davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
sam1120/dropoff-utcustom-TRAIN	--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 142272068.0 num_examples: 50 download_size: 43507500 dataset_size: 142272068.0 --- # Dataset Card for "dropoff-utcustom-TRAIN" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mayank082000/Multilingual_Sentences_with_Sentences	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 509463 num_examples: 2289 download_size: 53713 dataset_size: 509463 configs: - config_name: default data_files: - split: train path: data/train-* ---
aisyahhrazak/crawl-malaysiagazette	--- language: - ms --- About - Data scraped from https://malaysiagazette.com/ - on 4.7.2023
dipteshkanojia/t5-qe-2023-indic-multi-da	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: task dtype: string splits: - name: train num_bytes: 47647871 num_examples: 58940 download_size: 18352409 dataset_size: 47647871 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "t5-qe-2023-indic-multi-da" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955857	--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_dev eval_info: task: text_zero_shot_classification model: facebook/opt-2.7b metrics: [] dataset_name: mathemakitten/winobias_antistereotype_dev dataset_config: mathemakitten--winobias_antistereotype_dev dataset_split: validation 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-2.7b * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
liuyanchen1015/MULTI_VALUE_qqp_drop_copula_be_AP	--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 574829 num_examples: 3667 - name: test num_bytes: 6086644 num_examples: 38655 - name: train num_bytes: 5201008 num_examples: 32930 download_size: 7404705 dataset_size: 11862481 --- # Dataset Card for "MULTI_VALUE_qqp_drop_copula_be_AP" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
paul-w-qs/contracts_v6	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: N_ROWS dtype: int64 - name: N_COLS dtype: int64 - name: FONT_SIZE dtype: int64 - name: FONT_NAME dtype: string - name: BORDER_THICKNESS dtype: int64 - name: TABLE_STYLE dtype: string - name: NOISED dtype: bool - name: LABEL_NOISE dtype: bool - name: JSON_LABEL dtype: string splits: - name: train num_bytes: 360922904.016 num_examples: 5364 download_size: 360853881 dataset_size: 360922904.016 --- # Dataset Card for "contracts_v6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
onurSakar/GYM-Exercise	--- language: - en dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 839599 num_examples: 1660 download_size: 293713 dataset_size: 839599 configs: - config_name: default data_files: - split: train path: data/train-* ---
irds/wikir_it16k	--- pretty_name: '`wikir/it16k`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `wikir/it16k` The `wikir/it16k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/wikir#wikir/it16k). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=503,012 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/wikir_it16k', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Frej2020Wikir, title={WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={LREC}, year={2020} } @inproceedings{Frej2020MlWikir, title={MLWIKIR: A Python Toolkit for Building Large-scale Wikipedia-based Information Retrieval Datasets in Chinese, English, French, Italian, Japanese, Spanish and More}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={CIRCLE}, year={2020} } ```
uobinxiao/open_tables_icttd_for_table_detection	--- license: apache-2.0 --- Datasets for the paper "Revisiting Table Detection Datasets for Visually Rich Documents" (https://arxiv.org/abs/2305.04833). ## License Since this dataset is built on several open datasets and open documents, users should also adhere to the licenses of these publicly available datasets and documents.
allganize/rag-ko	--- dataset_info: features: - name: index dtype: int64 - name: system dtype: string - name: human dtype: string - name: answer dtype: string - name: answer_position dtype: int64 - name: answer_context_title dtype: string - name: answer_context_summary dtype: string splits: - name: train num_bytes: 914673 num_examples: 200 - name: test num_bytes: 914673 num_examples: 200 download_size: 2352755 dataset_size: 1829346 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* language: - ko --- # rag-ko - `rag-ko` 데이터는 금융 도메인의 RAG(Retrieval Augmented Generation, 검색증강생성) 데이터셋입니다. RAG를 진행할 수 있도록 Golden Context 1개와 Negative Context 2개가 제공되고 Golen Context에 관련된 질문과 그 답변이 주어집니다. - 데이터의 컨텍스트는 위키피디아와 공공기관의 금융보고서, 금융용어집등을 대상으로 만듭니다. 이후 GPT-4를 이용하여 해당 컨텍스트에 대한 질문과 답변을 생성하고 이를 각각, Golden Context, Question, Golden Answer로 삼습니다. - 이후 컨텍스트 집합에서 Question으로 검색(BM25)했을때 Golden Context를 제외하고 점수가 높은 두개의 Context를 선택합니다. 이를 Negative Context로 삼습니다. - Golden Context, 2개의 Negative Context, Question과 Instruction을 모두 포함했을때 3K Token(Llama2 tokenizer기준)을 넘지 않도록 Allganize Summerizer(사내 추출형 요약엔진)을 이용해 요약합니다. - 이후 사람이 검수 완료한 200개의 데이터셋입니다. ### 데이터 출처 - [한국어 wikipedia 금융 분류](https://ko.wikipedia.org/wiki/%EB%B6%84%EB%A5%98:%EA%B8%88%EC%9C%B5) - [한국은행 경제연구 보고서](https://www.bok.or.kr/portal/bbs/P0002454/list.do?menuNo=200431) - [한국은행 해외경제 포커스](https://www.bok.or.kr/portal/bbs/P0000545/list.do?menuNo=200437) ### 데이터 예시 ``` { 'conversation_id': 'financial_mmlu_0', 'conversations': array([ { 'from': 'human', 'value': '금리의 종류에 대한 설명으로 바르지 않은 것은?\n 1. 변동금리는 시장금리 변동에 따른 위험을 자금공급자가 부담하게 된다\n 2. 피셔방정식에 의하면 실질금리는 명목금리에서 기대인플레이션을 차감하면\n 구할 수 있다.\n 3. 복리는 원금에 대한 이자뿐 아니라 이자에 대한 이자도 함께 계산하는 방법이\n다.\n 4. 실효금리는 이자지급방법, 상환방법, 수수료, 세금 등을 감안한 후 차입자가\n실질적으로 부담하는 순자금조달비용을 말한다.\n 5. 채권시장에서는 금리보다 수익률이라는 용어를 더 많이 사용한다.' }, { 'from': 'gpt', 'value': '1' } ], dtype=object) } ``` License - Wikipedia: CC BY-SA 4.0 - [한국은행 저작권 보호방침](https://www.bok.or.kr/portal/main/contents.do?menuNo=200228)
open-llm-leaderboard/details_ehartford__WizardLM-13B-Uncensored	--- pretty_name: Evaluation run of ehartford/WizardLM-13B-Uncensored dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ehartford/WizardLM-13B-Uncensored](https://huggingface.co/ehartford/WizardLM-13B-Uncensored)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 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 agregated 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_ehartford__WizardLM-13B-Uncensored\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-18T07:53:55.275923](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__WizardLM-13B-Uncensored/blob/main/results_2023-10-18T07-53-55.275923.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 \"em\": 0.20994127516778524,\n\ \ \"em_stderr\": 0.004170789326061059,\n \"f1\": 0.3040310402684571,\n\ \ \"f1_stderr\": 0.004210803460550511,\n \"acc\": 0.3630369207736123,\n\ \ \"acc_stderr\": 0.00835492026013406\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.20994127516778524,\n \"em_stderr\": 0.004170789326061059,\n\ \ \"f1\": 0.3040310402684571,\n \"f1_stderr\": 0.004210803460550511\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.02047005307050796,\n \ \ \"acc_stderr\": 0.0039004133859157192\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7056037884767167,\n \"acc_stderr\": 0.0128094271343524\n\ \ }\n}\n```" repo_url: https://huggingface.co/ehartford/WizardLM-13B-Uncensored 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_07_19T19_00_32.745864 path: - '/details_harness\|arc:challenge\|25_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T19:00:32.745864.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_18T07_53_55.275923 path: - '/details_harness\|drop\|3_2023-10-18T07-53-55.275923.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-18T07-53-55.275923.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_18T07_53_55.275923 path: - '/details_harness\|gsm8k\|5_2023-10-18T07-53-55.275923.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-18T07-53-55.275923.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hellaswag\|10_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T19:00:32.745864.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T19_00_32.745864 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T19:00:32.745864.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_18T07_53_55.275923 path: - '/details_harness\|winogrande\|5_2023-10-18T07-53-55.275923.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-18T07-53-55.275923.parquet' - config_name: results data_files: - split: 2023_07_19T19_00_32.745864 path: - results_2023-07-19T19:00:32.745864.parquet - split: 2023_10_18T07_53_55.275923 path: - results_2023-10-18T07-53-55.275923.parquet - split: latest path: - results_2023-10-18T07-53-55.275923.parquet --- # Dataset Card for Evaluation run of ehartford/WizardLM-13B-Uncensored ## Dataset Description - Homepage: - Repository: https://huggingface.co/ehartford/WizardLM-13B-Uncensored - 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 [ehartford/WizardLM-13B-Uncensored](https://huggingface.co/ehartford/WizardLM-13B-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 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 agregated 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_ehartford__WizardLM-13B-Uncensored", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-18T07:53:55.275923](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__WizardLM-13B-Uncensored/blob/main/results_2023-10-18T07-53-55.275923.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": { "em": 0.20994127516778524, "em_stderr": 0.004170789326061059, "f1": 0.3040310402684571, "f1_stderr": 0.004210803460550511, "acc": 0.3630369207736123, "acc_stderr": 0.00835492026013406 }, "harness\|drop\|3": { "em": 0.20994127516778524, "em_stderr": 0.004170789326061059, "f1": 0.3040310402684571, "f1_stderr": 0.004210803460550511 }, "harness\|gsm8k\|5": { "acc": 0.02047005307050796, "acc_stderr": 0.0039004133859157192 }, "harness\|winogrande\|5": { "acc": 0.7056037884767167, "acc_stderr": 0.0128094271343524 } } ``` ### 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]
gagan3012/NewArOCRDatasetv5	--- 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: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 744898565.786 num_examples: 38219 - name: validation num_bytes: 14180587.0 num_examples: 425 - name: test num_bytes: 13690842.0 num_examples: 425 download_size: 692203880 dataset_size: 772769994.786 --- # Dataset Card for "NewArOCRDatasetv5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Baidicoot/openhermes-base64	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 109420486.63468009 num_examples: 210374 download_size: 66695207 dataset_size: 109420486.63468009 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/juno_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of juno/ジュノー/天后 (Azur Lane) This is the dataset of juno/ジュノー/天后 (Azur Lane), containing 24 images and their tags. The core tags of this character are `pink_hair, long_hair, crown, bangs, mini_crown, ribbon, twintails, pink_eyes, bow, purple_eyes`, 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 \| 24 \| 29.79 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/juno_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 24 \| 20.21 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/juno_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 59 \| 42.83 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/juno_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 24 \| 28.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/juno_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 59 \| 57.92 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/juno_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/juno_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 \| 24 \| ![](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, 1girl, solo, open_mouth, blush, collarbone, bare_shoulders, :d, dress, long_sleeves, simple_background, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| looking_at_viewer \| 1girl \| solo \| open_mouth \| blush \| collarbone \| bare_shoulders \| :d \| dress \| long_sleeves \| simple_background \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------\|:--------\|:-------\|:-------------\|:--------\|:-------------\|:-----------------\|:-----\|:--------\|:---------------\|:--------------------\|:-------------------\| \| 0 \| 24 \| ![](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 \|
datahrvoje/twitter_dataset_1712730747	--- 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: 18262 num_examples: 45 download_size: 14179 dataset_size: 18262 configs: - config_name: default data_files: - split: train path: data/train-* ---
GmoData/ui5_db	--- license: cc-by-4.0 ---
freshpearYoon/vr_train_free_24	--- 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: 6305883116 num_examples: 10000 download_size: 1075121807 dataset_size: 6305883116 configs: - config_name: default data_files: - split: train path: data/train-* ---
batelidan/dataset-classifcation-tv	--- dataset_info: features: - name: /content/drive/MyDrive/Wave/20220810-194349-3reality-00E93AAC59A5.wav dtype: string - name: TV dtype: string splits: - name: train num_bytes: 128533 num_examples: 1627 download_size: 23156 dataset_size: 128533 --- # Dataset Card for "dataset-classifcation-tv" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yonathanstwn/ecolindo	--- dataset_info: features: - name: translation struct: - name: english dtype: string - name: colloquial_indo dtype: string - name: formal_indo dtype: string splits: - name: train num_bytes: 70159189 num_examples: 672581 - name: test num_bytes: 202512 num_examples: 2000 - name: validation num_bytes: 202111 num_examples: 2000 download_size: 51847840 dataset_size: 70563812 task_categories: - translation language: - id - en --- # English to Colloquial Indonesian Dataset (EColIndo) First-ever large-scale high-quality English to Colloquial Indonesian dataset. Fully generated from ChatGPT Zero-Shot Translation. Author: Yonathan Setiawan
cfilt/IITB-MonoDoc	--- license: cc-by-4.0 task_categories: - text-generation language: - hi - mr - gu - sa - ta - te - ml - ne - as - bn - ks - or - pa - ur - sd - kn size_categories: - 10B<n<100B tags: - language-modeling - llm - clm ---
israel/MT-llama	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt_header dtype: string - name: datasource dtype: string splits: - name: train num_bytes: 84762357.7818897 num_examples: 200000 - name: validation num_bytes: 1209980 num_examples: 1994 - name: test num_bytes: 1306100 num_examples: 2024 download_size: 23384531 dataset_size: 87278437.7818897 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
benny-abhishek/AB_Speech	--- license: mit task_categories: - automatic-speech-recognition - text-to-speech language: - en tags: - asr - tts - ser pretty_name: ab_speech size_categories: - n<1K --- Contains voice of a single male speaker spoken with emphasis on all phonemes.
stuheart86/imageclassification	--- license: creativeml-openrail-m ---
bgspaditya/phishing-dataset	--- license: mit ---
open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B	--- pretty_name: Evaluation run of PulsarAI/Nebula-v2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [PulsarAI/Nebula-v2-7B](https://huggingface.co/PulsarAI/Nebula-v2-7B) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 1 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_PulsarAI__Nebula-v2-7B\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T13:58:09.073163](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B/blob/main/results_2023-12-02T13-58-09.073163.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\": 0.3169067475360121,\n\ \ \"acc_stderr\": 0.012815868296721373\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.3169067475360121,\n \"acc_stderr\": 0.012815868296721373\n\ \ }\n}\n```" repo_url: https://huggingface.co/PulsarAI/Nebula-v2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_gsm8k_5 data_files: - split: 2023_12_02T13_58_09.073163 path: - '/details_harness\|gsm8k\|5_2023-12-02T13-58-09.073163.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-02T13-58-09.073163.parquet' - config_name: results data_files: - split: 2023_12_02T13_58_09.073163 path: - results_2023-12-02T13-58-09.073163.parquet - split: latest path: - results_2023-12-02T13-58-09.073163.parquet --- # Dataset Card for Evaluation run of PulsarAI/Nebula-v2-7B ## Dataset Description - Homepage: - Repository: https://huggingface.co/PulsarAI/Nebula-v2-7B - 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 [PulsarAI/Nebula-v2-7B](https://huggingface.co/PulsarAI/Nebula-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 1 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_PulsarAI__Nebula-v2-7B", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T13:58:09.073163](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B/blob/main/results_2023-12-02T13-58-09.073163.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": 0.3169067475360121, "acc_stderr": 0.012815868296721373 }, "harness\|gsm8k\|5": { "acc": 0.3169067475360121, "acc_stderr": 0.012815868296721373 } } ``` ### 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]
Cohere/miracl-ar-queries-22-12	--- annotations_creators: - expert-generated language: - ar multilinguality: - multilingual size_categories: [] source_datasets: [] tags: [] task_categories: - text-retrieval license: - apache-2.0 task_ids: - document-retrieval --- # MIRACL (ar) embedded with cohere.ai `multilingual-22-12` encoder We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model. The query embeddings can be found in [Cohere/miracl-ar-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ar-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-ar-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ar-corpus-22-12). For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus). Dataset info: > MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world. > > The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage. ## Embeddings We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/). ## Loading the dataset In [miracl-ar-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ar-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large. You can either load the dataset like this: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/miracl-ar-corpus-22-12", split="train") ``` Or you can also stream it without downloading it before: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/miracl-ar-corpus-22-12", split="train", streaming=True) for doc in docs: docid = doc['docid'] title = doc['title'] text = doc['text'] emb = doc['emb'] ``` ## Search Have a look at [miracl-ar-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ar-queries-22-12) where we provide the query embeddings for the MIRACL dataset. To search in the documents, you must use dot-product. And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product. A full search example: ```python # Attention! For large datasets, this requires a lot of memory to store # all document embeddings and to compute the dot product scores. # Only use this for smaller datasets. For large datasets, use a vector DB from datasets import load_dataset import torch #Load documents + embeddings docs = load_dataset(f"Cohere/miracl-ar-corpus-22-12", split="train") doc_embeddings = torch.tensor(docs['emb']) # Load queries queries = load_dataset(f"Cohere/miracl-ar-queries-22-12", split="dev") # Select the first query as example qid = 0 query = queries[qid] query_embedding = torch.tensor(queries['emb']) # Compute dot score between query embedding and document embeddings dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1)) top_k = torch.topk(dot_scores, k=3) # Print results print("Query:", query['query']) for doc_id in top_k.indices[0].tolist(): print(docs[doc_id]['title']) print(docs[doc_id]['text']) ``` You can get embeddings for new queries using our API: ```python #Run: pip install cohere import cohere co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :)) texts = ['my search query'] response = co.embed(texts=texts, model='multilingual-22-12') query_embedding = response.embeddings[0] # Get the embedding for the first text ``` ## Performance In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset. We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results. Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted. \| Model \| cohere multilingual-22-12 nDCG@10 \| cohere multilingual-22-12 hit@3 \| ES 8.6.0 nDCG@10 \| ES 8.6.0 acc@3 \| \|---\|---\|---\|---\|---\| \| miracl-ar \| 64.2 \| 75.2 \| 46.8 \| 56.2 \| \| miracl-bn \| 61.5 \| 75.7 \| 49.2 \| 60.1 \| \| miracl-de \| 44.4 \| 60.7 \| 19.6 \| 29.8 \| \| miracl-en \| 44.6 \| 62.2 \| 30.2 \| 43.2 \| \| miracl-es \| 47.0 \| 74.1 \| 27.0 \| 47.2 \| \| miracl-fi \| 63.7 \| 76.2 \| 51.4 \| 61.6 \| \| miracl-fr \| 46.8 \| 57.1 \| 17.0 \| 21.6 \| \| miracl-hi \| 50.7 \| 62.9 \| 41.0 \| 48.9 \| \| miracl-id \| 44.8 \| 63.8 \| 39.2 \| 54.7 \| \| miracl-ru \| 49.2 \| 66.9 \| 25.4 \| 36.7 \| \| Avg \| 51.7 \| 67.5 \| 34.7 \| 46.0 \| Further languages (not supported by Elasticsearch): \| Model \| cohere multilingual-22-12 nDCG@10 \| cohere multilingual-22-12 hit@3 \| \|---\|---\|---\| \| miracl-fa \| 44.8 \| 53.6 \| \| miracl-ja \| 49.0 \| 61.0 \| \| miracl-ko \| 50.9 \| 64.8 \| \| miracl-sw \| 61.4 \| 74.5 \| \| miracl-te \| 67.8 \| 72.3 \| \| miracl-th \| 60.2 \| 71.9 \| \| miracl-yo \| 56.4 \| 62.2 \| \| miracl-zh \| 43.8 \| 56.5 \| \| Avg \| 54.3 \| 64.6 \|
mteb-pt/reddit-clustering	--- configs: - config_name: pt data_files: - split: test path: test* ---
bigscience-data/roots_indic-or_wikisource	--- language: or license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_indic-or_wikisource # wikisource_filtered - Dataset uid: `wikisource_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 2.6306 % of total - 12.7884 % of fr - 19.8886 % of indic-bn - 20.9966 % of indic-ta - 2.3478 % of ar - 4.7068 % of indic-hi - 18.0998 % of indic-te - 1.7155 % of es - 19.4800 % of indic-kn - 9.1737 % of indic-ml - 17.1771 % of indic-mr - 17.1870 % of indic-gu - 70.3687 % of indic-as - 1.0165 % of pt - 7.8642 % of indic-pa - 1.3501 % of vi - 4.9411 % of indic-or - 0.5307 % of ca - 2.3593 % of id - 1.5928 % of eu ### BigScience processing steps #### Filters applied to: fr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-bn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ta - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: ar - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-te - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: es - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-kn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - remove_wiki_mojibake - filter_small_docs_bytes_300 #### Filters applied to: indic-ml - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-mr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-gu - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-as - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-pa - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: vi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-or - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs #### Filters applied to: ca - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: id - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: eu - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs
waifu-research-department/embeddings	--- license: mit --- # Info >Try to include embedding info in the commit description (model, author, artist, images, etc) >Naming: name-object/style
