236820 dataset_size: 204920 - config_name: ts features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186215 num_examples: 159 download_size: 220601 dataset_size: 186215 - config_name: humaneval-cpp-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 218990 num_examples: 161 download_size: 249691 dataset_size: 218990 - config_name: humaneval-cpp-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240786 num_examples: 161 download_size: 272042 dataset_size: 240786 - config_name: humaneval-cpp features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 241036 num_examples: 161 download_size: 272292 dataset_size: 241036 - config_name: humaneval-cpp-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 199746 num_examples: 158 download_size: 228735 dataset_size: 199746 - config_name: humaneval-cs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 260822 num_examples: 158 download_size: 292085 dataset_size: 260822 - config_name: humaneval-cs-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 284686 num_examples: 158 download_size: 316511 dataset_size: 284686 - config_name: humaneval-cs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 284621 num_examples: 158 download_size: 316446 dataset_size: 284621 - config_name: humaneval-cs-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 238593 num_examples: 155 download_size: 268181 dataset_size: 238593 - config_name: humaneval-d-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 176864 num_examples: 156 download_size: 210856 dataset_size: 176864 - config_name: humaneval-d-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182057 num_examples: 156 download_size: 216585 dataset_size: 182057 - config_name: humaneval-d features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182232 num_examples: 156 download_size: 216760 dataset_size: 182232 - config_name: humaneval-d-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 158856 num_examples: 153 download_size: 191129 dataset_size: 158856 - config_name: humaneval-go-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 242054 num_examples: 154 download_size: 281348 dataset_size: 242054 - config_name: humaneval-go-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 248372 num_examples: 154 download_size: 288199 dataset_size: 248372 - config_name: humaneval-go features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 248278 num_examples: 154 download_size: 288105 dataset_size: 248278 - config_name: humaneval-go-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 222425 num_examples: 151 download_size: 259886 dataset_size: 222425 - config_name: humaneval-java-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 261057 num_examples: 158 download_size: 292320 dataset_size: 261057 - config_name: humaneval-java-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 287860 num_examples: 158 download_size: 319685 dataset_size: 287860 - config_name: humaneval-java features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 289343 num_examples: 158 download_size: 321168 dataset_size: 289343 - config_name: humaneval-java-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 238875 num_examples: 155 download_size: 268463 dataset_size: 238875 - config_name: humaneval-jl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164664 num_examples: 159 download_size: 199654 dataset_size: 164664 - config_name: humaneval-jl-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 168925 num_examples: 159 download_size: 204472 dataset_size: 168925 - config_name: humaneval-jl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 169207 num_examples: 159 download_size: 204754 dataset_size: 169207 - config_name: humaneval-jl-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 146851 num_examples: 156 download_size: 180098 dataset_size: 146851 - config_name: humaneval-js-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 178601 num_examples: 161 download_size: 212788 dataset_size: 178601 - config_name: humaneval-js-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182953 num_examples: 161 download_size: 217695 dataset_size: 182953 - config_name: humaneval-js features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 183137 num_examples: 161 download_size: 217879 dataset_size: 183137 - config_name: humaneval-js-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 159567 num_examples: 158 download_size: 191976 dataset_size: 159567 - config_name: humaneval-lua-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 181364 num_examples: 161 download_size: 213477 dataset_size: 181364 - config_name: humaneval-lua-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 185729 num_examples: 161 download_size: 217561 dataset_size: 185729 - config_name: humaneval-lua features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 185819 num_examples: 161 download_size: 217651 dataset_size: 185819 - config_name: humaneval-lua-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 162287 num_examples: 158 download_size: 192638 dataset_size: 162287 - config_name: humaneval-php-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 220492 num_examples: 161 download_size: 257100 dataset_size: 220492 - config_name: humaneval-php-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 226541 num_examples: 161 download_size: 263704 dataset_size: 226541 - config_name: humaneval-php features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 226696 num_examples: 161 download_size: 263859 dataset_size: 226696 - config_name: humaneval-php-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 200995 num_examples: 158 download_size: 235796 dataset_size: 200995 - config_name: humaneval-pl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 240840 num_examples: 161 download_size: 280317 dataset_size: 240840 - config_name: humaneval-pl-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 244577 num_examples: 161 download_size: 284733 dataset_size: 244577 - config_name: humaneval-pl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 244627 num_examples: 161 download_size: 284783 dataset_size: 244627 - config_name: humaneval-pl-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 221765 num_examples: 158 download_size: 259411 dataset_size: 221765 - config_name: humaneval-py-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 174503 num_examples: 161 download_size: 207975 dataset_size: 174503 - config_name: humaneval-py-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 178753 num_examples: 161 download_size: 211941 dataset_size: 178753 - config_name: humaneval-py features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 178753 num_examples: 161 download_size: 211941 dataset_size: 178753 - config_name: humaneval-py-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 156337 num_examples: 158 download_size: 188016 dataset_size: 156337 - config_name: humaneval-r-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186140 num_examples: 161 download_size: 215194 dataset_size: 186140 - config_name: humaneval-r-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 190637 num_examples: 161 download_size: 219410 dataset_size: 190637 - config_name: humaneval-r features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 190652 num_examples: 161 download_size: 219425 dataset_size: 190652 - config_name: humaneval-r-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 167742 num_examples: 158 download_size: 195091 dataset_size: 167742 - config_name: humaneval-rb-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182965 num_examples: 161 download_size: 217152 dataset_size: 182965 - config_name: humaneval-rb-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 189283 num_examples: 161 download_size: 224025 dataset_size: 189283 - config_name: humaneval-rb features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 189423 num_examples: 161 download_size: 224165 dataset_size: 189423 - config_name: humaneval-rb-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 164517 num_examples: 158 download_size: 196926 dataset_size: 164517 - config_name: humaneval-rkt-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 185503 num_examples: 161 download_size: 220012 dataset_size: 185503 - config_name: humaneval-rkt-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 190683 num_examples: 161 download_size: 225747 dataset_size: 190683 - config_name: humaneval-rkt features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 190500 num_examples: 161 download_size: 225564 dataset_size: 190500 - config_name: humaneval-rkt-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 166379 num_examples: 158 download_size: 199104 dataset_size: 166379 - config_name: humaneval-rs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 178127 num_examples: 156 download_size: 207540 dataset_size: 178127 - config_name: humaneval-rs-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 189523 num_examples: 156 download_size: 219491 dataset_size: 189523 - config_name: humaneval-rs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 189777 num_examples: 156 download_size: 219745 dataset_size: 189777 - config_name: humaneval-rs-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 159109 num_examples: 153 download_size: 186909 dataset_size: 159109 - config_name: humaneval-scala-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 223078 num_examples: 160 download_size: 253987 dataset_size: 223078 - config_name: humaneval-scala-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 241500 num_examples: 160 download_size: 272972 dataset_size: 241500 - config_name: humaneval-scala features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 241426 num_examples: 160 download_size: 272898 dataset_size: 241426 - config_name: humaneval-scala-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 201203 num_examples: 157 download_size: 230419 dataset_size: 201203 - config_name: humaneval-sh-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 159408 num_examples: 158 download_size: 194216 dataset_size: 159408 - config_name: humaneval-sh-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 165500 num_examples: 158 download_size: 202579 dataset_size: 165500 - config_name: humaneval-sh features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 165469 num_examples: 158 download_size: 202548 dataset_size: 165469 - config_name: