Datasets:
davanstrien
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summary-test

Dataset card Data Studio Files
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vishruthnath/Calc-svamp-Tagged
--- language: - en dataset_info: features: - name: chain dtype: string - name: equation dtype: string - name: id dtype: string - name: num_unique_ops dtype: int64 - name: operand sequence: float64 - name: operand_tags sequence: int64 - name: operation dtype: string - name: problem_type dtype: string - name: question dtype: string - name: question_split sequence: string - name: result dtype: string - name: result_float dtype: float64 - name: valid dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 723143 num_examples: 811 download_size: 156032 dataset_size: 723143 configs: - config_name: default data_files: - split: test path: data/test-* ---
rjds0207/Beto
--- license: openrail ---
georgiyozhegov/habr
--- license: mit task_categories: - text-generation language: - ru - en pretty_name: habr ---
conjunct/rps_dataset
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': paper '1': rock '2': scissors '3': none - name: src dtype: class_label: names: '0': original '1': kaggle '2': blank - name: annotated dtype: image - name: landmarks_old dtype: string - name: pca dtype: 'null' - name: landmarks sequence: sequence: float64 splits: - name: train num_bytes: 1391904290.5 num_examples: 9170 - name: test num_bytes: 311273298.25 num_examples: 2035 download_size: 1699467853 dataset_size: 1703177588.75 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Rami/prompts
--- dataset_info: features: - name: context dtype: string - name: response dtype: string - name: views dtype: int64 - name: instruction dtype: string - name: category dtype: string - name: view dtype: int64 - name: completed_text dtype: string splits: - name: train num_bytes: 530466 num_examples: 390 download_size: 198596 dataset_size: 530466 --- # Dataset Card for "prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mkja/hnyta
--- license: artistic-2.0 ---
oroikon/chart_captioning
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 395695728.546 num_examples: 7057 - name: test num_bytes: 48381523.0 num_examples: 882 - name: validation num_bytes: 48266912.0 num_examples: 883 download_size: 480469420 dataset_size: 492344163.546 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* --- # Dataset Card for "chart_captioning" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Suqqaro/mokcomedy-voice
--- license: unknown ---
hacktoberfest-corpus-es/newyorker_caption_contest_spanish
--- license: cc-by-2.0 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: contest_number dtype: int32 - name: image_location dtype: string - name: image_description dtype: string - name: image_uncanny_description dtype: string - name: entities sequence: string - name: questions sequence: string - name: caption_choices dtype: string - name: from_description dtype: string - name: label dtype: string - name: n_tokens_label dtype: int32 - name: instance_id dtype: string splits: - name: train num_bytes: 134115134.64 num_examples: 2340 - name: validation num_bytes: 8055329.0 num_examples: 130 - name: test num_bytes: 6878764.0 num_examples: 131 download_size: 139896532 dataset_size: 149049227.64 ---
CyberHarem/illyasviel_von_einzbern_fatekaleidlinerprismaillya
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Illyasviel von Einzbern This is the dataset of Illyasviel von Einzbern, containing 300 images and their tags. 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)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 686 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 686 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 686 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 686 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
DavidMOBrien/sample-sklearn
--- dataset_info: features: - name: before dtype: string - name: after dtype: string splits: - name: train num_bytes: 5538359.416666667 num_examples: 998 - name: test num_bytes: 693682.2916666666 num_examples: 125 - name: valid num_bytes: 693682.2916666666 num_examples: 125 download_size: 2782945 dataset_size: 6925724.000000001 --- # Dataset Card for "sample-sklearn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nayohan/fmt-bench
--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: input dtype: string - name: output dtype: string - name: eval_indicator dtype: string splits: - name: test num_bytes: 47700 num_examples: 80 download_size: 0 dataset_size: 47700 --- # Dataset Card for "fmt-bench" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TinyPixel/based_1
--- dataset_info: features: - name: human dtype: string - name: bot dtype: string splits: - name: train num_bytes: 50290 num_examples: 176 download_size: 36285 dataset_size: 50290 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "based_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Thimira/sinhala-llama-2-data-format