BAAI/COIG	--- license: apache-2.0 arxiv: 2304.07987 language: - zh --- # This is the Chinese Open Instruction Generalist project We propose the Chinese Open Instruction Generalist (COIG) project to maintain a harmless, helpful, and diverse set of Chinese instruction corpora. We welcome all researchers in the community to contribute to the corpus set and collaborate with us. We only release the first chip of COIG to help the Chinese LLMs' development in the exploration stage and appeal to more researchers joining us in building COIG. We introduce a manually verified translated general instruction corpus, a manually annotated exam instruction corpus, a human value alignment instruction corpus, a multi-round counterfactual correction chat corpus, and a leetcode instruction corpus. We provide these new instruction corpora to assist the community with instruction tuning on Chinese LLMs. These instruction corpora are also template workflows for how new Chinese instruction corpora can be built and expanded effectively. It is best to download the individual data files directly that you wish to use instead of using HF load_datasets. All datasets can be downloaded from: https://huggingface.co/datasets/BAAI/COIG/tree/main This dataset card is modified from [OIG](https://huggingface.co/datasets/laion/OIG). ### Translated Instructions (66,858) There are 66,858 instructions in total, which are composed of 1,616 task descriptions in [Super-NaturalInstructions](https://arxiv.org/abs/2204.07705) along with a single instance for each of them, 175 seed tasks in [Self-Instruct](https://arxiv.org/abs/2212.10560), and 66,007 instructions from [Unnatural Instructions](https://arxiv.org/abs/2212.09689). To reduce the cost and further improve the quality of the instruction corpus, we separate the translation procedure into three phases: automatic translation, manual verification, and manual correction. These strict quality verification procedures assure the reliability of the translated corpus. ### Exam Instructions (63,532) The Chinese National College Entrance Examination, Middle School Entrance Examinations, and Civil Servant Examination are the main Chinese commonsense tests. These exams contain various question formats and detailed analysis that can be used as the Chain-of-Thought (CoT) corpus. We extract six informative elements from original exam questions, including instruction, question context, question, answer, answer analysis, and coarse-grained subject. There are six main coarse-grained subjects: Chinese, English, Politics, Biology, History, and Geology. There are very few Math, Physics, and Chemistry questions in the corpus because these questions are often with complex symbols which are hard to annotate. For many choice questions, we recommend that the researchers utilize this corpus to further post-process it using prompts or post-process it to blank-filling questions to increase the instructions' diversity further. ### Human Value Alignment Instructions (34,471) To respect and reflect the major difference caused by different cultural backgrounds, different from other tasks in COIG that leverage one unified collection of instruction-following samples, we categorize the value alignment data into two separate series: - A set of samples that present shared human values in the Chinese-speaking world. In total, we choose 50 instructions as the augmentation seeds, and produce 3k resulting instructions following samples for general-purpose value alignment in the Chinese-speaking world. - Some additional sets of samples that present regional-culture or country-specific human values. ### Counterfactural Correction Multi-round Chat (13,653) The Counterfactual Correction Multi-round Chat dataset (CCMC) is constructed based on the [CN-DBpedia knowledge graph dataset](https://link.springer.com/chapter/10.1007/978-3-319-60045-1_44) with the aim of alleviating and resolving the pain points of hallucination and factual inconsistency in current LLMs. The CCMC dataset includes 5 rounds of role-playing chat between a student and a teacher, and the corresponding knowledge they refer to. The dataset contains ~13,000 dialogues with an average of 5 rounds per dialogue, resulting in ~65,000 rounds of chat. ### Leetcode Instructions (11,737) Given that the code-related tasks potentially contribute to the ability emergence of LLMs, we argue that code-related tasks aligned with the Chinese natural language should be considered in our datasets. Therefore, we build the Leetcode instructions from a CC-BY-SA-4.0 license [collection](https://github.com/doocs/leetcode) of 2,589 programming questions. The questions contain problem descriptions, multiple programming languages, and explanations (834 questions do not have explanations). ## Support this project Your contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can: Submit Github issues, track issues and help create datasets that need improvement. https://github.com/BAAI-Zlab/COIG ## Update: May 27, 2023 - v0.3: Update counterfactural_correction_multi_round_chat.tar.gz and make sure all round responses can be decoded as json. - v0.2: Update exam_instructions.jsonl, translated_instructions.jsonl and human_value_alignment_instructions_part2.json. - v0.1: Release the five datasets of COIG. ## Disclaimer These datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets. ## License The COIG dataset that is authored by BAAI is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as unnatural instructions data which is licensed under MIT License, or web-crawled data which is used under fair use principles. ## BibTeX & Citation ``` @misc{zhang2023chinese, title={Chinese Open Instruction Generalist: A Preliminary Release}, author={Ge Zhang and Yemin Shi and Ruibo Liu and Ruibin Yuan and Yizhi Li and Siwei Dong and Yu Shu and Zhaoqun Li and Zekun Wang and Chenghua Lin and Wenhao Huang and Jie Fu}, year={2023}, eprint={2304.07987}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
copenlu/scientific-exaggeration-detection	--- annotations_creators: - expert-generated language: - en language_creators: - found license: - gpl-3.0 multilinguality: - monolingual paperswithcode_id: semi-supervised-exaggeration-detection-of pretty_name: Scientific Exaggeration Detection size_categories: - n<1K source_datasets: [] tags: - scientific text - scholarly text - inference - fact checking - misinformation task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification --- # Dataset Card for Scientific Exaggeration Detection ## Dataset Description - Homepage: https://github.com/copenlu/scientific-exaggeration-detection - Repository: https://github.com/copenlu/scientific-exaggeration-detection - Paper: https://aclanthology.org/2021.emnlp-main.845.pdf ### Dataset Summary Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task. ## Dataset Structure The training and test data are derived from the InSciOut studies from [Sumner et al. 2014](https://www.bmj.com/content/349/bmj.g7015) and [Bratton et al. 2019](https://pubmed.ncbi.nlm.nih.gov/31728413/#:~:text=Results%3A%20We%20found%20that%20the,inference%20from%20non%2Dhuman%20studies.). The splits have the following fields: ``` original_file_id: The ID of the original spreadsheet in the Sumner/Bratton data where the annotations are derived from press_release_conclusion: The conclusion sentence from the press release press_release_strength: The strength label for the press release abstract_conclusion: The conclusion sentence from the abstract abstract_strength: The strength label for the abstract exaggeration_label: The final exaggeration label ``` The exaggeration label is one of `same`, `exaggerates`, or `downplays`. The strength label is one of the following: ``` 0: Statement of no relationship 1: Statement of correlation 2: Conditional statement of causation 3: Statement of causation ``` ## Dataset Creation See section 4 of the [paper](https://aclanthology.org/2021.emnlp-main.845.pdf) for details on how the dataset was curated. The original InSciOut data can be found [here](https://figshare.com/articles/dataset/InSciOut/903704) ## Citation ``` @inproceedings{wright2021exaggeration, title={{Semi-Supervised Exaggeration Detection of Health Science Press Releases}}, author={Dustin Wright and Isabelle Augenstein}, booktitle = {Proceedings of EMNLP}, publisher = {Association for Computational Linguistics}, year = 2021 } ``` Thanks to [@dwright37](https://github.com/dwright37) for adding this dataset.
irds/natural-questions	--- pretty_name: '`natural-questions`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `natural-questions` The `natural-questions` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/natural-questions#natural-questions). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=28,390,850 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/natural-questions', 'docs') for record in docs: record # {'doc_id': ..., 'text': ..., 'html': ..., 'start_byte': ..., 'end_byte': ..., 'start_token': ..., 'end_token': ..., 'document_title': ..., 'document_url': ..., 'parent_doc_id': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Kwiatkowski2019Nq, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {TACL} } ```
open-llm-leaderboard/details_Voicelab__trurl-2-7b	--- pretty_name: Evaluation run of Voicelab/trurl-2-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Voicelab/trurl-2-7b](https://huggingface.co/Voicelab/trurl-2-7b) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 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 agregated 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_Voicelab__trurl-2-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-24T13:00:35.734451](https://huggingface.co/datasets/open-llm-leaderboard/details_Voicelab__trurl-2-7b/blob/main/results_2023-10-24T13-00-35.734451.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 \"em\": 0.26908557046979864,\n\ \ \"em_stderr\": 0.004541696656496853,\n \"f1\": 0.3290079697986583,\n\ \ \"f1_stderr\": 0.004499453214736992,\n \"acc\": 0.3967222424009962,\n\ \ \"acc_stderr\": 0.009837690155913053\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.26908557046979864,\n \"em_stderr\": 0.004541696656496853,\n\ \ \"f1\": 0.3290079697986583,\n \"f1_stderr\": 0.004499453214736992\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0712661106899166,\n \ \ \"acc_stderr\": 0.007086462127954499\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7221783741120757,\n \"acc_stderr\": 0.012588918183871605\n\ \ }\n}\n```" repo_url: https://huggingface.co/Voicelab/trurl-2-7b 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_08_17T14_14_32.422343 path: - '/details_harness\|arc:challenge\|25_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-17T14:14:32.422343.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_24T13_00_35.734451 path: - '/details_harness\|drop\|3_2023-10-24T13-00-35.734451.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-24T13-00-35.734451.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_24T13_00_35.734451 path: - '/details_harness\|gsm8k\|5_2023-10-24T13-00-35.734451.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-24T13-00-35.734451.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hellaswag\|10_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-17T14:14:32.422343.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_17T14_14_32.422343 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-17T14:14:32.422343.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_24T13_00_35.734451 path: - '/details_harness\|winogrande\|5_2023-10-24T13-00-35.734451.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-24T13-00-35.734451.parquet' - config_name: results data_files: - split: 2023_08_17T14_14_32.422343 path: - results_2023-08-17T14:14:32.422343.parquet - split: 2023_10_24T13_00_35.734451 path: - results_2023-10-24T13-00-35.734451.parquet - split: latest path: - results_2023-10-24T13-00-35.734451.parquet --- # Dataset Card for Evaluation run of Voicelab/trurl-2-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/Voicelab/trurl-2-7b - 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 [Voicelab/trurl-2-7b](https://huggingface.co/Voicelab/trurl-2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 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 agregated 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_Voicelab__trurl-2-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T13:00:35.734451](https://huggingface.co/datasets/open-llm-leaderboard/details_Voicelab__trurl-2-7b/blob/main/results_2023-10-24T13-00-35.734451.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": { "em": 0.26908557046979864, "em_stderr": 0.004541696656496853, "f1": 0.3290079697986583, "f1_stderr": 0.004499453214736992, "acc": 0.3967222424009962, "acc_stderr": 0.009837690155913053 }, "harness\|drop\|3": { "em": 0.26908557046979864, "em_stderr": 0.004541696656496853, "f1": 0.3290079697986583, "f1_stderr": 0.004499453214736992 }, "harness\|gsm8k\|5": { "acc": 0.0712661106899166, "acc_stderr": 0.007086462127954499 }, "harness\|winogrande\|5": { "acc": 0.7221783741120757, "acc_stderr": 0.012588918183871605 } } ``` ### 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]
ChristophSchuhmann/test-files	--- license: apache-2.0 ---
FSMBench/fsmbench_what_will_be_the_state_12K_think_step_by_step	--- dataset_info: features: - name: query_id dtype: string - name: fsm_id dtype: string - name: fsm_json dtype: string - name: difficulty_level dtype: int64 - name: transition_matrix dtype: string - name: query dtype: string - name: answer dtype: string - name: substring_index dtype: int64 - name: number_of_states dtype: int64 - name: number_of_alphabets dtype: int64 - name: state_alpha_combo dtype: string splits: - name: validation num_bytes: 29342193 num_examples: 12800 download_size: 1211333 dataset_size: 29342193 configs: - config_name: default data_files: - split: validation path: data/validation-* ---
mask-distilled-one-sec-cv12/chunk_90	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1296275532 num_examples: 254571 download_size: 1322328801 dataset_size: 1296275532 --- # Dataset Card for "chunk_90" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
astrosbd/fake_review_hedi	--- dataset_info: features: - name: cat dtype: string - name: score dtype: float64 - name: label dtype: string - name: review dtype: string splits: - name: train num_bytes: 15867393 num_examples: 40432 download_size: 8285372 dataset_size: 15867393 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "fake_review_hedi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
adilhabibi/bioacoustic_segments	--- dataset_info: features: - name: segments sequence: sequence: sequence: float32 - name: label_idices dtype: int64 - name: label_names dtype: string splits: - name: train num_bytes: 72803953 num_examples: 1457 download_size: 53309954 dataset_size: 72803953 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "bioacoustic_segments" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Falah/deep_learning_books	--- dataset_info: features: - name: file_name dtype: string - name: content dtype: string splits: - name: train num_bytes: 2116608 num_examples: 1056 download_size: 1142689 dataset_size: 2116608 --- # Dataset Card for "deep_learning_books" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-billsum-default-37bdaa-1564755702	--- type: predictions tags: - autotrain - evaluation datasets: - billsum eval_info: task: summarization model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP17 metrics: [] dataset_name: billsum dataset_config: default dataset_split: test col_mapping: text: text target: summary --- # 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP17 * Dataset: billsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
moodlep/dt_atari_replay_hf	--- license: mit ---
adnankarim/polimer	--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 28628023.0 num_examples: 976 - name: validation num_bytes: 3228642.0 num_examples: 100 - name: test num_bytes: 347463.0 num_examples: 10 download_size: 32107011 dataset_size: 32204128.0 --- # Dataset Card for "polimer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-18000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 961620 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
sadeem-ai/arabic-qna	--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: ar-qna-train-data-hf.csv - split: test path: ar-qna-test-data-hf.csv task_categories: - question-answering language: - ar tags: - qna - questioning-answering - questions-generation pretty_name: arabic QnA dataset size_categories: - 1K<n<10K --- # Sadeem QnA: An Arabic QnA Dataset 🌍✨ Welcome to the Sadeem QnA dataset, a vibrant collection designed for the advancement of Arabic natural language processing, specifically tailored for Question Answering (QnA) systems. Sourced from the rich and diverse content of Arabic Wikipedia, this dataset is a gateway to exploring the depths of Arabic language understanding, offering a unique challenge to both researchers and AI enthusiasts alike. ## Table of Contents - [About Sadeem QnA](#about-sadeem-qna) - [Dataset Structure](#dataset-structure) - [Getting Started](#getting-started) - [Usage](#usage) - [Contributing](#contributing) - [License](#license) - [Citation](#citation) ## About Sadeem QnA The Sadeem QnA dataset is crafted with the intent to foster research and development in Arabic Question Answering systems. It encompasses a broad range of topics, reflecting the rich tapestry of Arabic culture, history, and science, making it an ideal resource for training and evaluating AI models. ### Why Sadeem QnA? - Rich Content: Over 6,000 QnA pairs across diverse subjects. - Real-World Questions: Derived from actual queries people might ask, providing practical value for real-world applications. - Dual Splits: Carefully partitioned into training (5,000 rows) and testing (1,030 rows) sets to facilitate effective model evaluation. ## Dataset Structure Each record in the dataset follows a structured format, containing the following fields: - `title`: The title of the Wikipedia article. - `text`: A snippet from the article related to the question. - `source`: The URL of the Wikipedia page. - `question`: A question related to the text snippet. - `answer`: The answer to the question. - `has_answer`: A boolean indicating whether the answer is present in the text snippet. ### Example Record ```json { 'title': 'قائمة الجوائز والترشيحات التي تلقتها سلسلة أفلام مباريات الجوع', 'text': 'قائمة الجوائز والترشيحات التي تلقتها سلسلة أفلام مباريات الجوع قائمة تُسجّل الترشيحات والجوائز التي تلقتها سلسلة أفلام مباريات الجوع المقتبسة من سلسلة مباريات الجوع للمؤلفة الأمريكية سوزان كولنز. والسلسلة من توزيع شركة ليونزغيت إنترتاينمنت، وقام ببطولتها جينيفر لورنس في دور كاتنيس إيفردين، جوش هوتشرسن في دور بيتا ميلاريك. وبدأت السلسلة بفيلم مباريات الجوع الذي صدر في العام 2012، ثم فيلم في العام 2013، وتبعهما كل من (2014) وأخيرًا: (2015). كان لجينيفر لورنس حصة الأسد في سجل الترشيحات والجوائز التي نالتها السلسلة.', 'source': 'https://ar.wikipedia.org/wiki?curid=6237097', 'question': 'متى صدر الفيلم الأول من سلسلة مباريات الجوع؟', 'answer': 'عام 2012', 'has_answer': True }, { 'title': 'سانت فرنسيس (ويسكونسن)', 'text': 'بلغ عدد الأسر 4,494 أسرة كانت نسبة 19.8% منها لديها أطفال تحت سن الثامنة عشر تعيش معهم، وبلغت نسبة الأزواج القاطنين مع بعضهم البعض 36.6% من أصل المجموع الكلي للأسر، ونسبة 8.7% من الأسر كان لديها معيلات من الإناث دون وجود شريك، بينما كانت نسبة 3.9% من الأسر لديها معيلون من الذكور دون وجود شريكة وكانت نسبة 50.8% من غير العائلات. تألفت نسبة 42.6% من أصل جميع الأسر من أفراد ونسبة 13.7% كانوا يعيش معهم شخص وحيد يبلغ من العمر 65 عاماً فما فوق. وبلغ متوسط حجم الأسرة المعيشية 2.80، أما متوسط حجم العائلات فبلغ 2.02.', 'source': 'https://ar.wikipedia.org/wiki?curid=2198358', 'question': 'ما هو عدد العائلات المقيمة في سانت فرنسيس؟', 'answer': '', 'has_answer': False } ``` ## Getting Started To get started with the Sadeem QnA dataset, you can download it directly from our [Huggingface repository](https://huggingface.co/datasets/sadeem-ai/arabic-qna). Follow the instructions there to load the dataset into your environment and begin exploring. ## Usage This dataset is perfect for: - Training machine learning models for Arabic question answering. - Evaluating the performance of NLP models on Arabic text. - Enhancing language understanding systems with a focus on Arabic. ## Contributing We welcome contributions from the community! Whether it's improving the documentation, adding more questions, or reporting issues, your help makes Sadeem QnA better for everyone. ## License The Sadeem QnA dataset is available under the Apache License 2.0. We encourage its use for academic research, commercial applications, and beyond, provided proper attribution is given. ## Citation If you use the Sadeem QnA dataset in your research, please cite it using the following format: ```bibtex @misc{sadeem_qna, title={Sadeem QnA: An Arabic QnA Dataset}, author={}, year={2024}, publisher={Huggingface}, howpublished={\url{https://huggingface.co/datasets/sadeem-ai/arabic-qna}}, } ``` Embark on your journey through the Arabic language with Sadeem QnA and unlock the potential of AI in understanding the complexity and beauty of Arabic text. 🚀💡
jlbaker361/league-maybe-runway-50	--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: seed dtype: int64 - name: steps dtype: int64 splits: - name: train num_bytes: 30718722.0 num_examples: 72 download_size: 30717346 dataset_size: 30718722.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
dhematillake/instructpix2pix-spatial	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: transformed_image dtype: image splits: - name: train num_bytes: 9300429930.63 num_examples: 9447 download_size: 8998126312 dataset_size: 9300429930.63 configs: - config_name: default data_files: - split: train path: data/train-* ---
AlekseyKorshuk/clean-dataset-preview	--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: check_word_number_criteria dtype: int64 splits: - name: train num_bytes: 686595411 num_examples: 337253 download_size: 283358430 dataset_size: 686595411 --- # Dataset Card for "clean-dataset-preview" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RZ412/mmlu_responses_1k	--- dataset_info: features: - name: exemplar_questions dtype: string - name: test_questions dtype: string - name: subject dtype: string - name: answers list: - name: answer dtype: string - name: model dtype: string - name: reference_answers dtype: int64 splits: - name: train num_bytes: 5847672 num_examples: 1000 download_size: 391151 dataset_size: 5847672 configs: - config_name: default data_files: - split: train path: data/train-* ---
eduagarcia-temp/brwac_meta	--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: dedup struct: - name: exact_norm struct: - name: cluster_main_idx dtype: int64 - name: cluster_size dtype: int64 - name: exact_hash_idx dtype: int64 - name: is_duplicate dtype: bool - name: minhash struct: - name: cluster_main_idx dtype: int64 - name: cluster_size dtype: int64 - name: is_duplicate dtype: bool - name: minhash_idx dtype: int64 - name: doc_id dtype: string - name: title dtype: string - name: uri dtype: string splits: - name: train num_bytes: 18279917379 num_examples: 3530796 download_size: 11165124126 dataset_size: 18279917379 --- # Dataset Card for "brwac_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SDbiaseval/faces	--- dataset_info: features: - name: model dtype: string - name: adjective dtype: string - name: profession dtype: string - name: 'no' dtype: int32 - name: image_name dtype: string - name: image dtype: image splits: - name: train num_bytes: 3432470489.253643 num_examples: 88708 download_size: 1970670181 dataset_size: 3432470489.253643 --- # Dataset Card for "faces" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tkuhn1988/tkuhnstyle	--- license: afl-3.0 ---
trl-internal-testing/mlabonne-chatml-dpo-pairs-copy	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 35914686 num_examples: 12859 download_size: 19539812 dataset_size: 35914686 configs: - config_name: default data_files: - split: train path: data/train-* --- This is a copy and unmaintained version of [`mlabonne/chatml_dpo_pairs`](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) that we use in TRL CI for testing purpose. Please refer to the original dataset for usage and more details
vietgpt-archive/vungoi_question_type1	--- dataset_info: features: - name: question_raw dtype: string - name: options_raw list: - name: answer_raw dtype: string - name: key dtype: string - name: answer_raw struct: - name: answer_raw dtype: string - name: key dtype: string - name: solution_raw dtype: string - name: metadata struct: - name: chapter dtype: string - name: difficult_degree dtype: int64 - name: grade dtype: string - name: id dtype: string - name: idx dtype: int64 - name: subject dtype: string splits: - name: train num_bytes: 137805960 num_examples: 112037 download_size: 78332631 dataset_size: 137805960 --- # Dataset Card for "vungoi_question_type1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Kalva014/male-asian-hairstyles	--- license: mit ---
alwanrahmana/ner_scientifict_resampled	--- license: apache-2.0 ---
longhoang06/MC-ViMath	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: explanation dtype: string - name: answer dtype: string splits: - name: train num_bytes: 6086303 num_examples: 9328 download_size: 3016997 dataset_size: 6086303 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "MC-ViMath" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
huggingartists/kendrick-lamar	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/kendrick-lamar" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [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) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 2.493223 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f08637c8cfdeaab4dfbf0631424001ec.640x640x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/kendrick-lamar"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Kendrick Lamar</div> <a href="https://genius.com/artists/kendrick-lamar"> <div style="text-align: center; font-size: 14px;">@kendrick-lamar</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/kendrick-lamar). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/kendrick-lamar") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|861\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/kendrick-lamar") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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tellarin-ai/ntx_llm_instructions	--- license: cc-by-sa-4.0 language: - ar - de - en - es - fr - hi - it - ja - ko - nl - pt - sv - tr - zh task_categories: - token-classification --- # Dataset Card for NTX v1 in the Aya format This dataset is a format conversion from its original v1 format into the Aya instruction format and it's released here under the CC-BY-SA 4.0 license and conditions. It contains data in multiple languages and this version is intended for multi-lingual LLM construction/tuning. ## Citation If you utilize this dataset version, feel free to cite/footnote this huggingface dataset repo, but please also cite the original dataset publication. BibTeX: ``` @preprint{chen2023dataset, title={Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions}, author={Sanxing Chen and Yongqiang Chen and Börje F. Karlsson}, year={2023}, eprint={2303.18103}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Dataset Details For the original NTX dataset for information extraction of numerical and temporal expressions and more details, please check the arXiv paper: https://arxiv.org/abs/2303.18103. NOTE: Unfortunately, due to a conversion issue with numerical expressions, this version here only includes the temporal expressions part of NTX. ## Format Conversion Details The templates used to reformat the dataset are in the ./templates-ntx directory.
liuyanchen1015/MULTI_VALUE_rte_what_comparative	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 1345 num_examples: 5 - name: train num_bytes: 668 num_examples: 2 download_size: 8739 dataset_size: 2013 --- # Dataset Card for "MULTI_VALUE_rte_what_comparative" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mrpc_anaphoric_it	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 18672 num_examples: 64 - name: train num_bytes: 42888 num_examples: 150 - name: validation num_bytes: 2973 num_examples: 11 download_size: 54711 dataset_size: 64533 --- # Dataset Card for "MULTI_VALUE_mrpc_anaphoric_it" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mohamedsaeed823/egyARA2eng	--- license: apache-2.0 ---