humaneval-sh-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 141650 num_examples: 155 download_size: 174697 dataset_size: 141650 - config_name: humaneval-swift-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 202764 num_examples: 161 download_size: 234869 dataset_size: 202764 - config_name: humaneval-swift-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 205708 num_examples: 158 download_size: 237608 dataset_size: 205708 - config_name: humaneval-swift features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 205868 num_examples: 158 download_size: 237768 dataset_size: 205868 - config_name: humaneval-swift-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182629 num_examples: 158 download_size: 212995 dataset_size: 182629 - config_name: humaneval-ts-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 182717 num_examples: 159 download_size: 216543 dataset_size: 182717 - config_name: humaneval-ts-transform features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 186991 num_examples: 159 download_size: 221377 dataset_size: 186991 - config_name: humaneval-ts features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 187169 num_examples: 159 download_size: 221555 dataset_size: 187169 - config_name: humaneval-ts-remove features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 163817 num_examples: 156 download_size: 195921 dataset_size: 163817 - config_name: mbpp-cpp-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 360057 num_examples: 397 download_size: 428174 dataset_size: 360057 - config_name: mbpp-cpp features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 362541 num_examples: 397 download_size: 430658 dataset_size: 362541 - config_name: mbpp-cs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 416276 num_examples: 386 download_size: 484875 dataset_size: 416276 - config_name: mbpp-cs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 418156 num_examples: 386 download_size: 486755 dataset_size: 418156 - config_name: mbpp-d-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 232820 num_examples: 358 download_size: 303807 dataset_size: 232820 - config_name: mbpp-d features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 234776 num_examples: 358 download_size: 305763 dataset_size: 234776 - config_name: mbpp-go-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 399157 num_examples: 374 download_size: 486803 dataset_size: 399157 - config_name: mbpp-go features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 400841 num_examples: 374 download_size: 488487 dataset_size: 400841 - config_name: mbpp-java-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 419406 num_examples: 386 download_size: 488005 dataset_size: 419406 - config_name: mbpp-java features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 423652 num_examples: 386 download_size: 492251 dataset_size: 423652 - config_name: mbpp-jl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 228259 num_examples: 390 download_size: 305322 dataset_size: 228259 - config_name: mbpp-jl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 230672 num_examples: 390 download_size: 307735 dataset_size: 230672 - config_name: mbpp-js-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 256499 num_examples: 397 download_size: 333225 dataset_size: 256499 - config_name: mbpp-js features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 258734 num_examples: 397 download_size: 335460 dataset_size: 258734 - config_name: mbpp-lua-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 262378 num_examples: 397 download_size: 335520 dataset_size: 262378 - config_name: mbpp-lua features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 264635 num_examples: 397 download_size: 337777 dataset_size: 264635 - config_name: mbpp-php-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 308918 num_examples: 397 download_size: 388541 dataset_size: 308918 - config_name: mbpp-php features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 311263 num_examples: 397 download_size: 390886 dataset_size: 311263 - config_name: mbpp-pl-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 321045 num_examples: 396 download_size: 402353 dataset_size: 321045 - config_name: mbpp-pl features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 323224 num_examples: 396 download_size: 404532 dataset_size: 323224 - config_name: mbpp-py-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 253037 num_examples: 397 download_size: 330230 dataset_size: 253037 - config_name: mbpp-py features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 255022 num_examples: 397 download_size: 332215 dataset_size: 255022 - config_name: mbpp-r-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 257698 num_examples: 397 download_size: 323297 dataset_size: 257698 - config_name: mbpp-r features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 259514 num_examples: 397 download_size: 325113 dataset_size: 259514 - config_name: mbpp-rb-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 266702 num_examples: 397 download_size: 343428 dataset_size: 266702 - config_name: mbpp-rb features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 268881 num_examples: 397 download_size: 345607 dataset_size: 268881 - config_name: mbpp-rkt-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 269019 num_examples: 397 download_size: 346539 dataset_size: 269019 - config_name: mbpp-rkt features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 270933 num_examples: 397 download_size: 348453 dataset_size: 270933 - config_name: mbpp-rs-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 218020 num_examples: 354 download_size: 277268 dataset_size: 218020 - config_name: mbpp-rs features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 220113 num_examples: 354 download_size: 279361 dataset_size: 220113 - config_name: mbpp-scala-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 330435 num_examples: 396 download_size: 399451 dataset_size: 330435 - config_name: mbpp-scala features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 332677 num_examples: 396 download_size: 401693 dataset_size: 332677 - config_name: mbpp-sh-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 217246 num_examples: 382 download_size: 289241 dataset_size: 217246 - config_name: mbpp-sh features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 219035 num_examples: 382 download_size: 291030 dataset_size: 219035 - config_name: mbpp-swift-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 317271 num_examples: 396 download_size: 388726 dataset_size: 317271 - config_name: mbpp-swift features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 319946 num_examples: 396 download_size: 391401 dataset_size: 319946 - config_name: mbpp-ts-keep features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 265973 num_examples: 390 download_size: 341007 dataset_size: 265973 - config_name: mbpp-ts features: - name: name dtype: string - name: language dtype: string - name: prompt dtype: string - name: doctests dtype: string - name: original dtype: string - name: prompt_terminology dtype: string - name: tests dtype: string - name: stop_tokens sequence: string splits: - name: test num_bytes: 268179 num_examples: 390 download_size: 343213 dataset_size: 268179 --- # Dataset Card for MultiPL-E ## Dataset Description - **Homepage:** https://nuprl.github.io/MultiPL-E/ - **Repository:** https://github.com/nuprl/MultiPL-E - **Paper:** https://ieeexplore.ieee.org/abstract/document/10103177 - **Point of Contact:** carolyn.anderson@wellesley.edu, mfeldman@oberlin.edu, a.guha@northeastern.edu ## Dataset Summary MultiPL-E is a dataset for evaluating large language models for code generation that supports 18 programming languages. It takes the OpenAI "HumanEval" and the MBPP Python benchmarks and uses little compilers to translate them to other languages. It is easy to add support for new languages and benchmarks. ## Subsets For most purposes, you should use the variations called *SRCDATA-LANG*, where *SRCDATA* is either "humaneval" or "mbpp" and *LANG* is one of the supported languages. We use the canonical file extension for each language to identify the language, e.g., "py" for Python, "cpp" for C++, "lua" for Lua, and so on. We also provide a few other variations: - *SRCDATA-LANG-keep* is the same as *SRCDATA-LANG*, but the text of the prompt is totally unchanged. If the original prompt had Python doctests, they remain as Python instead of being translated to *LANG*. If the original prompt had Python-specific terminology, e.g., "list", it remains "list", instead of being translated, e.g., to "vector" for C++. - *SRCDATA-LANG-transform* transforms the doctests to *LANG* but leaves the natural language text of the prompt unchanged. - *SRCDATA-LANG-removed* removes the doctests from the prompt. Note that MBPP does not have any doctests, so the "removed" and "transform" variations are not available for MBPP. ## Example The following script uses the Salesforce/codegen model to generate Lua and MultiPL-E to produce a script with unit tests for luaunit. ```python import datasets from transformers import AutoTokenizer, AutoModelForCausalLM LANG = "lua" MODEL_NAME = "Salesforce/codegen-350M-multi" tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForCausalLM.from_pretrained(MODEL_NAME).half().cuda() problems = datasets.load_dataset("nuprl/MultiPL-E", f"humaneval-{LANG}") def stop_at_stop_token(decoded_string, problem): """ Truncates the output at stop tokens, taking care to skip the prompt which may have stop tokens. """ min_stop_index = len(decoded_string) for stop_token in problem["stop_tokens"]: stop_index = decoded_string.find(stop_token) if stop_index != -1 and stop_index > len(problem["prompt"]) and stop_index < min_stop_index: min_stop_index = stop_index return decoded_string[:min_stop_index] for problem in problems["test"]: input_ids = tokenizer( problem["prompt"], return_tensors="pt", ).input_ids.cuda() generated_ids = model.generate( input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id + 2 ) truncated_string = stop_at_stop_token(tokenizer.decode(generated_ids[0]), problem) filename = problem["name"] + "." + LANG with open(filename, "w") as f: print(f"Created {filename}") f.write(truncated_string) f.write("\n") f.write(problem["tests"]) ```