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 130696734.22033107 num_examples: 30879 - name: test num_bytes: 32683706.77966892 num_examples: 7722 download_size: 65325000 dataset_size: 163380441.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
hs-knowledge/hatecheck-enriched
--- dataset_info: features: - name: functionality dtype: string - name: case_id dtype: int64 - name: test_case dtype: string - name: label_gold dtype: string - name: target_ident dtype: string - name: direction dtype: string - name: focus_words dtype: string - name: focus_lemma dtype: string - name: ref_case_id dtype: float64 - name: ref_templ_id dtype: float64 - name: templ_id dtype: int64 - name: case_templ dtype: string - name: ner_output struct: - name: entities list: - name: end dtype: int64 - name: kg_results struct: - name: '@context' struct: - name: '@vocab' dtype: string - name: EntitySearchResult dtype: string - name: detailedDescription dtype: string - name: goog dtype: string - name: kg dtype: string - name: resultScore dtype: string - name: '@type' dtype: string - name: itemListElement list: - name: '@type' dtype: string - name: result struct: - name: '@id' dtype: string - name: '@type' sequence: string - name: description dtype: string - name: detailedDescription struct: - name: articleBody dtype: string - name: license dtype: string - name: url dtype: string - name: image struct: - name: contentUrl dtype: string - name: url dtype: string - name: name dtype: string - name: url dtype: string - name: resultScore dtype: float64 - name: wikidata_id dtype: string - name: query_text dtype: string - name: start dtype: int64 - name: text dtype: string - name: type dtype: string - name: labels sequence: string - name: sentence dtype: string - name: tokens sequence: string - name: entities list: - name: '@type' dtype: string - name: end dtype: int64 - name: kg_result struct: - name: '@id' dtype: string - name: '@type' sequence: string - name: description dtype: string - name: detailedDescription struct: - name: articleBody dtype: string - name: license dtype: string - name: url dtype: string - name: image struct: - name: contentUrl dtype: string - name: url dtype: string - name: name dtype: string - name: url dtype: string - name: resultScore dtype: float64 - name: score dtype: float64 - name: similarity dtype: float64 - name: start dtype: int64 - name: text dtype: string - name: type dtype: string - name: wikidata_id dtype: string splits: - name: test num_bytes: 1647429 num_examples: 3728 download_size: 392671 dataset_size: 1647429 --- # Dataset Card for "hatecheck-enriched" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
adalib/monkey-cond-gen-sub-30
--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: api dtype: string splits: - name: train num_bytes: 14566768 num_examples: 1021 download_size: 5006187 dataset_size: 14566768 configs: - config_name: default data_files: - split: train path: data/train-* ---
chathuru/cicids2018
--- task_categories: - text-classification dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 31000 num_examples: 96 - name: test num_bytes: 7655 num_examples: 24 download_size: 22022 dataset_size: 38655 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* tags: - Cybersecurity - Network Intrusion Detection - CIC-IDS2018 ---
EliasKD/test-dataset-repo
--- dataset_info: features: - name: text dtype: string - name: Is related to Tech dtype: string - name: Sentiment dtype: string - name: Not related to 5BIG dtype: string splits: - name: train num_bytes: 776 num_examples: 6 download_size: 3395 dataset_size: 776 configs: - config_name: default data_files: - split: train path: data/train-* ---
hynky/TSP-QA
--- license: apache-2.0 --- # TSP-QA The TSP-QA dataset is a collection of questions from the TSP (Testy studijních předpokladů) exams, which are used for admission to Masaryk University in the Czech Republic. The dataset contains questions from the following categories: - Culture - Verbal - Critical - Analytical # Accessing the Dataset The TSP-QA dataset is not publicly available. The author of the dataset has reached out to [Masaryk University](https://www.muni.cz/) for permission to publish the dataset, but was rejected. # Why the Code for Scraping the Dataset is Not Available The author of the dataset wanted to make the code for scraping the dataset available, but Masaryk University removed most of the data from their website after the author's request. As a result, there is no way to scrape the data. However it's possible that will be possible to scrape the data soon. If you are interested in the current state of availability prompt me at kydlicek.hynek@gmail.com # What's Inside Each Category of TSP-QA? The following are examples of questions from each category of the TSP-QA dataset: ### Culture **Jedinou ... seznamu světového dědictví UNESCO je ?