AlekseyKorshuk/chai-experiment-v0-chatml	--- dataset_info: features: - name: source dtype: string - name: conversation list: - name: content dtype: string - name: do_train dtype: bool - name: role dtype: string splits: - name: train num_bytes: 1765802637.0 num_examples: 322064 download_size: 909265481 dataset_size: 1765802637.0 --- # Dataset Card for "chai-experiment-v0-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
metabloit/offensive-swahili-text	--- license: mit task_categories: - text-classification language: - sw size_categories: - 1K<n<10K viewer: true --- # Overview This dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist. The dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse. ## Dataset details The dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset consists of 990 sentences and the test dataset consists of 660 sentences. ### Dataset annotations - 0: non-offensive - 1: offensive
arbml/ArSAS	--- dataset_info: features: - name: '#Tweet_ID' dtype: string - name: Tweet_text dtype: string - name: Topic dtype: string - name: Sentiment_label_confidence dtype: string - name: Speech_act_label dtype: string - name: Speech_act_label_confidence dtype: string - name: label dtype: class_label: names: 0: Negative 1: Neutral 2: Positive 3: Mixed splits: - name: train num_bytes: 6147723 num_examples: 19897 download_size: 2998319 dataset_size: 6147723 --- # Dataset Card for "ArSAS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
0xMaka/trading-candles-subset-sc-format	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Bearish '1': Bullish splits: - name: train num_bytes: 11878595.339187406 num_examples: 155885 - name: test num_bytes: 5090913.660812595 num_examples: 66809 download_size: 6788665 dataset_size: 16969509.0 --- # Dataset Card for "trading-candles-subset-sc-format" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dim/lmsys_chatbot_arena_conversations	--- dataset_info: features: - name: question_id dtype: string - name: model_a dtype: string - name: model_b dtype: string - name: winner dtype: string - name: judge dtype: string - name: conversation_a list: - name: content dtype: string - name: role dtype: string - name: conversation_b list: - name: content dtype: string - name: role dtype: string - name: turn dtype: int64 - name: anony dtype: bool - name: language dtype: string - name: tstamp dtype: float64 - name: openai_moderation struct: - name: categories struct: - name: harassment dtype: bool - name: harassment/threatening dtype: bool - name: hate dtype: bool - name: hate/threatening dtype: bool - name: self-harm dtype: bool - name: self-harm/instructions dtype: bool - name: self-harm/intent dtype: bool - name: sexual dtype: bool - name: sexual/minors dtype: bool - name: violence dtype: bool - name: violence/graphic dtype: bool - name: category_scores struct: - name: harassment dtype: float64 - name: harassment/threatening dtype: float64 - name: hate dtype: float64 - name: hate/threatening dtype: float64 - name: self-harm dtype: float64 - name: self-harm/instructions dtype: float64 - name: self-harm/intent dtype: float64 - name: sexual dtype: float64 - name: sexual/minors dtype: float64 - name: violence dtype: float64 - name: violence/graphic dtype: float64 - name: flagged dtype: bool - name: toxic_chat_tag struct: - name: roberta-large struct: - name: flagged dtype: bool - name: probability dtype: float64 - name: t5-large struct: - name: flagged dtype: bool - name: score dtype: float64 splits: - name: train num_bytes: 81159839 num_examples: 33000 download_size: 41573740 dataset_size: 81159839 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "lmsys_chatbot_arena_conversations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
notfan/fa_pim_qalog	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 463142 num_examples: 742 download_size: 264389 dataset_size: 463142 configs: - config_name: default data_files: - split: train path: data/train-* ---
TrainingDataPro/llm-dataset	--- license: cc-by-nc-nd-4.0 task_categories: - text-classification - table-question-answering - question-answering - text2text-generation - text-generation language: - en - es - ar - el - fr - ja - pt - uk - az - ga - ko - ca - eo - hi - ml - sl - hu - mr - cs - fa - id - nl - th - de - fi - it - pl - tr tags: - code - legal - finance --- # LLM Dataset - Prompts and Generated Texts The dataset contains prompts and texts generated by the Large Language Models (LLMs) in 32 different languages. The prompts are short sentences or phrases for the model to generate text. The texts generated by the LLM are responses to these prompts and can vary in length and complexity. Researchers and developers can use this dataset to train and fine-tune their own language models for multilingual applications. The dataset provides a rich and diverse collection of outputs from the model, demonstrating its ability to generate coherent and contextually relevant text in multiple languages. # 💴 For Commercial Usage: Full version of the dataset includes 4,000,000 logs generated in 32 languages with diferent types of LLM, including Uncensored GPT, leave a request on [TrainingData](https://trainingdata.pro/data-market/llm?utm_source=huggingface&utm_medium=cpc&utm_campaign=llm) to buy the dataset ### Models used for text generation: - GPT-3.5, - GPT-4 ### Languages in the dataset: Arabic, Azerbaijani, Catalan, Chinese, Czech, Danish, German, Greek, English, Esperanto, Spanish, Persian, Finnish, French, Irish, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Malayalam, Maratham, Netherlands, Polish, Portuguese, Portuguese (Brazil), Slovak, Swedish, Thai, Turkish, Ukrainian ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Ff60c93f09ec82a765aa39678e4aa0a58%2Fsnapedit_1709731090855.jpeg?generation=1709738798916444&alt=media) # Content CSV File includes the following data: - from_language: language the prompt is made in, - model: type of the model (GPT-3.5, GPT-4 and Uncensored GPT Version), - time: time when the answer was generated, - text: user prompt, - response: response generated by the model # 💴 Buy the Dataset: This is just an example of the data. Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/llm?utm_source=huggingface&utm_medium=cpc&utm_campaign=llm) to discuss your requirements, learn about the price and buy the dataset ## [TrainingData](https://trainingdata.pro/data-market/llm?utm_source=huggingface&utm_medium=cpc&utm_campaign=llm) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets keywords: dataset, machine learning, natural language processing, artificial intelligence, deep learning, neural networks, text generation, language models, openai, gpt-3, data science, predictive modeling, sentiment analysis, keyword extraction, text classification, sequence-to-sequence models, attention mechanisms, transformer architecture, word embeddings, glove embeddings, chatbots, question answering, language understanding, text mining, information retrieval, data preprocessing, feature engineering, explainable ai, model deployment
open-llm-leaderboard/details_Danielbrdz__Barcenas-7b	--- pretty_name: Evaluation run of Danielbrdz/Barcenas-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Danielbrdz/Barcenas-7b](https://huggingface.co/Danielbrdz/Barcenas-7b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 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 agregated 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_Danielbrdz__Barcenas-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T23:34:07.541919](https://huggingface.co/datasets/open-llm-leaderboard/details_Danielbrdz__Barcenas-7b/blob/main/results_2023-09-17T23-34-07.541919.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 \"em\": 0.004718959731543624,\n\ \ \"em_stderr\": 0.0007018360183131257,\n \"f1\": 0.0816715604026848,\n\ \ \"f1_stderr\": 0.0017762083839348887,\n \"acc\": 0.39889766050552516,\n\ \ \"acc_stderr\": 0.009497938418122394\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.004718959731543624,\n \"em_stderr\": 0.0007018360183131257,\n\ \ \"f1\": 0.0816715604026848,\n \"f1_stderr\": 0.0017762083839348887\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.06141015921152388,\n \ \ \"acc_stderr\": 0.006613027536586322\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7363851617995264,\n \"acc_stderr\": 0.012382849299658464\n\ \ }\n}\n```" repo_url: https://huggingface.co/Danielbrdz/Barcenas-7b 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_08_28T22_47_45.353935 path: - '/details_harness\|arc:challenge\|25_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-28T22:47:45.353935.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T23_34_07.541919 path: - '/details_harness\|drop\|3_2023-09-17T23-34-07.541919.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T23-34-07.541919.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T23_34_07.541919 path: - '/details_harness\|gsm8k\|5_2023-09-17T23-34-07.541919.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T23-34-07.541919.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hellaswag\|10_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-28T22:47:45.353935.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_28T22_47_45.353935 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-28T22:47:45.353935.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T23_34_07.541919 path: - '/details_harness\|winogrande\|5_2023-09-17T23-34-07.541919.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T23-34-07.541919.parquet' - config_name: results data_files: - split: 2023_08_28T22_47_45.353935 path: - results_2023-08-28T22:47:45.353935.parquet - split: 2023_09_17T23_34_07.541919 path: - results_2023-09-17T23-34-07.541919.parquet - split: latest path: - results_2023-09-17T23-34-07.541919.parquet --- # Dataset Card for Evaluation run of Danielbrdz/Barcenas-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/Danielbrdz/Barcenas-7b - 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 [Danielbrdz/Barcenas-7b](https://huggingface.co/Danielbrdz/Barcenas-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 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 agregated 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_Danielbrdz__Barcenas-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T23:34:07.541919](https://huggingface.co/datasets/open-llm-leaderboard/details_Danielbrdz__Barcenas-7b/blob/main/results_2023-09-17T23-34-07.541919.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": { "em": 0.004718959731543624, "em_stderr": 0.0007018360183131257, "f1": 0.0816715604026848, "f1_stderr": 0.0017762083839348887, "acc": 0.39889766050552516, "acc_stderr": 0.009497938418122394 }, "harness\|drop\|3": { "em": 0.004718959731543624, "em_stderr": 0.0007018360183131257, "f1": 0.0816715604026848, "f1_stderr": 0.0017762083839348887 }, "harness\|gsm8k\|5": { "acc": 0.06141015921152388, "acc_stderr": 0.006613027536586322 }, "harness\|winogrande\|5": { "acc": 0.7363851617995264, "acc_stderr": 0.012382849299658464 } } ``` ### 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]
nqv2291/en-Pile-NER-seq2seq_format	--- dataset_info: features: - name: id dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 664745884 num_examples: 350974 - name: test num_bytes: 13593212 num_examples: 7208 download_size: 147499047 dataset_size: 678339096 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
zpn/zinc20	--- license: mit dataset_info: features: - name: selfies dtype: string - name: smiles dtype: string - name: id dtype: string splits: - name: train num_bytes: 238295712864 num_examples: 804925861 - name: validation num_bytes: 26983481360 num_examples: 100642661 - name: test num_bytes: 29158755632 num_examples: 101082073 download_size: 40061255073 dataset_size: 294437949856 tags: - bio - selfies - smiles - small_molecules pretty_name: zinc20 size_categories: - 1B<n<10B --- # Dataset Card for Zinc20 ## Dataset Description - Homepage: https://zinc20.docking.org/ - Paper: https://pubs.acs.org/doi/10.1021/acs.jcim.0c00675 ### Dataset Summary ZINC is a publicly available database that aggregates commercially available and annotated compounds. ZINC provides downloadable 2D and 3D versions as well as a website that enables rapid molecule lookup and analog search. ZINC has grown from fewer than 1 million compounds in 2005 to nearly 2 billion now. This dataset includes ~1B molecules in total. We have filtered out any compounds that were not avaible to be converted from `smiles` to `seflies` representations. ### 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 The dataset is split into an 80/10/10 train/valid/test random split across files (which roughly corresponds to the same percentages) ### Source Data #### Initial Data Collection and Normalization Initial data was released at https://zinc20.docking.org/. We have downloaded and added a `selfies` field and filtered out all molecules that did not contain molecules that could be converted to `selfies` representations. ### Citation Information @article{Irwin2020, doi = {10.1021/acs.jcim.0c00675}, url = {https://doi.org/10.1021/acs.jcim.0c00675}, year = {2020}, month = oct, publisher = {American Chemical Society ({ACS})}, volume = {60}, number = {12}, pages = {6065--6073}, author = {John J. Irwin and Khanh G. Tang and Jennifer Young and Chinzorig Dandarchuluun and Benjamin R. Wong and Munkhzul Khurelbaatar and Yurii S. Moroz and John Mayfield and Roger A. Sayle}, title = {{ZINC}20{\textemdash}A Free Ultralarge-Scale Chemical Database for Ligand Discovery}, journal = {Journal of Chemical Information and Modeling} } ### Contributions This dataset was curated and added by [@zanussbaum](https://github.com/zanussbaum).
autoevaluate/autoeval-staging-eval-project-200453bd-7694959	--- type: predictions tags: - autotrain - evaluation datasets: - masakhaner eval_info: task: entity_extraction model: arnolfokam/bert-base-uncased-swa metrics: [] dataset_name: masakhaner dataset_config: swa dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: arnolfokam/bert-base-uncased-swa * Dataset: masakhaner To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
emozilla/sat-reading	--- dataset_info: features: - name: text dtype: string - name: answer dtype: string - name: requires_line dtype: bool - name: id dtype: string splits: - name: train num_bytes: 1399648 num_examples: 298 - name: test num_bytes: 196027 num_examples: 38 - name: validation num_bytes: 183162 num_examples: 39 download_size: 365469 dataset_size: 1778837 language: - en --- # Dataset Card for "sat-reading" This dataset contains the passages and questions from the Reading part of ten publicly available SAT Practice Tests. For more information see the blog post [Language Models vs. The SAT Reading Test](https://jeffq.com/blog/language-models-vs-the-sat-reading-test). For each question, the reading passage from the section it is contained in is prefixed. Then, the question is prompted with `Question #:`, followed by the four possible answers. Each entry ends with `Answer:`. Questions which reference a diagram, chart, table, etc. have been removed (typically three per test). In addition, there is a boolean `requires_line` feature, which indiciates if the question references specific lines within the passage. To maintain generalizability in finetuning scenarios, `SAT READING COMPREHENSION TEST` appears at the beginning of each entry -- it may be desireable to remove this depending on your intentions. Eight tests appear in the training split; one each in the validation and test splits.
sgoedecke/5s_birdcall_samples_16k	--- dataset_info: features: - name: audio dtype: audio - name: label dtype: string - name: input_values sequence: float32 - name: attention_mask sequence: int32 splits: - name: train num_bytes: 2149373508.375 num_examples: 4797 - name: test num_bytes: 2149821590.25 num_examples: 4798 download_size: 3704408556 dataset_size: 4299195098.625 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647_random	--- 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_1_bs_16 num_bytes: 85893233.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 88896569.125 num_examples: 5647 download_size: 168043271 dataset_size: 174789802.25 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-futin__feed-top_en_-3f631c-2246071662	--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-30b metrics: [] dataset_name: futin/feed dataset_config: top_en_ 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-30b * Dataset: futin/feed * Config: top_en_ * 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.
Back-up/Topic-Prediction-Context-With-Random-Prompts-in-the-end	--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: topic struct: - name: topic dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: instruction dtype: string - name: prompt_name dtype: string splits: - name: train num_bytes: 248398 num_examples: 101 download_size: 125460 dataset_size: 248398 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Topic-Prediction-Context-With-Random-Prompts-in-the-end" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NghiemAbe/Legal_Corpus	--- dataset_info: features: - name: id dtype: string - name: ministry dtype: string - name: type dtype: string - name: name dtype: string - name: chapter_id dtype: string - name: chapter_name dtype: string - name: article dtype: string - name: content dtype: string splits: - name: corpus num_bytes: 1071858956 num_examples: 515188 download_size: 294811460 dataset_size: 1071858956 --- # Dataset Card for "Legal_Corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DazMashaly/test_data	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 365263950.94 num_examples: 5108 download_size: 354753479 dataset_size: 365263950.94 --- # Dataset Card for "test_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
fancyzhx/dbpedia_14	--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - topic-classification paperswithcode_id: dbpedia pretty_name: DBpedia dataset_info: config_name: dbpedia_14 features: - name: label dtype: class_label: names: '0': Company '1': EducationalInstitution '2': Artist '3': Athlete '4': OfficeHolder '5': MeanOfTransportation '6': Building '7': NaturalPlace '8': Village '9': Animal '10': Plant '11': Album '12': Film '13': WrittenWork - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 178428970 num_examples: 560000 - name: test num_bytes: 22310285 num_examples: 70000 download_size: 119424374 dataset_size: 200739255 configs: - config_name: dbpedia_14 data_files: - split: train path: dbpedia_14/train-* - split: test path: dbpedia_14/test-* default: true --- # Dataset Card for DBpedia14 ## 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: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Repository: https://github.com/zhangxiangxiao/Crepe - Paper: https://arxiv.org/abs/1509.01626 - Point of Contact: [Xiang Zhang](mailto:xiang.zhang@nyu.edu) ### Dataset Summary The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes from DBpedia 2014. They are listed in classes.txt. From each of thse 14 ontology classes, we randomly choose 40,000 training samples and 5,000 testing samples. Therefore, the total size of the training dataset is 560,000 and testing dataset 70,000. There are 3 columns in the dataset (same for train and test splits), corresponding to class index (1 to 14), title and content. The title and content are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). There are no new lines in title or content. ### Supported Tasks and Leaderboards - `text-classification`, `topic-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct topic. ### Languages Although DBpedia is a multilingual knowledge base, the DBpedia14 extract contains English data mainly, other languages may appear (e.g. a film whose title is origanlly not English). ## Dataset Structure ### Data Instances A typical data point, comprises of a title, a content and the corresponding label. An example from the DBpedia test set looks as follows: ``` { 'title':'', 'content':" TY KU /taɪkuː/ is an American alcoholic beverage company that specializes in sake and other spirits. The privately-held company was founded in 2004 and is headquartered in New York City New York. While based in New York TY KU's beverages are made in Japan through a joint venture with two sake breweries. Since 2011 TY KU's growth has extended its products into all 50 states.", 'label':0 } ``` ### Data Fields - 'title': a string containing the title of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). - 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). - 'label': one of the 14 possible topics. ### Data Splits The data is split into a training and test set. For each of the 14 classes we have 40,000 training samples and 5,000 testing samples. Therefore, the total size of the training dataset is 560,000 and testing dataset 70,000. ## Dataset Creation ### Curation Rationale The DBPedia ontology classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu), licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Source Data #### Initial Data Collection and Normalization Source data is taken from DBpedia: https://wiki.dbpedia.org/develop/datasets #### 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 The DBPedia ontology classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu), licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Licensing Information The DBPedia ontology classification dataset is licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. ### Citation Information ``` @inproceedings{NIPS2015_250cf8b5, author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, booktitle = {Advances in Neural Information Processing Systems}, editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett}, pages = {}, publisher = {Curran Associates, Inc.}, title = {Character-level Convolutional Networks for Text Classification}, url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/250cf8b51c773f3f8dc8b4be867a9a02-Paper.pdf}, volume = {28}, year = {2015} } ``` Lehmann, Jens, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann et al. "DBpedia–a large-scale, multilingual knowledge base extracted from Wikipedia." Semantic web 6, no. 2 (2015): 167-195. ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.
satendra4u2022/dpo_precise_datasets	--- license: mit ---
vertigo23/njogerera_english_luganda_corpus	--- license: unknown size_categories: - 10K<n<100K ---
aryamannningombam/indian-female-tts-dataset	--- dataset_info: features: - name: file dtype: string - name: text dtype: string - name: tag dtype: string - name: file_path dtype: string - name: y sequence: float32 - name: emotional_embedding sequence: int64 - name: non_characters dtype: 'null' - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 923807734 num_examples: 2843 download_size: 911770863 dataset_size: 923807734 configs: - config_name: default data_files: - split: train path: data/train-* ---
Svenni551/NPC_talks	--- dataset_info: features: - name: prompt dtype: string - name: output dtype: string splits: - name: train num_bytes: 38012 num_examples: 10 download_size: 31737 dataset_size: 38012 configs: - config_name: default data_files: - split: train path: data/train-* ---
NickyNicky/h2ogpt-oig-oasst1-instruct-cleaned-v3	--- dataset_info: features: - name: list_json list: - name: bot dtype: string - name: human dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 164763347 num_examples: 269406 download_size: 105519939 dataset_size: 164763347 configs: - config_name: default data_files: - split: train path: data/train-* ---