Ddream-ai/InsuranceCorpus

--- license: mit dataset_info: features: - name: 咨询 dtype: string - name: 回复 dtype: string splits: - name: train num_bytes: 3612350 num_examples: 3599 - name: validation num_bytes: 186138 num_examples: 189 download_size: 2267366 dataset_size: 3798488 ---

joey234/mmlu-security_studies-original-neg

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 30097.763265306123 num_examples: 36 download_size: 14397 dataset_size: 30097.763265306123 --- # Dataset Card for "mmlu-security_studies-original-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_MTSAIR__MultiVerse_70B

--- pretty_name: Evaluation run of MTSAIR/MultiVerse_70B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [MTSAIR/MultiVerse_70B](https://huggingface.co/MTSAIR/MultiVerse_70B) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MTSAIR__MultiVerse_70B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-28T11:20:18.515649](https://huggingface.co/datasets/open-llm-leaderboard/details_MTSAIR__MultiVerse_70B/blob/main/results_2024-03-28T11-20-18.515649.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7830598805961457,\n\ \ \"acc_stderr\": 0.027571176888417693,\n \"acc_norm\": 0.7846693275609173,\n\ \ \"acc_norm_stderr\": 0.028121251178584224,\n \"mc1\": 0.6499388004895961,\n\ \ \"mc1_stderr\": 0.016697949420151022,\n \"mc2\": 0.7508968077654237,\n\ \ \"mc2_stderr\": 0.014534916537858438\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7636518771331058,\n \"acc_stderr\": 0.012414960524301822,\n\ \ \"acc_norm\": 0.7858361774744027,\n \"acc_norm_stderr\": 0.01198838320596649\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7490539733120892,\n\ \ \"acc_stderr\": 0.0043267144532667355,\n \"acc_norm\": 0.8974307906791475,\n\ \ \"acc_norm_stderr\": 0.0030277534195929483\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n\ \ \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n\ \ \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8881578947368421,\n \"acc_stderr\": 0.02564834125169361,\n\ \ \"acc_norm\": 0.8881578947368421,\n \"acc_norm_stderr\": 0.02564834125169361\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8528301886792453,\n \"acc_stderr\": 0.02180412613479737,\n\ \ \"acc_norm\": 0.8528301886792453,\n \"acc_norm_stderr\": 0.02180412613479737\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9236111111111112,\n\ \ \"acc_stderr\": 0.022212203938345918,\n \"acc_norm\": 0.9236111111111112,\n\ \ \"acc_norm_stderr\": 0.022212203938345918\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7630057803468208,\n\ \ \"acc_stderr\": 0.032424147574830975,\n \"acc_norm\": 0.7630057803468208,\n\ \ \"acc_norm_stderr\": 0.032424147574830975\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.049665709039785295,\n\ \ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.049665709039785295\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n\ \ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.8468085106382979,\n \"acc_stderr\": 0.023545179061675203,\n\ \ \"acc_norm\": 0.8468085106382979,\n \"acc_norm_stderr\": 0.023545179061675203\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583706,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583706\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\ \ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.6957671957671958,\n\ \ \"acc_stderr\": 0.023695415009463087,\n \"acc_norm\": 0.6957671957671958,\n\ \ \"acc_norm_stderr\": 0.023695415009463087\n },\n \"harness|hendrycksTest-formal_logic|5\"\ : {\n \"acc\": 0.5476190476190477,\n \"acc_stderr\": 0.044518079590553275,\n\ \ \"acc_norm\": 0.5476190476190477,\n \"acc_norm_stderr\": 0.044518079590553275\n\ \ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_biology|5\"\ : {\n \"acc\": 0.8870967741935484,\n \"acc_stderr\": 0.018003603325863614,\n\ \ \"acc_norm\": 0.8870967741935484,\n \"acc_norm_stderr\": 0.018003603325863614\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.7142857142857143,\n \"acc_stderr\": 0.03178529710642751,\n \"\ acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.03178529710642751\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\ : 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706467,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706467\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9292929292929293,\n \"acc_stderr\": 0.01826310542019948,\n \"\ acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.01826310542019948\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9844559585492227,\n \"acc_stderr\": 0.008927492715084334,\n\ \ \"acc_norm\": 0.9844559585492227,\n \"acc_norm_stderr\": 0.008927492715084334\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8025641025641026,\n \"acc_stderr\": 0.02018264696867485,\n \ \ \"acc_norm\": 0.8025641025641026,\n \"acc_norm_stderr\": 0.02018264696867485\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.5037037037037037,\n \"acc_stderr\": 0.03048470166508437,\n \ \ \"acc_norm\": 0.5037037037037037,\n \"acc_norm_stderr\": 0.03048470166508437\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.023005459446673936,\n\ \ \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.023005459446673936\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5894039735099338,\n \"acc_stderr\": 0.04016689594849928,\n \"\ acc_norm\": 0.5894039735099338,\n \"acc_norm_stderr\": 0.04016689594849928\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9357798165137615,\n \"acc_stderr\": 0.010510494713201405,\n \"\ acc_norm\": 0.9357798165137615,\n \"acc_norm_stderr\": 0.010510494713201405\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.7129629629629629,\n \"acc_stderr\": 0.03085199299325701,\n \"\ acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.03085199299325701\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \ \ \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8161434977578476,\n\ \ \"acc_stderr\": 0.025998379092356513,\n \"acc_norm\": 0.8161434977578476,\n\ \ \"acc_norm_stderr\": 0.025998379092356513\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515375,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515375\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8925619834710744,\n \"acc_stderr\": 0.028268812192540616,\n \"\ acc_norm\": 0.8925619834710744,\n \"acc_norm_stderr\": 0.028268812192540616\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8703703703703703,\n\ \ \"acc_stderr\": 0.03247224389917947,\n \"acc_norm\": 0.8703703703703703,\n\ \ \"acc_norm_stderr\": 0.03247224389917947\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n\ \ \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.7053571428571429,\n\ \ \"acc_stderr\": 0.0432704093257873,\n \"acc_norm\": 0.7053571428571429,\n\ \ \"acc_norm_stderr\": 0.0432704093257873\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.03288180278808628,\n\ \ \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.03288180278808628\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.015537514263253874,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.015537514263253874\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896308,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896308\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9233716475095786,\n\ \ \"acc_stderr\": 0.00951217069932386,\n \"acc_norm\": 0.9233716475095786,\n\ \ \"acc_norm_stderr\": 0.00951217069932386\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8497109826589595,\n \"acc_stderr\": 0.01923931878390472,\n\ \ \"acc_norm\": 0.8497109826589595,\n \"acc_norm_stderr\": 0.01923931878390472\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.823463687150838,\n\ \ \"acc_stderr\": 0.012751770640520499,\n \"acc_norm\": 0.823463687150838,\n\ \ \"acc_norm_stderr\": 0.012751770640520499\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8562091503267973,\n \"acc_stderr\": 0.020091188936043725,\n\ \ \"acc_norm\": 0.8562091503267973,\n \"acc_norm_stderr\": 0.020091188936043725\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.842443729903537,\n\ \ \"acc_stderr\": 0.020692237273583984,\n \"acc_norm\": 0.842443729903537,\n\ \ \"acc_norm_stderr\": 0.020692237273583984\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8734567901234568,\n \"acc_stderr\": 0.018498600558790906,\n\ \ \"acc_norm\": 0.8734567901234568,\n \"acc_norm_stderr\": 0.018498600558790906\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6631205673758865,\n \"acc_stderr\": 0.02819553487396673,\n \ \ \"acc_norm\": 0.6631205673758865,\n \"acc_norm_stderr\": 0.02819553487396673\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6323337679269883,\n\ \ \"acc_stderr\": 0.012314845910071703,\n \"acc_norm\": 0.6323337679269883,\n\ \ \"acc_norm_stderr\": 0.012314845910071703\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.02151396405285963,\n\ \ \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02151396405285963\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8300653594771242,\n \"acc_stderr\": 0.015194153113184724,\n \ \ \"acc_norm\": 0.8300653594771242,\n \"acc_norm_stderr\": 0.015194153113184724\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7818181818181819,\n\ \ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.7818181818181819,\n\ \ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8163265306122449,\n \"acc_stderr\": 0.024789071332007646,\n\ \ \"acc_norm\": 0.8163265306122449,\n \"acc_norm_stderr\": 0.024789071332007646\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101716,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101716\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.95,\n \"acc_stderr\": 0.021904291355759026,\n \ \ \"acc_norm\": 0.95,\n \"acc_norm_stderr\": 0.021904291355759026\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276894,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276894\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6499388004895961,\n\ \ \"mc1_stderr\": 0.016697949420151022,\n \"mc2\": 0.7508968077654237,\n\ \ \"mc2_stderr\": 0.014534916537858438\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8737174427782163,\n \"acc_stderr\": 0.009335559129908452\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7680060652009097,\n \ \ \"acc_stderr\": 0.011626873175092412\n }\n}\n```" repo_url: https://huggingface.co/MTSAIR/MultiVerse_70B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|arc:challenge|25_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|arc:challenge|25_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-28T11-20-18.515649.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|gsm8k|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|gsm8k|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hellaswag|10_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hellaswag|10_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-28T11-18-17.303683.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-28T11-20-18.515649.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-management|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-management|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-28T11-20-18.515649.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|truthfulqa:mc|0_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|truthfulqa:mc|0_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-28T11-20-18.515649.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_28T11_18_17.303683 path: - '**/details_harness|winogrande|5_2024-03-28T11-18-17.303683.parquet' - split: 2024_03_28T11_20_18.515649 path: - '**/details_harness|winogrande|5_2024-03-28T11-20-18.515649.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-28T11-20-18.515649.parquet' - config_name: results data_files: - split: 2024_03_28T11_18_17.303683 path: - results_2024-03-28T11-18-17.303683.parquet - split: 2024_03_28T11_20_18.515649 path: - results_2024-03-28T11-20-18.515649.parquet - split: latest path: - results_2024-03-28T11-20-18.515649.parquet --- # Dataset Card for Evaluation run of MTSAIR/MultiVerse_70B  Dataset automatically created during the evaluation run of model [MTSAIR/MultiVerse_70B](https://huggingface.co/MTSAIR/MultiVerse_70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_MTSAIR__MultiVerse_70B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-28T11:20:18.515649](https://huggingface.co/datasets/open-llm-leaderboard/details_MTSAIR__MultiVerse_70B/blob/main/results_2024-03-28T11-20-18.515649.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.7830598805961457, "acc_stderr": 0.027571176888417693, "acc_norm": 0.7846693275609173, "acc_norm_stderr": 0.028121251178584224, "mc1": 0.6499388004895961, "mc1_stderr": 0.016697949420151022, "mc2": 0.7508968077654237, "mc2_stderr": 0.014534916537858438 }, "harness|arc:challenge|25": { "acc": 0.7636518771331058, "acc_stderr": 0.012414960524301822, "acc_norm": 0.7858361774744027, "acc_norm_stderr": 0.01198838320596649 }, "harness|hellaswag|10": { "acc": 0.7490539733120892, "acc_stderr": 0.0043267144532667355, "acc_norm": 0.8974307906791475, "acc_norm_stderr": 0.0030277534195929483 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8881578947368421, "acc_stderr": 0.02564834125169361, "acc_norm": 0.8881578947368421, "acc_norm_stderr": 0.02564834125169361 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8528301886792453, "acc_stderr": 0.02180412613479737, "acc_norm": 0.8528301886792453, "acc_norm_stderr": 0.02180412613479737 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9236111111111112, "acc_stderr": 0.022212203938345918, "acc_norm": 0.9236111111111112, "acc_norm_stderr": 0.022212203938345918 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7630057803468208, "acc_stderr": 0.032424147574830975, "acc_norm": 0.7630057803468208, "acc_norm_stderr": 0.032424147574830975 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5294117647058824, "acc_stderr": 0.049665709039785295, "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.049665709039785295 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.8468085106382979, "acc_stderr": 0.023545179061675203, "acc_norm": 0.8468085106382979, "acc_norm_stderr": 0.023545179061675203 