benchang1110/sciencetw	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: title dtype: string - name: article dtype: string splits: - name: train num_bytes: 157012034 num_examples: 26056 download_size: 105424999 dataset_size: 157012034 --- # Dataset Card for "sciencetw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NUS-IDS/beyond_blue	--- configs: - config_name: default data_files: - split: anxiety path: data/anxiety-* - split: depression path: data/depression-* - split: ptsd path: data/ptsd-* dataset_info: features: - name: url dtype: string - name: comments list: - name: author dtype: string - name: content dtype: string - name: date dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: content dtype: string - name: author dtype: string splits: - name: anxiety num_bytes: 56172807 num_examples: 6943 - name: depression num_bytes: 60224734 num_examples: 6008 - name: ptsd num_bytes: 21141031 num_examples: 1816 download_size: 68731517 dataset_size: 137538572 --- # Dataset Card for "beyond_blue" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nbtpj/Movies_and_TV	--- dataset_info: features: - name: overall dtype: float64 - name: verified dtype: bool - name: reviewTime dtype: string - name: reviewerID dtype: string - name: asin dtype: string - name: style dtype: string - name: reviewerName dtype: string - name: reviewText dtype: string - name: summary dtype: string - name: unixReviewTime dtype: int64 - name: vote dtype: string - name: image sequence: string splits: - name: train num_bytes: 4058038162 num_examples: 8765568 download_size: 2295911945 dataset_size: 4058038162 --- # Dataset Card for "Movies_and_TV" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/nagayoshi_subaru_theidolmstermillionlive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nagayoshi_subaru/永吉昴 (THE iDOLM@STER: Million Live!) This is the dataset of nagayoshi_subaru/永吉昴 (THE iDOLM@STER: Million Live!), containing 229 images and their tags. The core tags of this character are `short_hair, green_hair, red_eyes, bangs, brown_hair`, 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 \| 229 \| 242.00 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 229 \| 154.84 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 519 \| 319.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 229 \| 219.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 519 \| 431.13 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/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/nagayoshi_subaru_theidolmstermillionlive', 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) \| 1girl, looking_at_viewer, solo, long_sleeves, white_gloves, ascot, closed_mouth, crown, hat, purple_eyes, white_background, white_jacket, white_pants, blush, epaulettes, grin, hair_between_eyes, hair_ornament, simple_background, upper_body \| \| 1 \| 12 \| ![](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, looking_at_viewer, open_mouth, :d, solo, purple_eyes, dress, jewelry, necktie \| \| 2 \| 8 \| ![](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, looking_at_viewer, purple_eyes, solo, open_mouth, :d, baseball, letterman_jacket, shorts \| \| 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, blush, looking_at_viewer, solo, open_mouth, skirt, smile, jewelry \| \| 4 \| 12 \| ![](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, hair_between_eyes, blush, looking_at_viewer, simple_background, solo, smile, white_background, white_shirt, upper_body, open_mouth, collarbone, jacket, short_sleeves \| \| 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) \| 1girl, blush, solo, cleavage, looking_at_viewer, medium_breasts, navel, collarbone, hair_between_eyes, sitting, smile, striped_bikini, beachball, one_eye_closed, open_mouth, partially_submerged, short_shorts, water \| \| 6 \| 6 \| ![](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, blush, censored, nipples, pussy, small_breasts, hetero, on_back, open_mouth, solo_focus, 1boy, spread_legs, hair_between_eyes, navel, nude, on_bed, penis, pillow, sex, sweat \| \| 7 \| 5 \| ![](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, detached_collar, playboy_bunny, rabbit_ears, solo, fake_animal_ears, strapless_leotard, wrist_cuffs, blush, indian_style, looking_at_viewer, pantyhose, rabbit_tail, simple_background, small_breasts, black_bowtie, black_leotard, cleavage, covered_navel, purple_eyes, red_bowtie, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| long_sleeves \| white_gloves \| ascot \| closed_mouth \| crown \| hat \| purple_eyes \| white_background \| white_jacket \| white_pants \| blush \| epaulettes \| grin \| hair_between_eyes \| hair_ornament \| simple_background \| upper_body \| open_mouth \| :d \| dress \| jewelry \| necktie \| baseball \| letterman_jacket \| shorts \| skirt \| smile \| white_shirt \| collarbone \| jacket \| short_sleeves \| cleavage \| medium_breasts \| navel \| sitting \| striped_bikini \| beachball \| one_eye_closed \| partially_submerged \| short_shorts \| water \| censored \| nipples \| pussy \| small_breasts \| hetero \| on_back \| solo_focus \| 1boy \| spread_legs \| nude \| on_bed \| penis \| pillow \| sex \| sweat \| detached_collar \| playboy_bunny \| rabbit_ears \| fake_animal_ears \| strapless_leotard \| wrist_cuffs \| indian_style \| pantyhose \| rabbit_tail \| black_bowtie \| black_leotard \| covered_navel \| red_bowtie \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:---------------\|:---------------\|:--------\|:---------------\|:--------\|:------\|:--------------\|:-------------------\|:---------------\|:--------------\|:--------\|:-------------\|:-------\|:--------------------\|:----------------\|:--------------------\|:-------------\|:-------------\|:-----\|:--------\|:----------\|:----------\|:-----------\|:-------------------\|:---------\|:--------\|:--------\|:--------------\|:-------------\|:---------\|:----------------\|:-----------\|:-----------------\|:--------\|:----------\|:-----------------\|:------------\|:-----------------\|:----------------------\|:---------------\|:--------\|:-----------\|:----------\|:--------\|:----------------\|:---------\|:----------\|:-------------\|:-------\|:--------------\|:-------\|:---------\|:--------\|:---------\|:------\|:--------\|:------------------\|:----------------\|:--------------\|:-------------------\|:--------------------\|:--------------\|:---------------\|:------------\|:--------------\|:---------------\|:----------------\|:----------------\|:-------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 12 \| ![](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 \| 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 \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 6 \| ![](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 \| 5 \| ![](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 \|
acheong08/nsfw_reddit	--- license: openrail ---
yangyz1230/promoter_tata	--- dataset_info: features: - name: name dtype: string - name: sequence dtype: string - name: chrom dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: strand dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 954157 num_examples: 2732 - name: test num_bytes: 115635 num_examples: 332 download_size: 519991 dataset_size: 1069792 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-23000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1080073 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
jacquelinehe/enron-emails	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1701056967 num_examples: 926132 download_size: 972216068 dataset_size: 1701056967 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_wandb__pruned_mistral	--- pretty_name: Evaluation run of wandb/pruned_mistral dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [wandb/pruned_mistral](https://huggingface.co/wandb/pruned_mistral) 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_wandb__pruned_mistral\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T16:40:40.526366](https://huggingface.co/datasets/open-llm-leaderboard/details_wandb__pruned_mistral/blob/main/results_2024-03-21T16-40-40.526366.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\": 0.2677672008134633,\n\ \ \"acc_stderr\": 0.031196121816193134,\n \"acc_norm\": 0.26979863634977536,\n\ \ \"acc_norm_stderr\": 0.03200972209199791,\n \"mc1\": 0.24724602203182375,\n\ \ \"mc1_stderr\": 0.015102404797359652,\n \"mc2\": 0.4108681590294748,\n\ \ \"mc2_stderr\": 0.014542287705752187\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.24829351535836178,\n \"acc_stderr\": 0.012624912868089764,\n\ \ \"acc_norm\": 0.2832764505119454,\n \"acc_norm_stderr\": 0.013167478735134575\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.37353116908982276,\n\ \ \"acc_stderr\": 0.004827526584889684,\n \"acc_norm\": 0.46345349531965746,\n\ \ \"acc_norm_stderr\": 0.004976434387469965\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.3037037037037037,\n\ \ \"acc_stderr\": 0.039725528847851375,\n \"acc_norm\": 0.3037037037037037,\n\ \ \"acc_norm_stderr\": 0.039725528847851375\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123415,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123415\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.18,\n\ \ \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.18,\n \ \ \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.24305555555555555,\n\ \ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.24305555555555555,\n\ \ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|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_mathematics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.24277456647398843,\n\ \ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\ \ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237653,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237653\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n\ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.2127659574468085,\n \"acc_stderr\": 0.02675439134803977,\n\ \ \"acc_norm\": 0.2127659574468085,\n \"acc_norm_stderr\": 0.02675439134803977\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.0404933929774814,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.0404933929774814\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.03600105692727772,\n\ \ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.03600105692727772\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525218,\n \"\ acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525218\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.15079365079365079,\n\ \ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\ \ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.25806451612903225,\n \"acc_stderr\": 0.024892469172462836,\n \"\ acc_norm\": 0.25806451612903225,\n \"acc_norm_stderr\": 0.024892469172462836\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.24630541871921183,\n \"acc_stderr\": 0.030315099285617715,\n \"\ acc_norm\": 0.24630541871921183,\n \"acc_norm_stderr\": 0.030315099285617715\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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_european_history\|5\"\ : {\n \"acc\": 0.20606060606060606,\n \"acc_stderr\": 0.0315841532404771,\n\ \ \"acc_norm\": 0.20606060606060606,\n \"acc_norm_stderr\": 0.0315841532404771\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.25757575757575757,\n \"acc_stderr\": 0.031156269519646857,\n \"\ acc_norm\": 0.25757575757575757,\n \"acc_norm_stderr\": 0.031156269519646857\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.30569948186528495,\n \"acc_stderr\": 0.03324837939758159,\n\ \ \"acc_norm\": 0.30569948186528495,\n \"acc_norm_stderr\": 0.03324837939758159\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.36153846153846153,\n \"acc_stderr\": 0.02435958146539699,\n\ \ \"acc_norm\": 0.36153846153846153,\n \"acc_norm_stderr\": 0.02435958146539699\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.23333333333333334,\n \"acc_stderr\": 0.02578787422095931,\n \ \ \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.02578787422095931\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.27310924369747897,\n \"acc_stderr\": 0.028942004040998167,\n\ \ \"acc_norm\": 0.27310924369747897,\n \"acc_norm_stderr\": 0.028942004040998167\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.271523178807947,\n \"acc_stderr\": 0.03631329803969653,\n \"acc_norm\"\ : 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969653\n },\n\ \ \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.30458715596330277,\n\ \ \"acc_stderr\": 0.019732299420354038,\n \"acc_norm\": 0.30458715596330277,\n\ \ \"acc_norm_stderr\": 0.019732299420354038\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\"\ : {\n \"acc\": 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n\ \ \"acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.21568627450980393,\n \"acc_stderr\": 0.028867431449849303,\n \"\ acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.028867431449849303\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.23628691983122363,\n \"acc_stderr\": 0.027652153144159263,\n \ \ \"acc_norm\": 0.23628691983122363,\n \"acc_norm_stderr\": 0.027652153144159263\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.3004484304932735,\n\ \ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.3004484304932735,\n\ \ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.371900826446281,\n \"acc_stderr\": 0.044120158066245044,\n \"\ acc_norm\": 0.371900826446281,\n \"acc_norm_stderr\": 0.044120158066245044\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.24074074074074073,\n\ \ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.24074074074074073,\n\ \ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.27607361963190186,\n \"acc_stderr\": 0.0351238528370505,\n\ \ \"acc_norm\": 0.27607361963190186,\n \"acc_norm_stderr\": 0.0351238528370505\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.04059867246952689,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.04059867246952689\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.22330097087378642,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.22330097087378642,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.027236013946196697,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.027236013946196697\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.23754789272030652,\n\ \ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\ \ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24277456647398843,\n \"acc_stderr\": 0.023083658586984204,\n\ \ \"acc_norm\": 0.24277456647398843,\n \"acc_norm_stderr\": 0.023083658586984204\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2748603351955307,\n\ \ \"acc_stderr\": 0.014931316703220508,\n \"acc_norm\": 0.2748603351955307,\n\ \ \"acc_norm_stderr\": 0.014931316703220508\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.238562091503268,\n \"acc_stderr\": 0.024404394928087873,\n\ \ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.024404394928087873\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.26366559485530544,\n\ \ \"acc_stderr\": 0.02502553850053234,\n \"acc_norm\": 0.26366559485530544,\n\ \ \"acc_norm_stderr\": 0.02502553850053234\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.23148148148148148,\n \"acc_stderr\": 0.023468429832451166,\n\ \ \"acc_norm\": 0.23148148148148148,\n \"acc_norm_stderr\": 0.023468429832451166\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.2553191489361702,\n \"acc_stderr\": 0.026011992930902006,\n \ \ \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.026011992930902006\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.23989569752281617,\n\ \ \"acc_stderr\": 0.010906282617981634,\n \"acc_norm\": 0.23989569752281617,\n\ \ \"acc_norm_stderr\": 0.010906282617981634\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.24019607843137256,\n \"acc_stderr\": 0.01728276069516742,\n \ \ \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.01728276069516742\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.22727272727272727,\n\ \ \"acc_stderr\": 0.040139645540727756,\n \"acc_norm\": 0.22727272727272727,\n\ \ \"acc_norm_stderr\": 0.040139645540727756\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.028920583220675585,\n\ \ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.028920583220675585\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.23880597014925373,\n\ \ \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n\ \ \"acc_norm_stderr\": 0.030147775935409224\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.2891566265060241,\n\ \ \"acc_stderr\": 0.03529486801511115,\n \"acc_norm\": 0.2891566265060241,\n\ \ \"acc_norm_stderr\": 0.03529486801511115\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.32748538011695905,\n \"acc_stderr\": 0.035993357714560276,\n\ \ \"acc_norm\": 0.32748538011695905,\n \"acc_norm_stderr\": 0.035993357714560276\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.24724602203182375,\n\ \ \"mc1_stderr\": 0.015102404797359652,\n \"mc2\": 0.4108681590294748,\n\ \ \"mc2_stderr\": 0.014542287705752187\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5390686661404893,\n \"acc_stderr\": 0.014009521680980318\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.001516300227445034,\n \ \ \"acc_stderr\": 0.0010717793485492608\n }\n}\n```" repo_url: https://huggingface.co/wandb/pruned_mistral 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_21T16_40_40.526366 path: - '/details_harness\|arc:challenge\|25_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-21T16-40-40.526366.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|gsm8k\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hellaswag\|10_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T16-40-40.526366.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T16-40-40.526366.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '/details_harness\|winogrande\|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-21T16-40-40.526366.parquet' - config_name: results data_files: - split: 2024_03_21T16_40_40.526366 path: - results_2024-03-21T16-40-40.526366.parquet - split: latest path: - results_2024-03-21T16-40-40.526366.parquet --- # Dataset Card for Evaluation run of wandb/pruned_mistral <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [wandb/pruned_mistral](https://huggingface.co/wandb/pruned_mistral) 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_wandb__pruned_mistral", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T16:40:40.526366](https://huggingface.co/datasets/open-llm-leaderboard/details_wandb__pruned_mistral/blob/main/results_2024-03-21T16-40-40.526366.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": 0.2677672008134633, "acc_stderr": 0.031196121816193134, "acc_norm": 0.26979863634977536, "acc_norm_stderr": 0.03200972209199791, "mc1": 0.24724602203182375, "mc1_stderr": 0.015102404797359652, "mc2": 0.4108681590294748, "mc2_stderr": 0.014542287705752187 }, "harness\|arc:challenge\|25": { "acc": 0.24829351535836178, "acc_stderr": 0.012624912868089764, "acc_norm": 0.2832764505119454, "acc_norm_stderr": 0.013167478735134575 }, "harness\|hellaswag\|10": { "acc": 0.37353116908982276, "acc_stderr": 0.004827526584889684, "acc_norm": 0.46345349531965746, "acc_norm_stderr": 0.004976434387469965 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.3037037037037037, "acc_stderr": 0.039725528847851375, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.039725528847851375 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123415, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123415 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2792452830188679, "acc_stderr": 0.027611163402399715, "acc_norm": 0.2792452830188679, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.24305555555555555, "acc_stderr": 0.0358687928008034, "acc_norm": 0.24305555555555555, "acc_norm_stderr": 0.0358687928008034 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237653, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237653 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.2127659574468085, "acc_stderr": 0.02675439134803977, "acc_norm": 0.2127659574468085, "acc_norm_stderr": 0.02675439134803977 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.24561403508771928, "acc_stderr": 0.0404933929774814, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.0404933929774814 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2482758620689655, "acc_stderr": 0.03600105692727772, "acc_norm": 0.2482758620689655, "acc_norm_stderr": 0.03600105692727772 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2619047619047619, "acc_stderr": 0.022644212615525218, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.022644212615525218 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.15079365079365079, "acc_stderr": 0.03200686497287392, "acc_norm": 0.15079365079365079, "acc_norm_stderr": 0.03200686497287392 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.25806451612903225, "acc_stderr": 0.024892469172462836, "acc_norm": 0.25806451612903225, "acc_norm_stderr": 0.024892469172462836 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.24630541871921183, "acc_stderr": 0.030315099285617715, "acc_norm": 0.24630541871921183, "acc_norm_stderr": 0.030315099285617715 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.20606060606060606, "acc_stderr": 0.0315841532404771, "acc_norm": 0.20606060606060606, "acc_norm_stderr": 0.0315841532404771 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.25757575757575757, "acc_stderr": 0.031156269519646857, "acc_norm": 0.25757575757575757, "acc_norm_stderr": 0.031156269519646857 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.30569948186528495, "acc_stderr": 0.03324837939758159, "acc_norm": 0.30569948186528495, "acc_norm_stderr": 0.03324837939758159 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.36153846153846153, "acc_stderr": 0.02435958146539699, "acc_norm": 0.36153846153846153, "acc_norm_stderr": 0.02435958146539699 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.23333333333333334, "acc_stderr": 0.02578787422095931, "acc_norm": 0.23333333333333334, "acc_norm_stderr": 0.02578787422095931 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.27310924369747897, "acc_stderr": 0.028942004040998167, "acc_norm": 0.27310924369747897, "acc_norm_stderr": 0.028942004040998167 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.271523178807947, "acc_stderr": 0.03631329803969653, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.03631329803969653 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.30458715596330277, "acc_stderr": 0.019732299420354038, "acc_norm": 0.30458715596330277, "acc_norm_stderr": 0.019732299420354038 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4583333333333333, "acc_stderr": 0.03398110890294636, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.028867431449849303, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.028867431449849303 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.23628691983122363, "acc_stderr": 0.027652153144159263, "acc_norm": 0.23628691983122363, "acc_norm_stderr": 0.027652153144159263 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.3004484304932735, "acc_stderr": 0.03076935200822914, "acc_norm": 0.3004484304932735, "acc_norm_stderr": 0.03076935200822914 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2748091603053435, "acc_stderr": 0.03915345408847836, "acc_norm": 0.2748091603053435, "acc_norm_stderr": 0.03915345408847836 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.371900826446281, "acc_stderr": 0.044120158066245044, "acc_norm": 0.371900826446281, "acc_norm_stderr": 0.044120158066245044 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.24074074074074073, "acc_stderr": 0.04133119440243839, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.04133119440243839 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.27607361963190186, "acc_stderr": 0.0351238528370505, "acc_norm": 0.27607361963190186, "acc_norm_stderr": 0.0351238528370505 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.24107142857142858, "acc_stderr": 0.04059867246952689, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.04059867246952689 }, "harness\|hendrycksTest-management\|5": { "acc": 0.22330097087378642, "acc_stderr": 0.04123553189891431, "acc_norm": 0.22330097087378642, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2222222222222222, "acc_stderr": 0.027236013946196697, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.027236013946196697 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.23754789272030652, "acc_stderr": 0.015218733046150193, "acc_norm": 0.23754789272030652, "acc_norm_stderr": 0.015218733046150193 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24277456647398843, "acc_stderr": 0.023083658586984204, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.023083658586984204 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2748603351955307, "acc_stderr": 0.014931316703220508, "acc_norm": 0.2748603351955307, "acc_norm_stderr": 0.014931316703220508 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.238562091503268, "acc_stderr": 0.024404394928087873, "acc_norm": 0.238562091503268, "acc_norm_stderr": 0.024404394928087873 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.26366559485530544, "acc_stderr": 0.02502553850053234, "acc_norm": 0.26366559485530544, "acc_norm_stderr": 0.02502553850053234 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.23148148148148148, "acc_stderr": 0.023468429832451166, "acc_norm": 0.23148148148148148, "acc_norm_stderr": 0.023468429832451166 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2553191489361702, "acc_stderr": 0.026011992930902006, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.026011992930902006 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.23989569752281617, "acc_stderr": 0.010906282617981634, "acc_norm": 0.23989569752281617, "acc_norm_stderr": 0.010906282617981634 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4485294117647059, "acc_stderr": 0.030211479609121593, "acc_norm": 0.4485294117647059, "acc_norm_stderr": 0.030211479609121593 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.24019607843137256, "acc_stderr": 0.01728276069516742, "acc_norm": 0.24019607843137256, "acc_norm_stderr": 0.01728276069516742 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.22727272727272727, "acc_stderr": 0.040139645540727756, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.040139645540727756 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.028920583220675585, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.028920583220675585 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.2891566265060241, "acc_stderr": 0.03529486801511115, "acc_norm": 0.2891566265060241, "acc_norm_stderr": 0.03529486801511115 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.32748538011695905, "acc_stderr": 0.035993357714560276, "acc_norm": 0.32748538011695905, "acc_norm_stderr": 0.035993357714560276 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.24724602203182375, "mc1_stderr": 0.015102404797359652, "mc2": 0.4108681590294748, "mc2_stderr": 0.014542287705752187 }, "harness\|winogrande\|5": { "acc": 0.5390686661404893, "acc_stderr": 0.014009521680980318 }, "harness\|gsm8k\|5": { "acc": 0.001516300227445034, "acc_stderr": 0.0010717793485492608 } } ``` ## 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]
wav2gloss/cocoon-glosses	--- license: cc-by-nc-nd-4.0 task_categories: - automatic-speech-recognition ---
samurai-architects/materials-blip	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 32878470.0 num_examples: 10 download_size: 32881580 dataset_size: 32878470.0 --- # Dataset Card for "materials-blip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
stenio123/1000GovPdfLibraryCongress-vector	--- license: openrail dataset_info: features: - name: values sequence: float64 - name: metadata struct: - name: pdf_file dtype: string - name: text dtype: string splits: - name: train num_bytes: 30704920 num_examples: 981 download_size: 14917630 dataset_size: 30704920 configs: - config_name: default data_files: - split: train path: data/train-* ---
jaejoo/llama-2-ko-law	--- license: apache-2.0 language: - ko tags: - legal size_categories: - 1K<n<10K ---
ndr01/jeans-captioning-dataset	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: 'Unnamed: 0' dtype: int64 - name: description dtype: string splits: - name: train num_bytes: 593273258.0 num_examples: 179 download_size: 588198577 dataset_size: 593273258.0 --- # Dataset Card for "jeans-captioning-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sam1120/dropoff-utcustom-TRAIN