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583706, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583706 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.8, "acc_stderr": 0.0333333333333333, "acc_norm": 0.8, "acc_norm_stderr": 0.0333333333333333 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6957671957671958, "acc_stderr": 0.023695415009463087, "acc_norm": 0.6957671957671958, "acc_norm_stderr": 0.023695415009463087 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5476190476190477, "acc_stderr": 0.044518079590553275, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.044518079590553275 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8870967741935484, "acc_stderr": 0.018003603325863614, "acc_norm": 0.8870967741935484, "acc_norm_stderr": 0.018003603325863614 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.7142857142857143, "acc_stderr": 0.03178529710642751, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.03178529710642751 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706467, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706467 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9292929292929293, "acc_stderr": 0.01826310542019948, "acc_norm": 0.9292929292929293, "acc_norm_stderr": 0.01826310542019948 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9844559585492227, "acc_stderr": 0.008927492715084334, "acc_norm": 0.9844559585492227, "acc_norm_stderr": 0.008927492715084334 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8025641025641026, "acc_stderr": 0.02018264696867485, "acc_norm": 0.8025641025641026, "acc_norm_stderr": 0.02018264696867485 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.5037037037037037, "acc_stderr": 0.03048470166508437, "acc_norm": 0.5037037037037037, "acc_norm_stderr": 0.03048470166508437 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8529411764705882, "acc_stderr": 0.023005459446673936, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.023005459446673936 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5894039735099338, "acc_stderr": 0.04016689594849928, "acc_norm": 0.5894039735099338, "acc_norm_stderr": 0.04016689594849928 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9357798165137615, "acc_stderr": 0.010510494713201405, "acc_norm": 0.9357798165137615, "acc_norm_stderr": 0.010510494713201405 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.7129629629629629, "acc_stderr": 0.03085199299325701, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.03085199299325701 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658928, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658928 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9113924050632911, "acc_stderr": 0.018498315206865384, "acc_norm": 0.9113924050632911, "acc_norm_stderr": 0.018498315206865384 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8161434977578476, "acc_stderr": 0.025998379092356513, "acc_norm": 0.8161434977578476, "acc_norm_stderr": 0.025998379092356513 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8549618320610687, "acc_stderr": 0.030884661089515375, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.030884661089515375 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8925619834710744, "acc_stderr": 0.028268812192540616, "acc_norm": 0.8925619834710744, "acc_norm_stderr": 0.028268812192540616 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8703703703703703, "acc_stderr": 0.03247224389917947, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.03247224389917947 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.7053571428571429, "acc_stderr": 0.0432704093257873, "acc_norm": 0.7053571428571429, "acc_norm_stderr": 0.0432704093257873 }, "harness|hendrycksTest-management|5": { "acc": 0.8737864077669902, "acc_stderr": 0.03288180278808628, "acc_norm": 0.8737864077669902, "acc_norm_stderr": 0.03288180278808628 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253874, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253874 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.87, "acc_stderr": 0.03379976689896308, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896308 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9233716475095786, "acc_stderr": 0.00951217069932386, "acc_norm": 0.9233716475095786, "acc_norm_stderr": 0.00951217069932386 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8497109826589595, "acc_stderr": 0.01923931878390472, "acc_norm": 0.8497109826589595, "acc_norm_stderr": 0.01923931878390472 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.823463687150838, "acc_stderr": 0.012751770640520499, "acc_norm": 0.823463687150838, "acc_norm_stderr": 0.012751770640520499 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8562091503267973, "acc_stderr": 0.020091188936043725, "acc_norm": 0.8562091503267973, "acc_norm_stderr": 0.020091188936043725 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.842443729903537, "acc_stderr": 0.020692237273583984, "acc_norm": 0.842443729903537, "acc_norm_stderr": 0.020692237273583984 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8734567901234568, "acc_stderr": 0.018498600558790906, "acc_norm": 0.8734567901234568, "acc_norm_stderr": 0.018498600558790906 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6631205673758865, "acc_stderr": 0.02819553487396673, "acc_norm": 0.6631205673758865, "acc_norm_stderr": 0.02819553487396673 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6323337679269883, "acc_stderr": 0.012314845910071703, "acc_norm": 0.6323337679269883, "acc_norm_stderr": 0.012314845910071703 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8529411764705882, "acc_stderr": 0.02151396405285963, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.02151396405285963 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8300653594771242, "acc_stderr": 0.015194153113184724, "acc_norm": 0.8300653594771242, "acc_norm_stderr": 0.015194153113184724 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03955932861795833, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03955932861795833 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8163265306122449, "acc_stderr": 0.024789071332007646, "acc_norm": 0.8163265306122449, "acc_norm_stderr": 0.024789071332007646 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101716, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101716 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.95, "acc_stderr": 0.021904291355759026, "acc_norm": 0.95, "acc_norm_stderr": 0.021904291355759026 }, "harness|hendrycksTest-virology|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8713450292397661, "acc_stderr": 0.025679342723276894, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276894 }, "harness|truthfulqa:mc|0": { "mc1": 0.6499388004895961, "mc1_stderr": 0.016697949420151022, "mc2": 0.7508968077654237, "mc2_stderr": 0.014534916537858438 }, "harness|winogrande|5": { "acc": 0.8737174427782163, "acc_stderr": 0.009335559129908452 }, "harness|gsm8k|5": { "acc": 0.7680060652009097, "acc_stderr": 0.011626873175092412 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