--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 142272068.0 num_examples: 50 download_size: 43507500 dataset_size: 142272068.0 --- # Dataset Card for "dropoff-utcustom-TRAIN" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Mayank082000/Multilingual_Sentences_with_Sentences

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 509463 num_examples: 2289 download_size: 53713 dataset_size: 509463 configs: - config_name: default data_files: - split: train path: data/train-* ---

aisyahhrazak/crawl-malaysiagazette

--- language: - ms --- About - Data scraped from https://malaysiagazette.com/ - on 4.7.2023

dipteshkanojia/t5-qe-2023-indic-multi-da

--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: task dtype: string splits: - name: train num_bytes: 47647871 num_examples: 58940 download_size: 18352409 dataset_size: 47647871 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "t5-qe-2023-indic-multi-da" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955857

--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_dev eval_info: task: text_zero_shot_classification model: facebook/opt-2.7b metrics: [] dataset_name: mathemakitten/winobias_antistereotype_dev dataset_config: mathemakitten--winobias_antistereotype_dev dataset_split: validation 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-2.7b * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.

liuyanchen1015/MULTI_VALUE_qqp_drop_copula_be_AP

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 574829 num_examples: 3667 - name: test num_bytes: 6086644 num_examples: 38655 - name: train num_bytes: 5201008 num_examples: 32930 download_size: 7404705 dataset_size: 11862481 --- # Dataset Card for "MULTI_VALUE_qqp_drop_copula_be_AP" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

paul-w-qs/contracts_v6

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: N_ROWS dtype: int64 - name: N_COLS dtype: int64 - name: FONT_SIZE dtype: int64 - name: FONT_NAME dtype: string - name: BORDER_THICKNESS dtype: int64 - name: TABLE_STYLE dtype: string - name: NOISED dtype: bool - name: LABEL_NOISE dtype: bool - name: JSON_LABEL dtype: string splits: - name: train num_bytes: 360922904.016 num_examples: 5364 download_size: 360853881 dataset_size: 360922904.016 --- # Dataset Card for "contracts_v6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

onurSakar/GYM-Exercise

--- language: - en dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 839599 num_examples: 1660 download_size: 293713 dataset_size: 839599 configs: - config_name: default data_files: - split: train path: data/train-* ---

irds/wikir_it16k

--- pretty_name: '`wikir/it16k`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `wikir/it16k` The `wikir/it16k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/wikir#wikir/it16k). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=503,012 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/wikir_it16k', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Frej2020Wikir, title={WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={LREC}, year={2020} } @inproceedings{Frej2020MlWikir, title={MLWIKIR: A Python Toolkit for Building Large-scale Wikipedia-based Information Retrieval Datasets in Chinese, English, French, Italian, Japanese, Spanish and More}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={CIRCLE}, year={2020} } ```

uobinxiao/open_tables_icttd_for_table_detection

--- license: apache-2.0 --- Datasets for the paper "Revisiting Table Detection Datasets for Visually Rich Documents" (https://arxiv.org/abs/2305.04833). ## License Since this dataset is built on several open datasets and open documents, users should also adhere to the licenses of these publicly available datasets and documents.

allganize/rag-ko

--- dataset_info: features: - name: index dtype: int64 - name: system dtype: string - name: human dtype: string - name: answer dtype: string - name: answer_position dtype: int64 - name: answer_context_title dtype: string - name: answer_context_summary dtype: string splits: - name: train num_bytes: 914673 num_examples: 200 - name: test num_bytes: 914673 num_examples: 200 download_size: 2352755 dataset_size: 1829346 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* language: - ko --- # rag-ko - `rag-ko` 데이터는 금융 도메인의 RAG(Retrieval Augmented Generation, 검색증강생성) 데이터셋입니다. RAG를 진행할 수 있도록 Golden Context 1개와 Negative Context 2개가 제공되고 Golen Context에 관련된 질문과 그 답변이 주어집니다. - 데이터의 컨텍스트는 위키피디아와 공공기관의 금융보고서, 금융용어집등을 대상으로 만듭니다. 이후 GPT-4를 이용하여 해당 컨텍스트에 대한 질문과 답변을 생성하고 이를 각각, Golden Context, Question, Golden Answer로 삼습니다. - 이후 컨텍스트 집합에서 Question으로 검색(BM25)했을때 Golden Context를 제외하고 점수가 높은 두개의 Context를 선택합니다. 이를 Negative Context로 삼습니다. - Golden Context, 2개의 Negative Context, Question과 Instruction을 모두 포함했을때 3K Token(Llama2 tokenizer기준)을 넘지 않도록 Allganize Summerizer(사내 추출형 요약엔진)을 이용해 요약합니다. - 이후 사람이 검수 완료한 200개의 데이터셋입니다. ### 데이터 출처 - [한국어 wikipedia 금융 분류](https://ko.wikipedia.org/wiki/%EB%B6%84%EB%A5%98:%EA%B8%88%EC%9C%B5) - [한국은행 경제연구 보고서](https://www.bok.or.kr/portal/bbs/P0002454/list.do?menuNo=200431) - [한국은행 해외경제 포커스](https://www.bok.or.kr/portal/bbs/P0000545/list.do?menuNo=200437) ### 데이터 예시 ``` { 'conversation_id': 'financial_mmlu_0', 'conversations': array([ { 'from': 'human', 'value': '금리의 종류에 대한 설명으로 바르지 않은 것은?\n 1. 변동금리는 시장금리 변동에 따른 위험을 자금공급자가 부담하게 된다\n 2. 피셔방정식에 의하면 실질금리는 명목금리에서 기대인플레이션을 차감하면\n 구할 수 있다.\n 3. 복리는 원금에 대한 이자뿐 아니라 이자에 대한 이자도 함께 계산하는 방법이\n다.\n 4. 실효금리는 이자지급방법, 상환방법, 수수료, 세금 등을 감안한 후 차입자가\n실질적으로 부담하는 순자금조달비용을 말한다.\n 5. 채권시장에서는 금리보다 수익률이라는 용어를 더 많이 사용한다.' }, { 'from': 'gpt', 'value': '1' } ], dtype=object) } ``` License - Wikipedia: CC BY-SA 4.0 - [한국은행 저작권 보호방침](https://www.bok.or.kr/portal/main/contents.do?menuNo=200228)

open-llm-leaderboard/details_ehartford__WizardLM-13B-Uncensored

--- pretty_name: Evaluation run of ehartford/WizardLM-13B-Uncensored dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ehartford/WizardLM-13B-Uncensored](https://huggingface.co/ehartford/WizardLM-13B-Uncensored)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 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 agregated 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_ehartford__WizardLM-13B-Uncensored\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-18T07:53:55.275923](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__WizardLM-13B-Uncensored/blob/main/results_2023-10-18T07-53-55.275923.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 \"em\": 0.20994127516778524,\n\ \ \"em_stderr\": 0.004170789326061059,\n \"f1\": 0.3040310402684571,\n\ \ \"f1_stderr\": 0.004210803460550511,\n \"acc\": 0.3630369207736123,\n\ \ \"acc_stderr\": 0.00835492026013406\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.20994127516778524,\n \"em_stderr\": 0.004170789326061059,\n\ \ \"f1\": 0.3040310402684571,\n \"f1_stderr\": 0.004210803460550511\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02047005307050796,\n \ \ \"acc_stderr\": 0.0039004133859157192\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7056037884767167,\n \"acc_stderr\": 0.0128094271343524\n\ \ }\n}\n```" repo_url: https://huggingface.co/ehartford/WizardLM-13B-Uncensored 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_07_19T19_00_32.745864 path: - '**/details_harness|arc:challenge|25_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T19:00:32.745864.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_18T07_53_55.275923 path: - '**/details_harness|drop|3_2023-10-18T07-53-55.275923.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-18T07-53-55.275923.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_18T07_53_55.275923 path: - '**/details_harness|gsm8k|5_2023-10-18T07-53-55.275923.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-18T07-53-55.275923.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hellaswag|10_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:00:32.745864.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:00:32.745864.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T19_00_32.745864 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T19:00:32.745864.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T19:00:32.745864.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_18T07_53_55.275923 path: - '**/details_harness|winogrande|5_2023-10-18T07-53-55.275923.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-18T07-53-55.275923.parquet' - config_name: results data_files: - split: 2023_07_19T19_00_32.745864 path: - results_2023-07-19T19:00:32.745864.parquet - split: 2023_10_18T07_53_55.275923 path: - results_2023-10-18T07-53-55.275923.parquet - split: latest path: - results_2023-10-18T07-53-55.275923.parquet --- # Dataset Card for Evaluation run of ehartford/WizardLM-13B-Uncensored ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/ehartford/WizardLM-13B-Uncensored - **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 [ehartford/WizardLM-13B-Uncensored](https://huggingface.co/ehartford/WizardLM-13B-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 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 agregated 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_ehartford__WizardLM-13B-Uncensored", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-18T07:53:55.275923](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__WizardLM-13B-Uncensored/blob/main/results_2023-10-18T07-53-55.275923.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": { "em": 0.20994127516778524, "em_stderr": 0.004170789326061059, "f1": 0.3040310402684571, "f1_stderr": 0.004210803460550511, "acc": 0.3630369207736123, "acc_stderr": 0.00835492026013406 }, "harness|drop|3": { "em": 0.20994127516778524, "em_stderr": 0.004170789326061059, "f1": 0.3040310402684571, "f1_stderr": 0.004210803460550511 }, "harness|gsm8k|5": { "acc": 0.02047005307050796, "acc_stderr": 0.0039004133859157192 }, "harness|winogrande|5": { "acc": 0.7056037884767167, "acc_stderr": 0.0128094271343524 } } ``` ### 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]

gagan3012/NewArOCRDatasetv5

--- 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: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 744898565.786 num_examples: 38219 - name: validation num_bytes: 14180587.0 num_examples: 425 - name: test num_bytes: 13690842.0 num_examples: 425 download_size: 692203880 dataset_size: 772769994.786 --- # Dataset Card for "NewArOCRDatasetv5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Baidicoot/openhermes-base64

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 109420486.63468009 num_examples: 210374 download_size: 66695207 dataset_size: 109420486.63468009 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/juno_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of juno/ジュノー/天后 (Azur Lane) This is the dataset of juno/ジュノー/天后 (Azur Lane), containing 24 images and their tags. The core tags of this character are `pink_hair, long_hair, crown, bangs, mini_crown, ribbon, twintails, pink_eyes, bow, purple_eyes`, 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 | 24 | 29.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/juno_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 24 | 20.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/juno_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 59 | 42.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/juno_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 24 | 28.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/juno_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 59 | 57.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/juno_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/juno_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 | 24 | ![](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, 1girl, solo, open_mouth, blush, collarbone, bare_shoulders, :d, dress, long_sleeves, simple_background, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | looking_at_viewer | 1girl | solo | open_mouth | blush | collarbone | bare_shoulders | :d | dress | long_sleeves | simple_background | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------|:--------|:-------|:-------------|:--------|:-------------|:-----------------|:-----|:--------|:---------------|:--------------------|:-------------------| | 0 | 24 | ![](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 |

datahrvoje/twitter_dataset_1712730747

--- 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: 18262 num_examples: 45 download_size: 14179 dataset_size: 18262 configs: - config_name: default data_files: - split: train path: data/train-* ---

GmoData/ui5_db

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

freshpearYoon/vr_train_free_24

--- 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: 6305883116 num_examples: 10000 download_size: 1075121807 dataset_size: 6305883116 configs: - config_name: default data_files: - split: train path: data/train-* ---

batelidan/dataset-classifcation-tv

--- dataset_info: features: - name: /content/drive/MyDrive/Wave/20220810-194349-3reality-00E93AAC59A5.wav dtype: string - name: TV dtype: string splits: - name: train num_bytes: 128533 num_examples: 1627 download_size: 23156 dataset_size: 128533 --- # Dataset Card for "dataset-classifcation-tv" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yonathanstwn/ecolindo

--- dataset_info: features: - name: translation struct: - name: english dtype: string - name: colloquial_indo dtype: string - name: formal_indo dtype: string splits: - name: train num_bytes: 70159189 num_examples: 672581 - name: test num_bytes: 202512 num_examples: 2000 - name: validation num_bytes: 202111 num_examples: 2000 download_size: 51847840 dataset_size: 70563812 task_categories: - translation language: - id - en --- # English to Colloquial Indonesian Dataset (EColIndo) First-ever large-scale high-quality English to Colloquial Indonesian dataset. Fully generated from ChatGPT Zero-Shot Translation. Author: Yonathan Setiawan

cfilt/IITB-MonoDoc

--- license: cc-by-4.0 task_categories: - text-generation language: - hi - mr - gu - sa - ta - te - ml - ne - as - bn - ks - or - pa - ur - sd - kn size_categories: - 10B<n<100B tags: - language-modeling - llm - clm ---

israel/MT-llama

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: prompt_header dtype: string - name: datasource dtype: string splits: - name: train num_bytes: 84762357.7818897 num_examples: 200000 - name: validation num_bytes: 1209980 num_examples: 1994 - name: test num_bytes: 1306100 num_examples: 2024 download_size: 23384531 dataset_size: 87278437.7818897 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

benny-abhishek/AB_Speech

--- license: mit task_categories: - automatic-speech-recognition - text-to-speech language: - en tags: - asr - tts - ser pretty_name: ab_speech size_categories: - n<1K --- Contains voice of a single male speaker spoken with emphasis on all phonemes.

stuheart86/imageclassification

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

bgspaditya/phishing-dataset

--- license: mit ---

open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B

--- pretty_name: Evaluation run of PulsarAI/Nebula-v2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [PulsarAI/Nebula-v2-7B](https://huggingface.co/PulsarAI/Nebula-v2-7B) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 1 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_PulsarAI__Nebula-v2-7B\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T13:58:09.073163](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B/blob/main/results_2023-12-02T13-58-09.073163.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\": 0.3169067475360121,\n\ \ \"acc_stderr\": 0.012815868296721373\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.3169067475360121,\n \"acc_stderr\": 0.012815868296721373\n\ \ }\n}\n```" repo_url: https://huggingface.co/PulsarAI/Nebula-v2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_gsm8k_5 data_files: - split: 2023_12_02T13_58_09.073163 path: - '**/details_harness|gsm8k|5_2023-12-02T13-58-09.073163.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-02T13-58-09.073163.parquet' - config_name: results data_files: - split: 2023_12_02T13_58_09.073163 path: - results_2023-12-02T13-58-09.073163.parquet - split: latest path: - results_2023-12-02T13-58-09.073163.parquet --- # Dataset Card for Evaluation run of PulsarAI/Nebula-v2-7B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/PulsarAI/Nebula-v2-7B - **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 [PulsarAI/Nebula-v2-7B](https://huggingface.co/PulsarAI/Nebula-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 1 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_PulsarAI__Nebula-v2-7B", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T13:58:09.073163](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__Nebula-v2-7B/blob/main/results_2023-12-02T13-58-09.073163.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": 0.3169067475360121, "acc_stderr": 0.012815868296721373 }, "harness|gsm8k|5": { "acc": 0.3169067475360121, "acc_stderr": 0.012815868296721373 } } ``` ### 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]

Cohere/miracl-ar-queries-22-12

--- annotations_creators: - expert-generated language: - ar multilinguality: - multilingual size_categories: [] source_datasets: [] tags: [] task_categories: - text-retrieval license: - apache-2.0 task_ids: - document-retrieval --- # MIRACL (ar) embedded with cohere.ai `multilingual-22-12` encoder We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model. The query embeddings can be found in [Cohere/miracl-ar-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ar-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-ar-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ar-corpus-22-12). For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus). Dataset info: > MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world. > > The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage. ## Embeddings We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/). ## Loading the dataset In [miracl-ar-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-ar-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large. You can either load the dataset like this: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/miracl-ar-corpus-22-12", split="train") ``` Or you can also stream it without downloading it before: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/miracl-ar-corpus-22-12", split="train", streaming=True) for doc in docs: docid = doc['docid'] title = doc['title'] text = doc['text'] emb = doc['emb'] ``` ## Search Have a look at [miracl-ar-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-ar-queries-22-12) where we provide the query embeddings for the MIRACL dataset. To search in the documents, you must use **dot-product**. And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product. A full search example: ```python # Attention! For large datasets, this requires a lot of memory to store # all document embeddings and to compute the dot product scores. # Only use this for smaller datasets. For large datasets, use a vector DB from datasets import load_dataset import torch #Load documents + embeddings docs = load_dataset(f"Cohere/miracl-ar-corpus-22-12", split="train") doc_embeddings = torch.tensor(docs['emb']) # Load queries queries = load_dataset(f"Cohere/miracl-ar-queries-22-12", split="dev") # Select the first query as example qid = 0 query = queries[qid] query_embedding = torch.tensor(queries['emb']) # Compute dot score between query embedding and document embeddings dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1)) top_k = torch.topk(dot_scores, k=3) # Print results print("Query:", query['query']) for doc_id in top_k.indices[0].tolist(): print(docs[doc_id]['title']) print(docs[doc_id]['text']) ``` You can get embeddings for new queries using our API: ```python #Run: pip install cohere import cohere co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :)) texts = ['my search query'] response = co.embed(texts=texts, model='multilingual-22-12') query_embedding = response.embeddings[0] # Get the embedding for the first text ``` ## Performance In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset. We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results. Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted. | Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 | |---|---|---|---|---| | miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 | | miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 | | miracl-de | 44.4 | 60.7 | 19.6 | 29.8 | | miracl-en | 44.6 | 62.2 | 30.2 | 43.2 | | miracl-es | 47.0 | 74.1 | 27.0 | 47.2 | | miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 | | miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 | | miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 | | miracl-id | 44.8 | 63.8 | 39.2 | 54.7 | | miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 | | **Avg** | 51.7 | 67.5 | 34.7 | 46.0 | Further languages (not supported by Elasticsearch): | Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | |---|---|---| | miracl-fa | 44.8 | 53.6 | | miracl-ja | 49.0 | 61.0 | | miracl-ko | 50.9 | 64.8 | | miracl-sw | 61.4 | 74.5 | | miracl-te | 67.8 | 72.3 | | miracl-th | 60.2 | 71.9 | | miracl-yo | 56.4 | 62.2 | | miracl-zh | 43.8 | 56.5 | | **Avg** | 54.3 | 64.6 |

mteb-pt/reddit-clustering

--- configs: - config_name: pt data_files: - split: test path: test* ---

bigscience-data/roots_indic-or_wikisource

--- language: or license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_indic-or_wikisource # wikisource_filtered - Dataset uid: `wikisource_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 2.6306 % of total - 12.7884 % of fr - 19.8886 % of indic-bn - 20.9966 % of indic-ta - 2.3478 % of ar - 4.7068 % of indic-hi - 18.0998 % of indic-te - 1.7155 % of es - 19.4800 % of indic-kn - 9.1737 % of indic-ml - 17.1771 % of indic-mr - 17.1870 % of indic-gu - 70.3687 % of indic-as - 1.0165 % of pt - 7.8642 % of indic-pa - 1.3501 % of vi - 4.9411 % of indic-or - 0.5307 % of ca - 2.3593 % of id - 1.5928 % of eu ### BigScience processing steps #### Filters applied to: fr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-bn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-ta - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: ar - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-te - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: es - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: indic-kn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - remove_wiki_mojibake - filter_small_docs_bytes_300 #### Filters applied to: indic-ml - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-mr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-gu - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-as - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-pa - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: vi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: indic-or - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs #### Filters applied to: ca - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_1024 #### Filters applied to: id - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs - filter_small_docs_bytes_300 #### Filters applied to: eu - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - dedup_template_soft - filter_remove_empty_docs

waifu-research-department/embeddings

--- license: mit --- # Info >Try to include embedding info in the commit description (model, author, artist, images, etc) >Naming: name-object/style