veerav96/sokoto_real

--- license: apache-2.0 ---

GabrielTOP/Matheus

--- license: openrail ---

wangxinze/verilog_prepare

--- license: apache-2.0 ---

when2rl/OpenHermesPreferences_reformatted

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: other_info struct: - name: candidate_policies sequence: string - name: candidates_completions sequence: string - name: category dtype: string - name: chosen_policy dtype: string - name: chosen_rank dtype: int64 - name: rank_str dtype: string - name: ranks sequence: int64 - name: rejected_policy dtype: string - name: rejected_rank dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 9078591768 num_examples: 989490 download_size: 4430641969 dataset_size: 9078591768 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for OpenHermesPreferences_reformatted  This is a reformatted version of argilla's OpenHermesPreference: 1. reformatted the daatset to be consistent with ultrafeedback_binarized. ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

juancopi81/orca-math-word-problems-100020_110022

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 10803481 num_examples: 10002 download_size: 3904854 dataset_size: 10803481 configs: - config_name: default data_files: - split: train path: data/train-* ---

dipanjanS/imdb_sentiment_finetune_dataset

--- dataset_info: features: - name: review dtype: string - name: sentiment dtype: int64 splits: - name: train num_bytes: 2588807 num_examples: 2000 - name: validation num_bytes: 2667965 num_examples: 2000 - name: test num_bytes: 21177655 num_examples: 16000 download_size: 17194624 dataset_size: 26434427 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

adityarra07/ATC_test_2_noise

--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 454815537 num_examples: 871 download_size: 455105235 dataset_size: 454815537 --- # Dataset Card for "ATC_test_2_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/tsurumaki_kokoro_bangdream