BAAI/COIG

--- license: apache-2.0 arxiv: 2304.07987 language: - zh --- # This is the Chinese Open Instruction Generalist project We propose the Chinese Open Instruction Generalist (**COIG**) project to maintain a harmless, helpful, and diverse set of Chinese instruction corpora. We welcome all researchers in the community to contribute to the corpus set and collaborate with us. We only release the first chip of COIG to help the Chinese LLMs' development in the exploration stage and appeal to more researchers joining us in building COIG. We introduce a manually verified translated general instruction corpus, a manually annotated exam instruction corpus, a human value alignment instruction corpus, a multi-round counterfactual correction chat corpus, and a leetcode instruction corpus. We provide these new instruction corpora to assist the community with instruction tuning on Chinese LLMs. These instruction corpora are also template workflows for how new Chinese instruction corpora can be built and expanded effectively. It is best to download the individual data files directly that you wish to use instead of using HF load_datasets. All datasets can be downloaded from: https://huggingface.co/datasets/BAAI/COIG/tree/main This dataset card is modified from [OIG](https://huggingface.co/datasets/laion/OIG). ### Translated Instructions (66,858) There are 66,858 instructions in total, which are composed of 1,616 task descriptions in [Super-NaturalInstructions](https://arxiv.org/abs/2204.07705) along with a single instance for each of them, 175 seed tasks in [Self-Instruct](https://arxiv.org/abs/2212.10560), and 66,007 instructions from [Unnatural Instructions](https://arxiv.org/abs/2212.09689). To reduce the cost and further improve the quality of the instruction corpus, we separate the translation procedure into three phases: automatic translation, manual verification, and manual correction. These strict quality verification procedures assure the reliability of the translated corpus. ### Exam Instructions (63,532) The Chinese National College Entrance Examination, Middle School Entrance Examinations, and Civil Servant Examination are the main Chinese commonsense tests. These exams contain various question formats and detailed analysis that can be used as the Chain-of-Thought (**CoT**) corpus. We extract six informative elements from original exam questions, including instruction, question context, question, answer, answer analysis, and coarse-grained subject. There are six main coarse-grained subjects: Chinese, English, Politics, Biology, History, and Geology. There are very few Math, Physics, and Chemistry questions in the corpus because these questions are often with complex symbols which are hard to annotate. For many choice questions, we recommend that the researchers utilize this corpus to further post-process it using prompts or post-process it to blank-filling questions to increase the instructions' diversity further. ### Human Value Alignment Instructions (34,471) To respect and reflect the major difference caused by different cultural backgrounds, different from other tasks in COIG that leverage one unified collection of instruction-following samples, we categorize the value alignment data into two separate series: - A set of samples that present shared human values in the Chinese-speaking world. In total, we choose 50 instructions as the augmentation seeds, and produce 3k resulting instructions following samples for general-purpose value alignment in the Chinese-speaking world. - Some additional sets of samples that present regional-culture or country-specific human values. ### Counterfactural Correction Multi-round Chat (13,653) The Counterfactual Correction Multi-round Chat dataset (CCMC) is constructed based on the [CN-DBpedia knowledge graph dataset](https://link.springer.com/chapter/10.1007/978-3-319-60045-1_44) with the aim of alleviating and resolving the pain points of hallucination and factual inconsistency in current LLMs. The CCMC dataset includes 5 rounds of role-playing chat between a student and a teacher, and the corresponding knowledge they refer to. The dataset contains ~13,000 dialogues with an average of 5 rounds per dialogue, resulting in ~65,000 rounds of chat. ### Leetcode Instructions (11,737) Given that the code-related tasks potentially contribute to the ability emergence of LLMs, we argue that code-related tasks aligned with the Chinese natural language should be considered in our datasets. Therefore, we build the Leetcode instructions from a **CC-BY-SA-4.0** license [collection](https://github.com/doocs/leetcode) of 2,589 programming questions. The questions contain problem descriptions, multiple programming languages, and explanations (834 questions do not have explanations). ## Support this project Your contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can: Submit Github issues, track issues and help create datasets that need improvement. https://github.com/BAAI-Zlab/COIG ## Update: May 27, 2023 - v0.3: Update counterfactural_correction_multi_round_chat.tar.gz and make sure all round responses can be decoded as json. - v0.2: Update exam_instructions.jsonl, translated_instructions.jsonl and human_value_alignment_instructions_part2.json. - v0.1: Release the five datasets of COIG. ## Disclaimer These datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets. ## License The COIG dataset that is authored by BAAI is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as unnatural instructions data which is licensed under MIT License, or web-crawled data which is used under fair use principles. ## BibTeX & Citation ``` @misc{zhang2023chinese, title={Chinese Open Instruction Generalist: A Preliminary Release}, author={Ge Zhang and Yemin Shi and Ruibo Liu and Ruibin Yuan and Yizhi Li and Siwei Dong and Yu Shu and Zhaoqun Li and Zekun Wang and Chenghua Lin and Wenhao Huang and Jie Fu}, year={2023}, eprint={2304.07987}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

copenlu/scientific-exaggeration-detection

--- annotations_creators: - expert-generated language: - en language_creators: - found license: - gpl-3.0 multilinguality: - monolingual paperswithcode_id: semi-supervised-exaggeration-detection-of pretty_name: Scientific Exaggeration Detection size_categories: - n<1K source_datasets: [] tags: - scientific text - scholarly text - inference - fact checking - misinformation task_categories: - text-classification task_ids: - natural-language-inference - multi-input-text-classification --- # Dataset Card for Scientific Exaggeration Detection ## Dataset Description - **Homepage:** https://github.com/copenlu/scientific-exaggeration-detection - **Repository:** https://github.com/copenlu/scientific-exaggeration-detection - **Paper:** https://aclanthology.org/2021.emnlp-main.845.pdf ### Dataset Summary Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task. ## Dataset Structure The training and test data are derived from the InSciOut studies from [Sumner et al. 2014](https://www.bmj.com/content/349/bmj.g7015) and [Bratton et al. 2019](https://pubmed.ncbi.nlm.nih.gov/31728413/#:~:text=Results%3A%20We%20found%20that%20the,inference%20from%20non%2Dhuman%20studies.). The splits have the following fields: ``` original_file_id: The ID of the original spreadsheet in the Sumner/Bratton data where the annotations are derived from press_release_conclusion: The conclusion sentence from the press release press_release_strength: The strength label for the press release abstract_conclusion: The conclusion sentence from the abstract abstract_strength: The strength label for the abstract exaggeration_label: The final exaggeration label ``` The exaggeration label is one of `same`, `exaggerates`, or `downplays`. The strength label is one of the following: ``` 0: Statement of no relationship 1: Statement of correlation 2: Conditional statement of causation 3: Statement of causation ``` ## Dataset Creation See section 4 of the [paper](https://aclanthology.org/2021.emnlp-main.845.pdf) for details on how the dataset was curated. The original InSciOut data can be found [here](https://figshare.com/articles/dataset/InSciOut/903704) ## Citation ``` @inproceedings{wright2021exaggeration, title={{Semi-Supervised Exaggeration Detection of Health Science Press Releases}}, author={Dustin Wright and Isabelle Augenstein}, booktitle = {Proceedings of EMNLP}, publisher = {Association for Computational Linguistics}, year = 2021 } ``` Thanks to [@dwright37](https://github.com/dwright37) for adding this dataset.

irds/natural-questions

--- pretty_name: '`natural-questions`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `natural-questions` The `natural-questions` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/natural-questions#natural-questions). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=28,390,850 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/natural-questions', 'docs') for record in docs: record # {'doc_id': ..., 'text': ..., 'html': ..., 'start_byte': ..., 'end_byte': ..., 'start_token': ..., 'end_token': ..., 'document_title': ..., 'document_url': ..., 'parent_doc_id': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Kwiatkowski2019Nq, title = {Natural Questions: a Benchmark for Question Answering Research}, author = {Tom Kwiatkowski and Jennimaria Palomaki and Olivia Redfield and Michael Collins and Ankur Parikh and Chris Alberti and Danielle Epstein and Illia Polosukhin and Matthew Kelcey and Jacob Devlin and Kenton Lee and Kristina N. Toutanova and Llion Jones and Ming-Wei Chang and Andrew Dai and Jakob Uszkoreit and Quoc Le and Slav Petrov}, year = {2019}, journal = {TACL} } ```

open-llm-leaderboard/details_Voicelab__trurl-2-7b

--- pretty_name: Evaluation run of Voicelab/trurl-2-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Voicelab/trurl-2-7b](https://huggingface.co/Voicelab/trurl-2-7b) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 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 agregated 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_Voicelab__trurl-2-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-24T13:00:35.734451](https://huggingface.co/datasets/open-llm-leaderboard/details_Voicelab__trurl-2-7b/blob/main/results_2023-10-24T13-00-35.734451.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 \"em\": 0.26908557046979864,\n\ \ \"em_stderr\": 0.004541696656496853,\n \"f1\": 0.3290079697986583,\n\ \ \"f1_stderr\": 0.004499453214736992,\n \"acc\": 0.3967222424009962,\n\ \ \"acc_stderr\": 0.009837690155913053\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.26908557046979864,\n \"em_stderr\": 0.004541696656496853,\n\ \ \"f1\": 0.3290079697986583,\n \"f1_stderr\": 0.004499453214736992\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0712661106899166,\n \ \ \"acc_stderr\": 0.007086462127954499\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7221783741120757,\n \"acc_stderr\": 0.012588918183871605\n\ \ }\n}\n```" repo_url: https://huggingface.co/Voicelab/trurl-2-7b 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_08_17T14_14_32.422343 path: - '**/details_harness|arc:challenge|25_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-17T14:14:32.422343.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_24T13_00_35.734451 path: - '**/details_harness|drop|3_2023-10-24T13-00-35.734451.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-24T13-00-35.734451.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_24T13_00_35.734451 path: - '**/details_harness|gsm8k|5_2023-10-24T13-00-35.734451.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-24T13-00-35.734451.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hellaswag|10_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:14:32.422343.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-management|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T14:14:32.422343.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_17T14_14_32.422343 path: - '**/details_harness|truthfulqa:mc|0_2023-08-17T14:14:32.422343.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-17T14:14:32.422343.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_24T13_00_35.734451 path: - '**/details_harness|winogrande|5_2023-10-24T13-00-35.734451.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-24T13-00-35.734451.parquet' - config_name: results data_files: - split: 2023_08_17T14_14_32.422343 path: - results_2023-08-17T14:14:32.422343.parquet - split: 2023_10_24T13_00_35.734451 path: - results_2023-10-24T13-00-35.734451.parquet - split: latest path: - results_2023-10-24T13-00-35.734451.parquet --- # Dataset Card for Evaluation run of Voicelab/trurl-2-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Voicelab/trurl-2-7b - **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 [Voicelab/trurl-2-7b](https://huggingface.co/Voicelab/trurl-2-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 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 agregated 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_Voicelab__trurl-2-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T13:00:35.734451](https://huggingface.co/datasets/open-llm-leaderboard/details_Voicelab__trurl-2-7b/blob/main/results_2023-10-24T13-00-35.734451.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": { "em": 0.26908557046979864, "em_stderr": 0.004541696656496853, "f1": 0.3290079697986583, "f1_stderr": 0.004499453214736992, "acc": 0.3967222424009962, "acc_stderr": 0.009837690155913053 }, "harness|drop|3": { "em": 0.26908557046979864, "em_stderr": 0.004541696656496853, "f1": 0.3290079697986583, "f1_stderr": 0.004499453214736992 }, "harness|gsm8k|5": { "acc": 0.0712661106899166, "acc_stderr": 0.007086462127954499 }, "harness|winogrande|5": { "acc": 0.7221783741120757, "acc_stderr": 0.012588918183871605 } } ``` ### 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]

ChristophSchuhmann/test-files

--- license: apache-2.0 ---

FSMBench/fsmbench_what_will_be_the_state_12K_think_step_by_step

--- dataset_info: features: - name: query_id dtype: string - name: fsm_id dtype: string - name: fsm_json dtype: string - name: difficulty_level dtype: int64 - name: transition_matrix dtype: string - name: query dtype: string - name: answer dtype: string - name: substring_index dtype: int64 - name: number_of_states dtype: int64 - name: number_of_alphabets dtype: int64 - name: state_alpha_combo dtype: string splits: - name: validation num_bytes: 29342193 num_examples: 12800 download_size: 1211333 dataset_size: 29342193 configs: - config_name: default data_files: - split: validation path: data/validation-* ---

mask-distilled-one-sec-cv12/chunk_90

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1296275532 num_examples: 254571 download_size: 1322328801 dataset_size: 1296275532 --- # Dataset Card for "chunk_90" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

astrosbd/fake_review_hedi

--- dataset_info: features: - name: cat dtype: string - name: score dtype: float64 - name: label dtype: string - name: review dtype: string splits: - name: train num_bytes: 15867393 num_examples: 40432 download_size: 8285372 dataset_size: 15867393 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "fake_review_hedi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

adilhabibi/bioacoustic_segments

--- dataset_info: features: - name: segments sequence: sequence: sequence: float32 - name: label_idices dtype: int64 - name: label_names dtype: string splits: - name: train num_bytes: 72803953 num_examples: 1457 download_size: 53309954 dataset_size: 72803953 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "bioacoustic_segments" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Falah/deep_learning_books

--- dataset_info: features: - name: file_name dtype: string - name: content dtype: string splits: - name: train num_bytes: 2116608 num_examples: 1056 download_size: 1142689 dataset_size: 2116608 --- # Dataset Card for "deep_learning_books" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-billsum-default-37bdaa-1564755702

--- type: predictions tags: - autotrain - evaluation datasets: - billsum eval_info: task: summarization model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP17 metrics: [] dataset_name: billsum dataset_config: default dataset_split: test col_mapping: text: text target: summary --- # 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP17 * Dataset: billsum * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

moodlep/dt_atari_replay_hf

--- license: mit ---

adnankarim/polimer

--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 28628023.0 num_examples: 976 - name: validation num_bytes: 3228642.0 num_examples: 100 - name: test num_bytes: 347463.0 num_examples: 10 download_size: 32107011 dataset_size: 32204128.0 --- # Dataset Card for "polimer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-18000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 961620 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

sadeem-ai/arabic-qna

--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: ar-qna-train-data-hf.csv - split: test path: ar-qna-test-data-hf.csv task_categories: - question-answering language: - ar tags: - qna - questioning-answering - questions-generation pretty_name: arabic QnA dataset size_categories: - 1K<n<10K --- # Sadeem QnA: An Arabic QnA Dataset 🌍✨ Welcome to the **Sadeem QnA** dataset, a vibrant collection designed for the advancement of Arabic natural language processing, specifically tailored for Question Answering (QnA) systems. Sourced from the rich and diverse content of Arabic Wikipedia, this dataset is a gateway to exploring the depths of Arabic language understanding, offering a unique challenge to both researchers and AI enthusiasts alike. ## Table of Contents - [About Sadeem QnA](#about-sadeem-qna) - [Dataset Structure](#dataset-structure) - [Getting Started](#getting-started) - [Usage](#usage) - [Contributing](#contributing) - [License](#license) - [Citation](#citation) ## About Sadeem QnA The **Sadeem QnA** dataset is crafted with the intent to foster research and development in Arabic Question Answering systems. It encompasses a broad range of topics, reflecting the rich tapestry of Arabic culture, history, and science, making it an ideal resource for training and evaluating AI models. ### Why Sadeem QnA? - **Rich Content:** Over 6,000 QnA pairs across diverse subjects. - **Real-World Questions:** Derived from actual queries people might ask, providing practical value for real-world applications. - **Dual Splits:** Carefully partitioned into training (5,000 rows) and testing (1,030 rows) sets to facilitate effective model evaluation. ## Dataset Structure Each record in the dataset follows a structured format, containing the following fields: - `title`: The title of the Wikipedia article. - `text`: A snippet from the article related to the question. - `source`: The URL of the Wikipedia page. - `question`: A question related to the text snippet. - `answer`: The answer to the question. - `has_answer`: A boolean indicating whether the answer is present in the text snippet. ### Example Record ```json { 'title': 'قائمة الجوائز والترشيحات التي تلقتها سلسلة أفلام مباريات الجوع', 'text': 'قائمة الجوائز والترشيحات التي تلقتها سلسلة أفلام مباريات الجوع قائمة تُسجّل الترشيحات والجوائز التي تلقتها سلسلة أفلام مباريات الجوع المقتبسة من سلسلة مباريات الجوع للمؤلفة الأمريكية سوزان كولنز. والسلسلة من توزيع شركة ليونزغيت إنترتاينمنت، وقام ببطولتها جينيفر لورنس في دور كاتنيس إيفردين، جوش هوتشرسن في دور بيتا ميلاريك. وبدأت السلسلة بفيلم مباريات الجوع الذي صدر في العام 2012، ثم فيلم في العام 2013، وتبعهما كل من (2014) وأخيرًا: (2015). كان لجينيفر لورنس حصة الأسد في سجل الترشيحات والجوائز التي نالتها السلسلة.', 'source': 'https://ar.wikipedia.org/wiki?curid=6237097', 'question': 'متى صدر الفيلم الأول من سلسلة مباريات الجوع؟', 'answer': 'عام 2012', 'has_answer': True }, { 'title': 'سانت فرنسيس (ويسكونسن)', 'text': 'بلغ عدد الأسر 4,494 أسرة كانت نسبة 19.8% منها لديها أطفال تحت سن الثامنة عشر تعيش معهم، وبلغت نسبة الأزواج القاطنين مع بعضهم البعض 36.6% من أصل المجموع الكلي للأسر، ونسبة 8.7% من الأسر كان لديها معيلات من الإناث دون وجود شريك، بينما كانت نسبة 3.9% من الأسر لديها معيلون من الذكور دون وجود شريكة وكانت نسبة 50.8% من غير العائلات. تألفت نسبة 42.6% من أصل جميع الأسر من أفراد ونسبة 13.7% كانوا يعيش معهم شخص وحيد يبلغ من العمر 65 عاماً فما فوق. وبلغ متوسط حجم الأسرة المعيشية 2.80، أما متوسط حجم العائلات فبلغ 2.02.', 'source': 'https://ar.wikipedia.org/wiki?curid=2198358', 'question': 'ما هو عدد العائلات المقيمة في سانت فرنسيس؟', 'answer': '', 'has_answer': False } ``` ## Getting Started To get started with the **Sadeem QnA** dataset, you can download it directly from our [Huggingface repository](https://huggingface.co/datasets/sadeem-ai/arabic-qna). Follow the instructions there to load the dataset into your environment and begin exploring. ## Usage This dataset is perfect for: - Training machine learning models for Arabic question answering. - Evaluating the performance of NLP models on Arabic text. - Enhancing language understanding systems with a focus on Arabic. ## Contributing We welcome contributions from the community! Whether it's improving the documentation, adding more questions, or reporting issues, your help makes **Sadeem QnA** better for everyone. ## License The **Sadeem QnA** dataset is available under the Apache License 2.0. We encourage its use for academic research, commercial applications, and beyond, provided proper attribution is given. ## Citation If you use the **Sadeem QnA** dataset in your research, please cite it using the following format: ```bibtex @misc{sadeem_qna, title={Sadeem QnA: An Arabic QnA Dataset}, author={}, year={2024}, publisher={Huggingface}, howpublished={\url{https://huggingface.co/datasets/sadeem-ai/arabic-qna}}, } ``` Embark on your journey through the Arabic language with **Sadeem QnA** and unlock the potential of AI in understanding the complexity and beauty of Arabic text. 🚀💡

jlbaker361/league-maybe-runway-50

--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: seed dtype: int64 - name: steps dtype: int64 splits: - name: train num_bytes: 30718722.0 num_examples: 72 download_size: 30717346 dataset_size: 30718722.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

dhematillake/instructpix2pix-spatial

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: transformed_image dtype: image splits: - name: train num_bytes: 9300429930.63 num_examples: 9447 download_size: 8998126312 dataset_size: 9300429930.63 configs: - config_name: default data_files: - split: train path: data/train-* ---

AlekseyKorshuk/clean-dataset-preview

--- dataset_info: features: - name: input_text dtype: string - name: output_text dtype: string - name: check_word_number_criteria dtype: int64 splits: - name: train num_bytes: 686595411 num_examples: 337253 download_size: 283358430 dataset_size: 686595411 --- # Dataset Card for "clean-dataset-preview" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

RZ412/mmlu_responses_1k

--- dataset_info: features: - name: exemplar_questions dtype: string - name: test_questions dtype: string - name: subject dtype: string - name: answers list: - name: answer dtype: string - name: model dtype: string - name: reference_answers dtype: int64 splits: - name: train num_bytes: 5847672 num_examples: 1000 download_size: 391151 dataset_size: 5847672 configs: - config_name: default data_files: - split: train path: data/train-* ---

eduagarcia-temp/brwac_meta

--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: dedup struct: - name: exact_norm struct: - name: cluster_main_idx dtype: int64 - name: cluster_size dtype: int64 - name: exact_hash_idx dtype: int64 - name: is_duplicate dtype: bool - name: minhash struct: - name: cluster_main_idx dtype: int64 - name: cluster_size dtype: int64 - name: is_duplicate dtype: bool - name: minhash_idx dtype: int64 - name: doc_id dtype: string - name: title dtype: string - name: uri dtype: string splits: - name: train num_bytes: 18279917379 num_examples: 3530796 download_size: 11165124126 dataset_size: 18279917379 --- # Dataset Card for "brwac_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

SDbiaseval/faces

--- dataset_info: features: - name: model dtype: string - name: adjective dtype: string - name: profession dtype: string - name: 'no' dtype: int32 - name: image_name dtype: string - name: image dtype: image splits: - name: train num_bytes: 3432470489.253643 num_examples: 88708 download_size: 1970670181 dataset_size: 3432470489.253643 --- # Dataset Card for "faces" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tkuhn1988/tkuhnstyle

--- license: afl-3.0 ---

trl-internal-testing/mlabonne-chatml-dpo-pairs-copy

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 35914686 num_examples: 12859 download_size: 19539812 dataset_size: 35914686 configs: - config_name: default data_files: - split: train path: data/train-* --- This is a copy and unmaintained version of [`mlabonne/chatml_dpo_pairs`](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) that we use in TRL CI for testing purpose. Please refer to the original dataset for usage and more details

vietgpt-archive/vungoi_question_type1

--- dataset_info: features: - name: question_raw dtype: string - name: options_raw list: - name: answer_raw dtype: string - name: key dtype: string - name: answer_raw struct: - name: answer_raw dtype: string - name: key dtype: string - name: solution_raw dtype: string - name: metadata struct: - name: chapter dtype: string - name: difficult_degree dtype: int64 - name: grade dtype: string - name: id dtype: string - name: idx dtype: int64 - name: subject dtype: string splits: - name: train num_bytes: 137805960 num_examples: 112037 download_size: 78332631 dataset_size: 137805960 --- # Dataset Card for "vungoi_question_type1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Kalva014/male-asian-hairstyles

--- license: mit ---

alwanrahmana/ner_scientifict_resampled

--- license: apache-2.0 ---

longhoang06/MC-ViMath

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: explanation dtype: string - name: answer dtype: string splits: - name: train num_bytes: 6086303 num_examples: 9328 download_size: 3016997 dataset_size: 6086303 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "MC-ViMath" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

huggingartists/kendrick-lamar

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/kendrick-lamar" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [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) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 2.493223 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f08637c8cfdeaab4dfbf0631424001ec.640x640x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/kendrick-lamar"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Kendrick Lamar</div> <a href="https://genius.com/artists/kendrick-lamar"> <div style="text-align: center; font-size: 14px;">@kendrick-lamar</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/kendrick-lamar). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/kendrick-lamar") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |861| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/kendrick-lamar") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

tellarin-ai/ntx_llm_instructions

--- license: cc-by-sa-4.0 language: - ar - de - en - es - fr - hi - it - ja - ko - nl - pt - sv - tr - zh task_categories: - token-classification --- # Dataset Card for NTX v1 in the Aya format This dataset is a format conversion from its original v1 format into the Aya instruction format and it's released here under the CC-BY-SA 4.0 license and conditions. It contains data in multiple languages and this version is intended for multi-lingual LLM construction/tuning. ## Citation If you utilize this dataset version, feel free to cite/footnote this huggingface dataset repo, but please also cite the original dataset publication. **BibTeX:** ``` @preprint{chen2023dataset, title={Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions}, author={Sanxing Chen and Yongqiang Chen and Börje F. Karlsson}, year={2023}, eprint={2303.18103}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Dataset Details For the original NTX dataset for information extraction of numerical and temporal expressions and more details, please check the arXiv paper: https://arxiv.org/abs/2303.18103. **NOTE: ** Unfortunately, due to a conversion issue with numerical expressions, this version here only includes the temporal expressions part of NTX. ## Format Conversion Details The templates used to reformat the dataset are in the ./templates-ntx directory.

liuyanchen1015/MULTI_VALUE_rte_what_comparative

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 1345 num_examples: 5 - name: train num_bytes: 668 num_examples: 2 download_size: 8739 dataset_size: 2013 --- # Dataset Card for "MULTI_VALUE_rte_what_comparative" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_mrpc_anaphoric_it

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 18672 num_examples: 64 - name: train num_bytes: 42888 num_examples: 150 - name: validation num_bytes: 2973 num_examples: 11 download_size: 54711 dataset_size: 64533 --- # Dataset Card for "MULTI_VALUE_mrpc_anaphoric_it" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mohamedsaeed823/egyARA2eng

--- license: apache-2.0 ---

AlekseyKorshuk/chai-experiment-v0-chatml

--- dataset_info: features: - name: source dtype: string - name: conversation list: - name: content dtype: string - name: do_train dtype: bool - name: role dtype: string splits: - name: train num_bytes: 1765802637.0 num_examples: 322064 download_size: 909265481 dataset_size: 1765802637.0 --- # Dataset Card for "chai-experiment-v0-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

metabloit/offensive-swahili-text

--- license: mit task_categories: - text-classification language: - sw size_categories: - 1K<n<10K viewer: true --- # Overview This dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist. The dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse. ## Dataset details The dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset consists of 990 sentences and the test dataset consists of 660 sentences. ### Dataset annotations - 0: non-offensive - 1: offensive

arbml/ArSAS

--- dataset_info: features: - name: '#Tweet_ID' dtype: string - name: Tweet_text dtype: string - name: Topic dtype: string - name: Sentiment_label_confidence dtype: string - name: Speech_act_label dtype: string - name: Speech_act_label_confidence dtype: string - name: label dtype: class_label: names: 0: Negative 1: Neutral 2: Positive 3: Mixed splits: - name: train num_bytes: 6147723 num_examples: 19897 download_size: 2998319 dataset_size: 6147723 --- # Dataset Card for "ArSAS" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

0xMaka/trading-candles-subset-sc-format

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': Bearish '1': Bullish splits: - name: train num_bytes: 11878595.339187406 num_examples: 155885 - name: test num_bytes: 5090913.660812595 num_examples: 66809 download_size: 6788665 dataset_size: 16969509.0 --- # Dataset Card for "trading-candles-subset-sc-format" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dim/lmsys_chatbot_arena_conversations

--- dataset_info: features: - name: question_id dtype: string - name: model_a dtype: string - name: model_b dtype: string - name: winner dtype: string - name: judge dtype: string - name: conversation_a list: - name: content dtype: string - name: role dtype: string - name: conversation_b list: - name: content dtype: string - name: role dtype: string - name: turn dtype: int64 - name: anony dtype: bool - name: language dtype: string - name: tstamp dtype: float64 - name: openai_moderation struct: - name: categories struct: - name: harassment dtype: bool - name: harassment/threatening dtype: bool - name: hate dtype: bool - name: hate/threatening dtype: bool - name: self-harm dtype: bool - name: self-harm/instructions dtype: bool - name: self-harm/intent dtype: bool - name: sexual dtype: bool - name: sexual/minors dtype: bool - name: violence dtype: bool - name: violence/graphic dtype: bool - name: category_scores struct: - name: harassment dtype: float64 - name: harassment/threatening dtype: float64 - name: hate dtype: float64 - name: hate/threatening dtype: float64 - name: self-harm dtype: float64 - name: self-harm/instructions dtype: float64 - name: self-harm/intent dtype: float64 - name: sexual dtype: float64 - name: sexual/minors dtype: float64 - name: violence dtype: float64 - name: violence/graphic dtype: float64 - name: flagged dtype: bool - name: toxic_chat_tag struct: - name: roberta-large struct: - name: flagged dtype: bool - name: probability dtype: float64 - name: t5-large struct: - name: flagged dtype: bool - name: score dtype: float64 splits: - name: train num_bytes: 81159839 num_examples: 33000 download_size: 41573740 dataset_size: 81159839 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "lmsys_chatbot_arena_conversations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

notfan/fa_pim_qalog

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 463142 num_examples: 742 download_size: 264389 dataset_size: 463142 configs: - config_name: default data_files: - split: train path: data/train-* ---

TrainingDataPro/llm-dataset

--- license: cc-by-nc-nd-4.0 task_categories: - text-classification - table-question-answering - question-answering - text2text-generation - text-generation language: - en - es - ar - el - fr - ja - pt - uk - az - ga - ko - ca - eo - hi - ml - sl - hu - mr - cs - fa - id - nl - th - de - fi - it - pl - tr tags: - code - legal - finance --- # LLM Dataset - Prompts and Generated Texts The dataset contains prompts and texts generated by the Large Language Models (LLMs) in **32 different languages**. The prompts are short sentences or phrases for the model to generate text. The texts generated by the LLM are responses to these prompts and can vary in **length and complexity**. Researchers and developers can use this dataset to train and fine-tune their own language models for multilingual applications. The dataset provides a rich and diverse collection of outputs from the model, demonstrating its ability to generate coherent and contextually relevant text in multiple languages. # 💴 For Commercial Usage: Full version of the dataset includes **4,000,000 logs** generated in **32 languages** with diferent types of LLM, including Uncensored GPT, leave a request on **[TrainingData](https://trainingdata.pro/data-market/llm?utm_source=huggingface&utm_medium=cpc&utm_campaign=llm)** to buy the dataset ### Models used for text generation: - **GPT-3.5**, - **GPT-4** ### Languages in the dataset: *Arabic, Azerbaijani, Catalan, Chinese, Czech, Danish, German, Greek, English, Esperanto, Spanish, Persian, Finnish, French, Irish, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Malayalam, Maratham, Netherlands, Polish, Portuguese, Portuguese (Brazil), Slovak, Swedish, Thai, Turkish, Ukrainian* ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Ff60c93f09ec82a765aa39678e4aa0a58%2Fsnapedit_1709731090855.jpeg?generation=1709738798916444&alt=media) # Content CSV File includes the following data: - **from_language**: language the prompt is made in, - **model**: type of the model (GPT-3.5, GPT-4 and Uncensored GPT Version), - **time**: time when the answer was generated, - **text**: user prompt, - **response**: response generated by the model # 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/llm?utm_source=huggingface&utm_medium=cpc&utm_campaign=llm)** to discuss your requirements, learn about the price and buy the dataset ## **[TrainingData](https://trainingdata.pro/data-market/llm?utm_source=huggingface&utm_medium=cpc&utm_campaign=llm)** provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** *keywords: dataset, machine learning, natural language processing, artificial intelligence, deep learning, neural networks, text generation, language models, openai, gpt-3, data science, predictive modeling, sentiment analysis, keyword extraction, text classification, sequence-to-sequence models, attention mechanisms, transformer architecture, word embeddings, glove embeddings, chatbots, question answering, language understanding, text mining, information retrieval, data preprocessing, feature engineering, explainable ai, model deployment*

open-llm-leaderboard/details_Danielbrdz__Barcenas-7b

--- pretty_name: Evaluation run of Danielbrdz/Barcenas-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Danielbrdz/Barcenas-7b](https://huggingface.co/Danielbrdz/Barcenas-7b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 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 agregated 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_Danielbrdz__Barcenas-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T23:34:07.541919](https://huggingface.co/datasets/open-llm-leaderboard/details_Danielbrdz__Barcenas-7b/blob/main/results_2023-09-17T23-34-07.541919.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 \"em\": 0.004718959731543624,\n\ \ \"em_stderr\": 0.0007018360183131257,\n \"f1\": 0.0816715604026848,\n\ \ \"f1_stderr\": 0.0017762083839348887,\n \"acc\": 0.39889766050552516,\n\ \ \"acc_stderr\": 0.009497938418122394\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.004718959731543624,\n \"em_stderr\": 0.0007018360183131257,\n\ \ \"f1\": 0.0816715604026848,\n \"f1_stderr\": 0.0017762083839348887\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06141015921152388,\n \ \ \"acc_stderr\": 0.006613027536586322\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7363851617995264,\n \"acc_stderr\": 0.012382849299658464\n\ \ }\n}\n```" repo_url: https://huggingface.co/Danielbrdz/Barcenas-7b 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_08_28T22_47_45.353935 path: - '**/details_harness|arc:challenge|25_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-28T22:47:45.353935.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T23_34_07.541919 path: - '**/details_harness|drop|3_2023-09-17T23-34-07.541919.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T23-34-07.541919.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T23_34_07.541919 path: - '**/details_harness|gsm8k|5_2023-09-17T23-34-07.541919.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T23-34-07.541919.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hellaswag|10_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-28T22:47:45.353935.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-management|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T22:47:45.353935.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_28T22_47_45.353935 path: - '**/details_harness|truthfulqa:mc|0_2023-08-28T22:47:45.353935.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-28T22:47:45.353935.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T23_34_07.541919 path: - '**/details_harness|winogrande|5_2023-09-17T23-34-07.541919.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T23-34-07.541919.parquet' - config_name: results data_files: - split: 2023_08_28T22_47_45.353935 path: - results_2023-08-28T22:47:45.353935.parquet - split: 2023_09_17T23_34_07.541919 path: - results_2023-09-17T23-34-07.541919.parquet - split: latest path: - results_2023-09-17T23-34-07.541919.parquet --- # Dataset Card for Evaluation run of Danielbrdz/Barcenas-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Danielbrdz/Barcenas-7b - **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 [Danielbrdz/Barcenas-7b](https://huggingface.co/Danielbrdz/Barcenas-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 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 agregated 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_Danielbrdz__Barcenas-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T23:34:07.541919](https://huggingface.co/datasets/open-llm-leaderboard/details_Danielbrdz__Barcenas-7b/blob/main/results_2023-09-17T23-34-07.541919.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": { "em": 0.004718959731543624, "em_stderr": 0.0007018360183131257, "f1": 0.0816715604026848, "f1_stderr": 0.0017762083839348887, "acc": 0.39889766050552516, "acc_stderr": 0.009497938418122394 }, "harness|drop|3": { "em": 0.004718959731543624, "em_stderr": 0.0007018360183131257, "f1": 0.0816715604026848, "f1_stderr": 0.0017762083839348887 }, "harness|gsm8k|5": { "acc": 0.06141015921152388, "acc_stderr": 0.006613027536586322 }, "harness|winogrande|5": { "acc": 0.7363851617995264, "acc_stderr": 0.012382849299658464 } } ``` ### 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]

nqv2291/en-Pile-NER-seq2seq_format

--- dataset_info: features: - name: id dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 664745884 num_examples: 350974 - name: test num_bytes: 13593212 num_examples: 7208 download_size: 147499047 dataset_size: 678339096 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

zpn/zinc20

--- license: mit dataset_info: features: - name: selfies dtype: string - name: smiles dtype: string - name: id dtype: string splits: - name: train num_bytes: 238295712864 num_examples: 804925861 - name: validation num_bytes: 26983481360 num_examples: 100642661 - name: test num_bytes: 29158755632 num_examples: 101082073 download_size: 40061255073 dataset_size: 294437949856 tags: - bio - selfies - smiles - small_molecules pretty_name: zinc20 size_categories: - 1B<n<10B --- # Dataset Card for Zinc20 ## Dataset Description - **Homepage:** https://zinc20.docking.org/ - **Paper:** https://pubs.acs.org/doi/10.1021/acs.jcim.0c00675 ### Dataset Summary ZINC is a publicly available database that aggregates commercially available and annotated compounds. ZINC provides downloadable 2D and 3D versions as well as a website that enables rapid molecule lookup and analog search. ZINC has grown from fewer than 1 million compounds in 2005 to nearly 2 billion now. This dataset includes ~1B molecules in total. We have filtered out any compounds that were not avaible to be converted from `smiles` to `seflies` representations. ### 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 The dataset is split into an 80/10/10 train/valid/test random split across files (which roughly corresponds to the same percentages) ### Source Data #### Initial Data Collection and Normalization Initial data was released at https://zinc20.docking.org/. We have downloaded and added a `selfies` field and filtered out all molecules that did not contain molecules that could be converted to `selfies` representations. ### Citation Information @article{Irwin2020, doi = {10.1021/acs.jcim.0c00675}, url = {https://doi.org/10.1021/acs.jcim.0c00675}, year = {2020}, month = oct, publisher = {American Chemical Society ({ACS})}, volume = {60}, number = {12}, pages = {6065--6073}, author = {John J. Irwin and Khanh G. Tang and Jennifer Young and Chinzorig Dandarchuluun and Benjamin R. Wong and Munkhzul Khurelbaatar and Yurii S. Moroz and John Mayfield and Roger A. Sayle}, title = {{ZINC}20{\textemdash}A Free Ultralarge-Scale Chemical Database for Ligand Discovery}, journal = {Journal of Chemical Information and Modeling} } ### Contributions This dataset was curated and added by [@zanussbaum](https://github.com/zanussbaum).

autoevaluate/autoeval-staging-eval-project-200453bd-7694959

--- type: predictions tags: - autotrain - evaluation datasets: - masakhaner eval_info: task: entity_extraction model: arnolfokam/bert-base-uncased-swa metrics: [] dataset_name: masakhaner dataset_config: swa dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: arnolfokam/bert-base-uncased-swa * Dataset: masakhaner To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

emozilla/sat-reading

--- dataset_info: features: - name: text dtype: string - name: answer dtype: string - name: requires_line dtype: bool - name: id dtype: string splits: - name: train num_bytes: 1399648 num_examples: 298 - name: test num_bytes: 196027 num_examples: 38 - name: validation num_bytes: 183162 num_examples: 39 download_size: 365469 dataset_size: 1778837 language: - en --- # Dataset Card for "sat-reading" This dataset contains the passages and questions from the Reading part of ten publicly available SAT Practice Tests. For more information see the blog post [Language Models vs. The SAT Reading Test](https://jeffq.com/blog/language-models-vs-the-sat-reading-test). For each question, the reading passage from the section it is contained in is prefixed. Then, the question is prompted with `Question #:`, followed by the four possible answers. Each entry ends with `Answer:`. Questions which reference a diagram, chart, table, etc. have been removed (typically three per test). In addition, there is a boolean `requires_line` feature, which indiciates if the question references specific lines within the passage. To maintain generalizability in finetuning scenarios, `SAT READING COMPREHENSION TEST` appears at the beginning of each entry -- it may be desireable to remove this depending on your intentions. Eight tests appear in the training split; one each in the validation and test splits.

sgoedecke/5s_birdcall_samples_16k

--- dataset_info: features: - name: audio dtype: audio - name: label dtype: string - name: input_values sequence: float32 - name: attention_mask sequence: int32 splits: - name: train num_bytes: 2149373508.375 num_examples: 4797 - name: test num_bytes: 2149821590.25 num_examples: 4798 download_size: 3704408556 dataset_size: 4299195098.625 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647_random

--- 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_1_bs_16 num_bytes: 85893233.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 88896569.125 num_examples: 5647 download_size: 168043271 dataset_size: 174789802.25 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-futin__feed-top_en_-3f631c-2246071662

--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-30b metrics: [] dataset_name: futin/feed dataset_config: top_en_ 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-30b * Dataset: futin/feed * Config: top_en_ * 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.

Back-up/Topic-Prediction-Context-With-Random-Prompts-in-the-end

--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: topic struct: - name: topic dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: instruction dtype: string - name: prompt_name dtype: string splits: - name: train num_bytes: 248398 num_examples: 101 download_size: 125460 dataset_size: 248398 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Topic-Prediction-Context-With-Random-Prompts-in-the-end" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

NghiemAbe/Legal_Corpus

--- dataset_info: features: - name: id dtype: string - name: ministry dtype: string - name: type dtype: string - name: name dtype: string - name: chapter_id dtype: string - name: chapter_name dtype: string - name: article dtype: string - name: content dtype: string splits: - name: corpus num_bytes: 1071858956 num_examples: 515188 download_size: 294811460 dataset_size: 1071858956 --- # Dataset Card for "Legal_Corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DazMashaly/test_data

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 365263950.94 num_examples: 5108 download_size: 354753479 dataset_size: 365263950.94 --- # Dataset Card for "test_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fancyzhx/dbpedia_14

--- annotations_creators: - machine-generated language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - topic-classification paperswithcode_id: dbpedia pretty_name: DBpedia dataset_info: config_name: dbpedia_14 features: - name: label dtype: class_label: names: '0': Company '1': EducationalInstitution '2': Artist '3': Athlete '4': OfficeHolder '5': MeanOfTransportation '6': Building '7': NaturalPlace '8': Village '9': Animal '10': Plant '11': Album '12': Film '13': WrittenWork - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 178428970 num_examples: 560000 - name: test num_bytes: 22310285 num_examples: 70000 download_size: 119424374 dataset_size: 200739255 configs: - config_name: dbpedia_14 data_files: - split: train path: dbpedia_14/train-* - split: test path: dbpedia_14/test-* default: true --- # Dataset Card for DBpedia14 ## 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:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Repository:** https://github.com/zhangxiangxiao/Crepe - **Paper:** https://arxiv.org/abs/1509.01626 - **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu) ### Dataset Summary The DBpedia ontology classification dataset is constructed by picking 14 non-overlapping classes from DBpedia 2014. They are listed in classes.txt. From each of thse 14 ontology classes, we randomly choose 40,000 training samples and 5,000 testing samples. Therefore, the total size of the training dataset is 560,000 and testing dataset 70,000. There are 3 columns in the dataset (same for train and test splits), corresponding to class index (1 to 14), title and content. The title and content are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). There are no new lines in title or content. ### Supported Tasks and Leaderboards - `text-classification`, `topic-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct topic. ### Languages Although DBpedia is a multilingual knowledge base, the DBpedia14 extract contains English data mainly, other languages may appear (e.g. a film whose title is origanlly not English). ## Dataset Structure ### Data Instances A typical data point, comprises of a title, a content and the corresponding label. An example from the DBpedia test set looks as follows: ``` { 'title':'', 'content':" TY KU /taɪkuː/ is an American alcoholic beverage company that specializes in sake and other spirits. The privately-held company was founded in 2004 and is headquartered in New York City New York. While based in New York TY KU's beverages are made in Japan through a joint venture with two sake breweries. Since 2011 TY KU's growth has extended its products into all 50 states.", 'label':0 } ``` ### Data Fields - 'title': a string containing the title of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). - 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). - 'label': one of the 14 possible topics. ### Data Splits The data is split into a training and test set. For each of the 14 classes we have 40,000 training samples and 5,000 testing samples. Therefore, the total size of the training dataset is 560,000 and testing dataset 70,000. ## Dataset Creation ### Curation Rationale The DBPedia ontology classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu), licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Source Data #### Initial Data Collection and Normalization Source data is taken from DBpedia: https://wiki.dbpedia.org/develop/datasets #### 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 The DBPedia ontology classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu), licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). ### Licensing Information The DBPedia ontology classification dataset is licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License. ### Citation Information ``` @inproceedings{NIPS2015_250cf8b5, author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann}, booktitle = {Advances in Neural Information Processing Systems}, editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett}, pages = {}, publisher = {Curran Associates, Inc.}, title = {Character-level Convolutional Networks for Text Classification}, url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/250cf8b51c773f3f8dc8b4be867a9a02-Paper.pdf}, volume = {28}, year = {2015} } ``` Lehmann, Jens, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann et al. "DBpedia–a large-scale, multilingual knowledge base extracted from Wikipedia." Semantic web 6, no. 2 (2015): 167-195. ### Contributions Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.

satendra4u2022/dpo_precise_datasets

--- license: mit ---

vertigo23/njogerera_english_luganda_corpus

--- license: unknown size_categories: - 10K<n<100K ---

aryamannningombam/indian-female-tts-dataset

--- dataset_info: features: - name: file dtype: string - name: text dtype: string - name: tag dtype: string - name: file_path dtype: string - name: y sequence: float32 - name: emotional_embedding sequence: int64 - name: non_characters dtype: 'null' - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 923807734 num_examples: 2843 download_size: 911770863 dataset_size: 923807734 configs: - config_name: default data_files: - split: train path: data/train-* ---

Svenni551/NPC_talks

--- dataset_info: features: - name: prompt dtype: string - name: output dtype: string splits: - name: train num_bytes: 38012 num_examples: 10 download_size: 31737 dataset_size: 38012 configs: - config_name: default data_files: - split: train path: data/train-* ---

NickyNicky/h2ogpt-oig-oasst1-instruct-cleaned-v3

--- dataset_info: features: - name: list_json list: - name: bot dtype: string - name: human dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 164763347 num_examples: 269406 download_size: 105519939 dataset_size: 164763347 configs: - config_name: default data_files: - split: train path: data/train-* ---

benchang1110/sciencetw

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: title dtype: string - name: article dtype: string splits: - name: train num_bytes: 157012034 num_examples: 26056 download_size: 105424999 dataset_size: 157012034 --- # Dataset Card for "sciencetw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

NUS-IDS/beyond_blue

--- configs: - config_name: default data_files: - split: anxiety path: data/anxiety-* - split: depression path: data/depression-* - split: ptsd path: data/ptsd-* dataset_info: features: - name: url dtype: string - name: comments list: - name: author dtype: string - name: content dtype: string - name: date dtype: string - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: content dtype: string - name: author dtype: string splits: - name: anxiety num_bytes: 56172807 num_examples: 6943 - name: depression num_bytes: 60224734 num_examples: 6008 - name: ptsd num_bytes: 21141031 num_examples: 1816 download_size: 68731517 dataset_size: 137538572 --- # Dataset Card for "beyond_blue" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nbtpj/Movies_and_TV