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tsurumaki_kokoro (BanG Dream!) This is the dataset of tsurumaki_kokoro (BanG Dream!), containing 500 images and their tags. The core tags of this character are `blonde_hair, bangs, long_hair, yellow_eyes, sidelocks, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 748.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 413.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1275 | 906.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 652.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1275 | 1.30 GiB | [Download](https://huggingface.co/datasets/CyberHarem/tsurumaki_kokoro_bangdream/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/tsurumaki_kokoro_bangdream', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | looking_at_viewer, short_sleeves, wrist_cuffs, 1girl, earrings, hair_bow, midriff, smile, solo, blush, thighhighs, crop_top, navel, necklace, blue_skirt, multicolored_skirt, open_mouth, belt, boots, choker, frilled_skirt, layered_skirt, multicolored_shirt, polka_dot, see-through_sleeves, shoes, socks, white_background, white_footwear | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, earrings, solo, black_headwear, hat_bow, looking_at_viewer, pom_pom_(clothes), red_bowtie, top_hat, cleavage, medium_breasts, polka_dot_bow, open_mouth, blush, frills, smiley_face, black_shorts, :d, confetti, holding, short_shorts, teeth, white_background, back_bow | | 2 | 10 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | frills, looking_at_viewer, 1girl, blush, solo, confetti, earrings, white_gloves, hair_bow, open_mouth, star_(symbol), :d, red_bowtie, upper_body, ribbon, string_of_flags, twintails, balloon, blue_bow, corset, short_sleeves, striped_bowtie, top_hat | | 3 | 22 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, solo, looking_at_viewer, blush, bowtie, open_mouth, earrings, wrist_cuffs, frills, choker, fur-trimmed_capelet, striped_bow, mini_crown, navel, ribbon, blue_bow, red_capelet, :d, sparkle, midriff, white_background, one_eye_closed, short_sleeves, upper_body | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, epaulettes, looking_at_viewer, open_mouth, shako_cap, sleeveless, solo, upper_body, :d, band_uniform, blush, sash, wrist_cuffs, upper_teeth_only | | 5 | 13 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, :d, looking_at_viewer, open_mouth, solo, white_skirt, band_uniform, blush, epaulettes, wrist_cuffs, shako_cap, sleeveless_shirt, teeth, thighhighs, medium_breasts, sash, frilled_skirt, red_footwear, armpits, standing, thigh_boots, white_background, cowboy_shot, simple_background, star_(symbol) | | 6 | 32 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, solo, looking_at_viewer, short_sleeves, blush, red_shirt, striped_shirt, smile, collarbone, open_mouth, simple_background, white_background, overall_shorts, upper_body, medium_breasts, teeth | | 7 | 6 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, :d, blush, long_sleeves, looking_at_viewer, open_mouth, sheep_horns, sleep_mask, solo, star_(symbol), upper_teeth_only, bow, hair_flower, mask_on_head, headset, sparkle, apron, arms_up, center_frills, frilled_sleeves, pink_rose, ribbon, striped, upper_body | | 8 | 24 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, solo, white_dress, looking_at_viewer, sleeveless_dress, sun_hat, sundress, blush, day, outdoors, straw_hat, sunflower, open_mouth, collarbone, :d, frills, hat_flower, blue_sky, cloud, medium_breasts, cleavage, upper_body, upper_teeth_only | | 9 | 10 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, brown_dress, hanasakigawa_school_uniform, long_sleeves, looking_at_viewer, neck_ribbon, red_ribbon, sailor_dress, solo, double-breasted, white_sailor_collar, blush, open_mouth, white_background, simple_background, pleated_dress, one_eye_closed, teeth, :d, ;d, bow | | 10 | 10 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, looking_at_viewer, pleated_skirt, serafuku, short_sleeves, solo, white_skirt, blush, hanasakigawa_school_uniform, white_sailor_collar, white_background, blue_neckerchief, blue_shirt, open_mouth, simple_background, miniskirt, collarbone, :d | | 11 | 5 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, earrings, smile, solo, star_(symbol), looking_at_viewer, beret, blush, bowtie, frilled_shirt_collar, hat_bow, short_sleeves, upper_body, alternate_hairstyle, argyle, center_frills, christmas, cleavage, constellation_print, dress, frilled_sleeves, hairclip, headset, long_sleeves, red_headwear, ribbon, striped_bow, twintails | | 12 | 5 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | 1girl, blush, hair_flower, hair_ribbon, solo, sunflower, twintails, :d, bracelet, looking_at_viewer, open_mouth, alternate_hairstyle, blue_dress, blue_ribbon, frills, holding, necklace, beachball, bow, day, hairband, polka_dot, sky, sparkle, upper_body, white_background | | 13 | 8 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | 1girl, looking_at_viewer, skirt, solo, twintails, white_gloves, handcuffs, midriff, navel, police_hat, star_(symbol), blue_headwear, blush, short_sleeves, crop_top, hair_ribbon, open_mouth, peaked_cap, :d, cropped_jacket, detached_collar, frills, orange_necktie, short_necktie, belt, cleavage, knee_boots, open_clothes, shirt, striped, white_background, white_jacket, white_thighhighs | | 14 | 14 | ![](samples/14/clu14-sample0.png) | ![](samples/14/clu14-sample1.png) | ![](samples/14/clu14-sample2.png) | ![](samples/14/clu14-sample3.png) | ![](samples/14/clu14-sample4.png) | 1girl, kimono, obi, blush, looking_at_viewer, solo, wide_sleeves, floral_print, hair_flower, long_sleeves, ponytail, open_mouth, hair_bow, :d, holding, detached_sleeves, frills, red_flower, red_bow, sky, teeth, upper_body | | 15 | 16 | ![](samples/15/clu15-sample0.png) | ![](samples/15/clu15-sample1.png) | ![](samples/15/clu15-sample2.png) | ![](samples/15/clu15-sample3.png) | ![](samples/15/clu15-sample4.png) | blush, looking_at_viewer, 1girl, solo, nipples, open_mouth, completely_nude, pussy, collarbone, navel, simple_background, barefoot, large_breasts, medium_breasts, stomach, white_background, :d, fingernails, full_body, anus, armpits, blunt_bangs, cleft_of_venus, heart, toes, uncensored | | 16 | 5 | ![](samples/16/clu16-sample0.png) | ![](samples/16/clu16-sample1.png) | ![](samples/16/clu16-sample2.png) | ![](samples/16/clu16-sample3.png) | ![](samples/16/clu16-sample4.png) | 1boy, 1girl, blush, hetero, medium_breasts, open_mouth, solo_focus, sweat, completely_nude, saliva, sex_from_behind, heart, heavy_breathing, nipples, standing_sex, tears, upper_teeth_only, vaginal, :d, arm_support, ass, bent_over, cum, from_side, indoors, large_breasts, looking_at_viewer, looking_to_the_side, mosaic_censoring, motion_lines, penis, torso_grab, trembling | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | short_sleeves | wrist_cuffs | 1girl | earrings | hair_bow | midriff | smile | solo | blush | thighhighs | crop_top | navel | necklace | blue_skirt | multicolored_skirt | open_mouth | belt | boots | choker | frilled_skirt | layered_skirt | multicolored_shirt | polka_dot | see-through_sleeves | shoes | socks | white_background | white_footwear | black_headwear | hat_bow | pom_pom_(clothes) | red_bowtie | top_hat | cleavage | medium_breasts | polka_dot_bow | frills | smiley_face | black_shorts | :d | confetti | holding | short_shorts | teeth | back_bow | white_gloves | star_(symbol) | upper_body | ribbon | string_of_flags | twintails | balloon | blue_bow | corset | striped_bowtie | bowtie | fur-trimmed_capelet | striped_bow | mini_crown | red_capelet | sparkle | one_eye_closed | epaulettes | shako_cap | sleeveless | band_uniform | sash | upper_teeth_only | white_skirt | sleeveless_shirt | red_footwear | armpits | standing | thigh_boots | cowboy_shot | simple_background | red_shirt | striped_shirt | collarbone | overall_shorts | long_sleeves | sheep_horns | sleep_mask | bow | hair_flower | mask_on_head | headset | apron | arms_up | center_frills | frilled_sleeves | pink_rose | striped | white_dress | sleeveless_dress | sun_hat | sundress | day | outdoors | straw_hat | sunflower | hat_flower | blue_sky | cloud | brown_dress | hanasakigawa_school_uniform | neck_ribbon | red_ribbon | sailor_dress | double-breasted | white_sailor_collar | pleated_dress | ;d | pleated_skirt | serafuku | blue_neckerchief | blue_shirt | miniskirt | beret | frilled_shirt_collar | alternate_hairstyle | argyle | christmas | constellation_print | dress | hairclip | red_headwear | hair_ribbon | bracelet | blue_dress | blue_ribbon | beachball | hairband | sky | skirt | handcuffs | police_hat | blue_headwear | peaked_cap | cropped_jacket | detached_collar | orange_necktie | short_necktie | knee_boots | open_clothes | shirt | white_jacket | white_thighhighs | kimono | obi | wide_sleeves | floral_print | ponytail | detached_sleeves | red_flower | red_bow | nipples | completely_nude | pussy | barefoot | large_breasts | stomach | fingernails | full_body | anus | blunt_bangs | cleft_of_venus | heart | toes | uncensored | 1boy | hetero | solo_focus | sweat | saliva | sex_from_behind | heavy_breathing | standing_sex | tears | vaginal | arm_support | ass | bent_over | cum | from_side | indoors | looking_to_the_side | mosaic_censoring | motion_lines | penis | torso_grab | trembling | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------------------|:----------------|:--------------|:--------|:-----------|:-----------|:----------|:--------|:-------|:--------|:-------------|:-----------|:--------|:-----------|:-------------|:---------------------|:-------------|:-------|:--------|:---------|:----------------|:----------------|:---------------------|:------------|:----------------------|:--------|:--------|:-------------------|:-----------------|:-----------------|:----------|:--------------------|:-------------|:----------|:-----------|:-----------------|:----------------|:---------|:--------------|:---------------|:-----|:-----------|:----------|:---------------|:--------|:-----------|:---------------|:----------------|:-------------|:---------|:------------------|:------------|:----------|:-----------|:---------|:-----------------|:---------|:----------------------|:--------------|:-------------|:--------------|:----------|:-----------------|:-------------|:------------|:-------------|:---------------|:-------|:-------------------|:--------------|:-------------------|:---------------|:----------|:-----------|:--------------|:--------------|:--------------------|:------------|:----------------|:-------------|:-----------------|:---------------|:--------------|:-------------|:------|:--------------|:---------------|:----------|:--------|:----------|:----------------|:------------------|:------------|:----------|:--------------|:-------------------|:----------|:-----------|:------|:-----------|:------------|:------------|:-------------|:-----------|:--------|:--------------|:------------------------------|:--------------|:-------------|:---------------|:------------------|:----------------------|:----------------|:-----|:----------------|:-----------|:-------------------|:-------------|:------------|:--------|:-----------------------|:----------------------|:---------|:------------|:----------------------|:--------|:-----------|:---------------|:--------------|:-----------|:-------------|:--------------|:------------|:-----------|:------|:--------|:------------|:-------------|:----------------|:-------------|:-----------------|:------------------|:-----------------|:----------------|:-------------|:---------------|:--------|:---------------|:-------------------|:---------|:------|:---------------|:---------------|:-----------|:-------------------|:-------------|:----------|:----------|:------------------|:--------|:-----------|:----------------|:----------|:--------------|:------------|:-------|:--------------|:-----------------|:--------|:-------|:-------------|:-------|:---------|:-------------|:--------|:---------|:------------------|:------------------|:---------------|:--------|:----------|:--------------|:------|:------------|:------|:------------|:----------|:----------------------|:-------------------|:---------------|:--------|:-------------|:------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | X | X | | | | X | X | | | | | | | X | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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| | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 6 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | X | | | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 24 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | 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| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 10 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | X | | X | | | | | X | X | | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 11 | 5 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | X | X | | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | X | X | X | | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 12 | 5 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | X | | | X | | | | | X | X | | | | X | | | X | | | | | | | X | | | | X | | | | | | | | | | X | | | X | | X | | | | | | X | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 13 | 8 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | X | X | | X | | | X | | X | X | | X | X | | | | X | X | | | | | | | | | | X | | | | | | | X | | | X | | | X | | | | | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 14 | 14 | ![](samples/14/clu14-sample0.png) | ![](samples/14/clu14-sample1.png) | ![](samples/14/clu14-sample2.png) | ![](samples/14/clu14-sample3.png) | ![](samples/14/clu14-sample4.png) | X | | | X | | X | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | | X | | X | | X | | | | X | | | | | | | | | | | | | | | | 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shidowake/Doctor-Shotgun_capybara-sharegpt_subset_split_5

--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 9064100.571348244 num_examples: 2001 download_size: 4725927 dataset_size: 9064100.571348244 configs: - config_name: default data_files: - split: train path: data/train-* ---

rhaymison/questions_answers_geo_nord

--- language: - pt license: apache-2.0 size_categories: - 10K<n<100K task_categories: - text-generation pretty_name: geografia Nordestina dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 11397122.0 num_examples: 44004 download_size: 5647644 dataset_size: 11397122.0 configs: - config_name: default data_files: - split: train path: data/train-* tags: - portugues - nordeste - mixtral ---

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d718bf95

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1335 dataset_size: 188 --- # Dataset Card for "d718bf95" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)