--- dataset_info: features: - name: overall dtype: float64 - name: verified dtype: bool - name: reviewTime dtype: string - name: reviewerID dtype: string - name: asin dtype: string - name: style dtype: string - name: reviewerName dtype: string - name: reviewText dtype: string - name: summary dtype: string - name: unixReviewTime dtype: int64 - name: vote dtype: string - name: image sequence: string splits: - name: train num_bytes: 4058038162 num_examples: 8765568 download_size: 2295911945 dataset_size: 4058038162 --- # Dataset Card for "Movies_and_TV" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/nagayoshi_subaru_theidolmstermillionlive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nagayoshi_subaru/永吉昴 (THE iDOLM@STER: Million Live!) This is the dataset of nagayoshi_subaru/永吉昴 (THE iDOLM@STER: Million Live!), containing 229 images and their tags. The core tags of this character are `short_hair, green_hair, red_eyes, bangs, brown_hair`, 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 | 229 | 242.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 229 | 154.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 519 | 319.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 229 | 219.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 519 | 431.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/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/nagayoshi_subaru_theidolmstermillionlive', 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) | 1girl, looking_at_viewer, solo, long_sleeves, white_gloves, ascot, closed_mouth, crown, hat, purple_eyes, white_background, white_jacket, white_pants, blush, epaulettes, grin, hair_between_eyes, hair_ornament, simple_background, upper_body | | 1 | 12 | ![](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, looking_at_viewer, open_mouth, :d, solo, purple_eyes, dress, jewelry, necktie | | 2 | 8 | ![](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, looking_at_viewer, purple_eyes, solo, open_mouth, :d, baseball, letterman_jacket, shorts | | 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, blush, looking_at_viewer, solo, open_mouth, skirt, smile, jewelry | | 4 | 12 | ![](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, hair_between_eyes, blush, looking_at_viewer, simple_background, solo, smile, white_background, white_shirt, upper_body, open_mouth, collarbone, jacket, short_sleeves | | 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) | 1girl, blush, solo, cleavage, looking_at_viewer, medium_breasts, navel, collarbone, hair_between_eyes, sitting, smile, striped_bikini, beachball, one_eye_closed, open_mouth, partially_submerged, short_shorts, water | | 6 | 6 | ![](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, blush, censored, nipples, pussy, small_breasts, hetero, on_back, open_mouth, solo_focus, 1boy, spread_legs, hair_between_eyes, navel, nude, on_bed, penis, pillow, sex, sweat | | 7 | 5 | ![](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, detached_collar, playboy_bunny, rabbit_ears, solo, fake_animal_ears, strapless_leotard, wrist_cuffs, blush, indian_style, looking_at_viewer, pantyhose, rabbit_tail, simple_background, small_breasts, black_bowtie, black_leotard, cleavage, covered_navel, purple_eyes, red_bowtie, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | long_sleeves | white_gloves | ascot | closed_mouth | crown | hat | purple_eyes | white_background | white_jacket | white_pants | blush | epaulettes | grin | hair_between_eyes | hair_ornament | simple_background | upper_body | open_mouth | :d | dress | jewelry | necktie | baseball | letterman_jacket | shorts | skirt | smile | white_shirt | collarbone | jacket | short_sleeves | cleavage | medium_breasts | navel | sitting | striped_bikini | beachball | one_eye_closed | partially_submerged | short_shorts | water | censored | nipples | pussy | small_breasts | hetero | on_back | solo_focus | 1boy | spread_legs | nude | on_bed | penis | pillow | sex | sweat | detached_collar | playboy_bunny | rabbit_ears | fake_animal_ears | strapless_leotard | wrist_cuffs | indian_style | pantyhose | rabbit_tail | black_bowtie | black_leotard | covered_navel | red_bowtie | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:---------------|:---------------|:--------|:---------------|:--------|:------|:--------------|:-------------------|:---------------|:--------------|:--------|:-------------|:-------|:--------------------|:----------------|:--------------------|:-------------|:-------------|:-----|:--------|:----------|:----------|:-----------|:-------------------|:---------|:--------|:--------|:--------------|:-------------|:---------|:----------------|:-----------|:-----------------|:--------|:----------|:-----------------|:------------|:-----------------|:----------------------|:---------------|:--------|:-----------|:----------|:--------|:----------------|:---------|:----------|:-------------|:-------|:--------------|:-------|:---------|:--------|:---------|:------|:--------|:------------------|:----------------|:--------------|:-------------------|:--------------------|:--------------|:---------------|:------------|:--------------|:---------------|:----------------|:----------------|:-------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 12 | ![](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 | 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 | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 6 | ![](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 | 5 | ![](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 |

acheong08/nsfw_reddit

--- license: openrail ---

yangyz1230/promoter_tata

--- dataset_info: features: - name: name dtype: string - name: sequence dtype: string - name: chrom dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: strand dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 954157 num_examples: 2732 - name: test num_bytes: 115635 num_examples: 332 download_size: 519991 dataset_size: 1069792 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-23000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1080073 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

jacquelinehe/enron-emails

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1701056967 num_examples: 926132 download_size: 972216068 dataset_size: 1701056967 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_wandb__pruned_mistral

--- pretty_name: Evaluation run of wandb/pruned_mistral dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [wandb/pruned_mistral](https://huggingface.co/wandb/pruned_mistral) 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_wandb__pruned_mistral\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T16:40:40.526366](https://huggingface.co/datasets/open-llm-leaderboard/details_wandb__pruned_mistral/blob/main/results_2024-03-21T16-40-40.526366.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\": 0.2677672008134633,\n\ \ \"acc_stderr\": 0.031196121816193134,\n \"acc_norm\": 0.26979863634977536,\n\ \ \"acc_norm_stderr\": 0.03200972209199791,\n \"mc1\": 0.24724602203182375,\n\ \ \"mc1_stderr\": 0.015102404797359652,\n \"mc2\": 0.4108681590294748,\n\ \ \"mc2_stderr\": 0.014542287705752187\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.24829351535836178,\n \"acc_stderr\": 0.012624912868089764,\n\ \ \"acc_norm\": 0.2832764505119454,\n \"acc_norm_stderr\": 0.013167478735134575\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.37353116908982276,\n\ \ \"acc_stderr\": 0.004827526584889684,\n \"acc_norm\": 0.46345349531965746,\n\ \ \"acc_norm_stderr\": 0.004976434387469965\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3037037037037037,\n\ \ \"acc_stderr\": 0.039725528847851375,\n \"acc_norm\": 0.3037037037037037,\n\ \ \"acc_norm_stderr\": 0.039725528847851375\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123415,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123415\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.18,\n\ \ \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.18,\n \ \ \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.24305555555555555,\n\ \ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.24305555555555555,\n\ \ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n\ \ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\ \ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237653,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237653\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n\ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.2127659574468085,\n \"acc_stderr\": 0.02675439134803977,\n\ \ \"acc_norm\": 0.2127659574468085,\n \"acc_norm_stderr\": 0.02675439134803977\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.0404933929774814,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.0404933929774814\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.03600105692727772,\n\ \ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.03600105692727772\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525218,\n \"\ acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525218\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15079365079365079,\n\ \ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\ \ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.25806451612903225,\n \"acc_stderr\": 0.024892469172462836,\n \"\ acc_norm\": 0.25806451612903225,\n \"acc_norm_stderr\": 0.024892469172462836\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.24630541871921183,\n \"acc_stderr\": 0.030315099285617715,\n \"\ acc_norm\": 0.24630541871921183,\n \"acc_norm_stderr\": 0.030315099285617715\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\"\ : {\n \"acc\": 0.20606060606060606,\n \"acc_stderr\": 0.0315841532404771,\n\ \ \"acc_norm\": 0.20606060606060606,\n \"acc_norm_stderr\": 0.0315841532404771\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.25757575757575757,\n \"acc_stderr\": 0.031156269519646857,\n \"\ acc_norm\": 0.25757575757575757,\n \"acc_norm_stderr\": 0.031156269519646857\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.30569948186528495,\n \"acc_stderr\": 0.03324837939758159,\n\ \ \"acc_norm\": 0.30569948186528495,\n \"acc_norm_stderr\": 0.03324837939758159\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.36153846153846153,\n \"acc_stderr\": 0.02435958146539699,\n\ \ \"acc_norm\": 0.36153846153846153,\n \"acc_norm_stderr\": 0.02435958146539699\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.23333333333333334,\n \"acc_stderr\": 0.02578787422095931,\n \ \ \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.02578787422095931\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.27310924369747897,\n \"acc_stderr\": 0.028942004040998167,\n\ \ \"acc_norm\": 0.27310924369747897,\n \"acc_norm_stderr\": 0.028942004040998167\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.271523178807947,\n \"acc_stderr\": 0.03631329803969653,\n \"acc_norm\"\ : 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969653\n },\n\ \ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.30458715596330277,\n\ \ \"acc_stderr\": 0.019732299420354038,\n \"acc_norm\": 0.30458715596330277,\n\ \ \"acc_norm_stderr\": 0.019732299420354038\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\ : {\n \"acc\": 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n\ \ \"acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.21568627450980393,\n \"acc_stderr\": 0.028867431449849303,\n \"\ acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.028867431449849303\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.23628691983122363,\n \"acc_stderr\": 0.027652153144159263,\n \ \ \"acc_norm\": 0.23628691983122363,\n \"acc_norm_stderr\": 0.027652153144159263\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3004484304932735,\n\ \ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.3004484304932735,\n\ \ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2748091603053435,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.2748091603053435,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.371900826446281,\n \"acc_stderr\": 0.044120158066245044,\n \"\ acc_norm\": 0.371900826446281,\n \"acc_norm_stderr\": 0.044120158066245044\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.24074074074074073,\n\ \ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.24074074074074073,\n\ \ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.27607361963190186,\n \"acc_stderr\": 0.0351238528370505,\n\ \ \"acc_norm\": 0.27607361963190186,\n \"acc_norm_stderr\": 0.0351238528370505\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.04059867246952689,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.04059867246952689\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.22330097087378642,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.22330097087378642,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.027236013946196697,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.027236013946196697\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\ \ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\ \ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24277456647398843,\n \"acc_stderr\": 0.023083658586984204,\n\ \ \"acc_norm\": 0.24277456647398843,\n \"acc_norm_stderr\": 0.023083658586984204\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2748603351955307,\n\ \ \"acc_stderr\": 0.014931316703220508,\n \"acc_norm\": 0.2748603351955307,\n\ \ \"acc_norm_stderr\": 0.014931316703220508\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.238562091503268,\n \"acc_stderr\": 0.024404394928087873,\n\ \ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.024404394928087873\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.26366559485530544,\n\ \ \"acc_stderr\": 0.02502553850053234,\n \"acc_norm\": 0.26366559485530544,\n\ \ \"acc_norm_stderr\": 0.02502553850053234\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.23148148148148148,\n \"acc_stderr\": 0.023468429832451166,\n\ \ \"acc_norm\": 0.23148148148148148,\n \"acc_norm_stderr\": 0.023468429832451166\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2553191489361702,\n \"acc_stderr\": 0.026011992930902006,\n \ \ \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.026011992930902006\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23989569752281617,\n\ \ \"acc_stderr\": 0.010906282617981634,\n \"acc_norm\": 0.23989569752281617,\n\ \ \"acc_norm_stderr\": 0.010906282617981634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.24019607843137256,\n \"acc_stderr\": 0.01728276069516742,\n \ \ \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.01728276069516742\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.22727272727272727,\n\ \ \"acc_stderr\": 0.040139645540727756,\n \"acc_norm\": 0.22727272727272727,\n\ \ \"acc_norm_stderr\": 0.040139645540727756\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.028920583220675585,\n\ \ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.028920583220675585\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n\ \ \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n\ \ \"acc_norm_stderr\": 0.030147775935409224\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2891566265060241,\n\ \ \"acc_stderr\": 0.03529486801511115,\n \"acc_norm\": 0.2891566265060241,\n\ \ \"acc_norm_stderr\": 0.03529486801511115\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.32748538011695905,\n \"acc_stderr\": 0.035993357714560276,\n\ \ \"acc_norm\": 0.32748538011695905,\n \"acc_norm_stderr\": 0.035993357714560276\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.24724602203182375,\n\ \ \"mc1_stderr\": 0.015102404797359652,\n \"mc2\": 0.4108681590294748,\n\ \ \"mc2_stderr\": 0.014542287705752187\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5390686661404893,\n \"acc_stderr\": 0.014009521680980318\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \ \ \"acc_stderr\": 0.0010717793485492608\n }\n}\n```" repo_url: https://huggingface.co/wandb/pruned_mistral 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_21T16_40_40.526366 path: - '**/details_harness|arc:challenge|25_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-21T16-40-40.526366.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|gsm8k|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hellaswag|10_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T16-40-40.526366.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T16-40-40.526366.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T16-40-40.526366.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T16_40_40.526366 path: - '**/details_harness|winogrande|5_2024-03-21T16-40-40.526366.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-21T16-40-40.526366.parquet' - config_name: results data_files: - split: 2024_03_21T16_40_40.526366 path: - results_2024-03-21T16-40-40.526366.parquet - split: latest path: - results_2024-03-21T16-40-40.526366.parquet --- # Dataset Card for Evaluation run of wandb/pruned_mistral  Dataset automatically created during the evaluation run of model [wandb/pruned_mistral](https://huggingface.co/wandb/pruned_mistral) 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_wandb__pruned_mistral", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T16:40:40.526366](https://huggingface.co/datasets/open-llm-leaderboard/details_wandb__pruned_mistral/blob/main/results_2024-03-21T16-40-40.526366.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": 0.2677672008134633, "acc_stderr": 0.031196121816193134, "acc_norm": 0.26979863634977536, "acc_norm_stderr": 0.03200972209199791, "mc1": 0.24724602203182375, "mc1_stderr": 0.015102404797359652, "mc2": 0.4108681590294748, "mc2_stderr": 0.014542287705752187 }, "harness|arc:challenge|25": { "acc": 0.24829351535836178, "acc_stderr": 0.012624912868089764, "acc_norm": 0.2832764505119454, "acc_norm_stderr": 0.013167478735134575 }, "harness|hellaswag|10": { "acc": 0.37353116908982276, "acc_stderr": 0.004827526584889684, "acc_norm": 0.46345349531965746, "acc_norm_stderr": 0.004976434387469965 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3037037037037037, "acc_stderr": 0.039725528847851375, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.039725528847851375 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123415, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123415 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2792452830188679, "acc_stderr": 0.027611163402399715, "acc_norm": 0.2792452830188679, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.24305555555555555, "acc_stderr": 0.0358687928008034, "acc_norm": 0.24305555555555555, "acc_norm_stderr": 0.0358687928008034 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237653, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237653 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2127659574468085, "acc_stderr": 0.02675439134803977, "acc_norm": 0.2127659574468085, "acc_norm_stderr": 0.02675439134803977 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.24561403508771928, "acc_stderr": 0.0404933929774814, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.0404933929774814 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2482758620689655, "acc_stderr": 0.03600105692727772, "acc_norm": 0.2482758620689655, "acc_norm_stderr": 0.03600105692727772 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2619047619047619, "acc_stderr": 0.022644212615525218, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.022644212615525218 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.15079365079365079, "acc_stderr": 0.03200686497287392, "acc_norm": 0.15079365079365079, "acc_norm_stderr": 0.03200686497287392 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.25806451612903225, "acc_stderr": 0.024892469172462836, "acc_norm": 0.25806451612903225, "acc_norm_stderr": 0.024892469172462836 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.24630541871921183, "acc_stderr": 0.030315099285617715, "acc_norm": 0.24630541871921183, "acc_norm_stderr": 0.030315099285617715 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.20606060606060606, "acc_stderr": 0.0315841532404771, "acc_norm": 0.20606060606060606, "acc_norm_stderr": 0.0315841532404771 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.25757575757575757, "acc_stderr": 0.031156269519646857, "acc_norm": 0.25757575757575757, "acc_norm_stderr": 0.031156269519646857 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.30569948186528495, "acc_stderr": 0.03324837939758159, "acc_norm": 0.30569948186528495, "acc_norm_stderr": 0.03324837939758159 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.36153846153846153, "acc_stderr": 0.02435958146539699, "acc_norm": 0.36153846153846153, "acc_norm_stderr": 0.02435958146539699 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.23333333333333334, "acc_stderr": 0.02578787422095931, "acc_norm": 0.23333333333333334, "acc_norm_stderr": 0.02578787422095931 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.27310924369747897, "acc_stderr": 0.028942004040998167, "acc_norm": 0.27310924369747897, "acc_norm_stderr": 0.028942004040998167 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.271523178807947, "acc_stderr": 0.03631329803969653, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.03631329803969653 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.30458715596330277, "acc_stderr": 0.019732299420354038, "acc_norm": 0.30458715596330277, "acc_norm_stderr": 0.019732299420354038 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4583333333333333, "acc_stderr": 0.03398110890294636, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.21568627450980393, "acc_stderr": 0.028867431449849303, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.028867431449849303 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.23628691983122363, "acc_stderr": 0.027652153144159263, "acc_norm": 0.23628691983122363, "acc_norm_stderr": 0.027652153144159263 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.3004484304932735, "acc_stderr": 0.03076935200822914, "acc_norm": 0.3004484304932735, "acc_norm_stderr": 0.03076935200822914 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2748091603053435, "acc_stderr": 0.03915345408847836, "acc_norm": 0.2748091603053435, "acc_norm_stderr": 0.03915345408847836 }, "harness|hendrycksTest-international_law|5": { "acc": 0.371900826446281, "acc_stderr": 0.044120158066245044, "acc_norm": 0.371900826446281, "acc_norm_stderr": 0.044120158066245044 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.24074074074074073, "acc_stderr": 0.04133119440243839, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.04133119440243839 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.27607361963190186, "acc_stderr": 0.0351238528370505, "acc_norm": 0.27607361963190186, "acc_norm_stderr": 0.0351238528370505 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.24107142857142858, "acc_stderr": 0.04059867246952689, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.04059867246952689 }, "harness|hendrycksTest-management|5": { "acc": 0.22330097087378642, "acc_stderr": 0.04123553189891431, "acc_norm": 0.22330097087378642, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2222222222222222, "acc_stderr": 0.027236013946196697, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.027236013946196697 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.23754789272030652, "acc_stderr": 0.015218733046150193, "acc_norm": 0.23754789272030652, "acc_norm_stderr": 0.015218733046150193 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24277456647398843, "acc_stderr": 0.023083658586984204, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.023083658586984204 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2748603351955307, "acc_stderr": 0.014931316703220508, "acc_norm": 0.2748603351955307, "acc_norm_stderr": 0.014931316703220508 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.238562091503268, "acc_stderr": 0.024404394928087873, "acc_norm": 0.238562091503268, "acc_norm_stderr": 0.024404394928087873 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.26366559485530544, "acc_stderr": 0.02502553850053234, "acc_norm": 0.26366559485530544, "acc_norm_stderr": 0.02502553850053234 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.23148148148148148, "acc_stderr": 0.023468429832451166, "acc_norm": 0.23148148148148148, "acc_norm_stderr": 0.023468429832451166 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2553191489361702, "acc_stderr": 0.026011992930902006, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.026011992930902006 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.23989569752281617, "acc_stderr": 0.010906282617981634, "acc_norm": 0.23989569752281617, "acc_norm_stderr": 0.010906282617981634 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4485294117647059, "acc_stderr": 0.030211479609121593, "acc_norm": 0.4485294117647059, "acc_norm_stderr": 0.030211479609121593 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.24019607843137256, "acc_stderr": 0.01728276069516742, "acc_norm": 0.24019607843137256, "acc_norm_stderr": 0.01728276069516742 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.22727272727272727, "acc_stderr": 0.040139645540727756, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.040139645540727756 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.2857142857142857, "acc_stderr": 0.028920583220675585, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.028920583220675585 }, "harness|hendrycksTest-sociology|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-virology|5": { "acc": 0.2891566265060241, "acc_stderr": 0.03529486801511115, "acc_norm": 0.2891566265060241, "acc_norm_stderr": 0.03529486801511115 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.32748538011695905, "acc_stderr": 0.035993357714560276, "acc_norm": 0.32748538011695905, "acc_norm_stderr": 0.035993357714560276 }, "harness|truthfulqa:mc|0": { "mc1": 0.24724602203182375, "mc1_stderr": 0.015102404797359652, "mc2": 0.4108681590294748, "mc2_stderr": 0.014542287705752187 }, "harness|winogrande|5": { "acc": 0.5390686661404893, "acc_stderr": 0.014009521680980318 }, "harness|gsm8k|5": { "acc": 0.001516300227445034, "acc_stderr": 0.0010717793485492608 } } ``` ## 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]

wav2gloss/cocoon-glosses

--- license: cc-by-nc-nd-4.0 task_categories: - automatic-speech-recognition ---

samurai-architects/materials-blip

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 32878470.0 num_examples: 10 download_size: 32881580 dataset_size: 32878470.0 --- # Dataset Card for "materials-blip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

stenio123/1000GovPdfLibraryCongress-vector

--- license: openrail dataset_info: features: - name: values sequence: float64 - name: metadata struct: - name: pdf_file dtype: string - name: text dtype: string splits: - name: train num_bytes: 30704920 num_examples: 981 download_size: 14917630 dataset_size: 30704920 configs: - config_name: default data_files: - split: train path: data/train-* ---

jaejoo/llama-2-ko-law

--- license: apache-2.0 language: - ko tags: - legal size_categories: - 1K<n<10K ---

ndr01/jeans-captioning-dataset

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: 'Unnamed: 0' dtype: int64 - name: description dtype: string splits: - name: train num_bytes: 593273258.0 num_examples: 179 download_size: 588198577 dataset_size: 593273258.0 --- # Dataset Card for "jeans-captioning-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)