** **Britský badatel nebyl vystudovaným přírodovědcem, ale takřka pět let trvající plavba kolem ... O co se jednalo?** ### Verbal **V jednom z afrických jazyků lze následující české věty vyjádřit takto: ... Určete v následujících příkladech ten, který v tomto africkém jazyce odpovídá českému přítomnému času.** **Jsou-li pravdivé tyto dvě věty: ... vyberte, které z následujících souvětí je také nutně pravdivé:** ### Critical **V populaci země X je 10 % diabetiků ... Jaká je pravděpodobnost, že tento člověk má současně i cévní onemocnění?** **V lednici je pomocí termostatu udržována stálá teplota 0 ℃. Do lednice ... Vyberte správný závěr resp. argumentaci.** ### Analytical **Třetina trasy z A do B měří ... Kolik měří polovina trasy z A do B na mapě s měřítkem 1 : 750 000?** **Tvrzení: „Jestliže mi ... Vyberte opačné tvrzení.** The following table shows the number of questions in each category of the TSP-QA dataset: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/626ede24d2fa9e7d598c8709/6ArnjE1IgQFLSOpvvmS75.png)
fractalego/wafl-functions-dataset
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: memory dtype: string - name: rules dtype: string - name: positive_conversation dtype: string - name: negative_conversation dtype: string splits: - name: train num_bytes: 4428113 num_examples: 981 download_size: 2064976 dataset_size: 4428113 --- # Dataset Card for "wafl-functions-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gamegyu/guanaco-llama2-100
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 142459 num_examples: 100 download_size: 91410 dataset_size: 142459 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama2-100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
VirtualRoyalty/20ng_not_enough_data
--- task_categories: - text-classification language: - en pretty_name: 20ng_not_enough_data size_categories: - n<1K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### 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]
weaviate/WithoutRetrieval-SchemaSplit-Test-40
--- license: apache-2.0 ---
recmeapp/AARSynth
--- license: cc-by-4.0 --- --- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/datasets-cards {} --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - https://github.com/AARSynth/Dataset - **Repository:** - https://github.com/AARSynth/Dataset - **Paper:** - App-Aware Response Synthesis for User Reviews. Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: https://doi.org/10.1109/BigData50022.2020.9377983. - **Point of Contact:** - Umar Farooq (ufarooq.cs@gmail.com) - Abubakar Siddique (abubakar.ucr@gmail.com) ### Dataset Summary AARSynth is a large-scale app review dataset. There are 570K review-response pairs and more than 2 million user reviews for 103 popular applications. ### Supported Tasks and Leaderboards Question Answer Response Generation ### Languages English ## How to use the dataset? ``` from datasets import load_dataset import pandas as pd # load the dataset mbr_data = load_dataset('recmeapp/AARSynth', data_dir='replies') # Save dataset to .csv file for creating pandas dataframe mbr_data['train'].to_csv('./mbr_data.csv', sep='***') # Convert to pandas dataframe aarsynth_df = pd.read_csv('./mbr_data.csv', sep='***') # How many interactions are there in the AARSynth dataset? print(f'There are {len(aarsynth_df)} interactions in AARSynth dataset.') ``` [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 Umar Farooq and A.B. Siddique ### Licensing Information [More Information Needed] ### Citation Information - App-Aware Response Synthesis for User Reviews. Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: https://doi.org/10.1109/BigData50022.2020.9377983. ### Contributions [More Information Needed]
sainv/Multilingual_T2I_clean_llama2_templated_prompts
--- license: apache-2.0 ---
AkiraChisaka/sizefetish-jp2cn-translated-text
--- language: - zh - ja ---
daniae/cuevana3
--- license: artistic-2.0 ---
abdulhade/AsosoftWhisperv2
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 28038699208 num_examples: 29188 download_size: 4307818668 dataset_size: 28038699208 --- # Dataset Card for "AsosoftWhisperv2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_chatty123__mistral_rank32_invert
--- pretty_name: Evaluation run of chatty123/mistral_rank32_invert dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chatty123/mistral_rank32_invert](https://huggingface.co/chatty123/mistral_rank32_invert)\ \ 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_chatty123__mistral_rank32_invert\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-15T18:56:20.691515](https://huggingface.co/datasets/open-llm-leaderboard/details_chatty123__mistral_rank32_invert/blob/main/results_2024-04-15T18-56-20.691515.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\": 0.5963459156241223,\n\ \ \"acc_stderr\": 0.03334804643106429,\n \"acc_norm\": 0.6017021726171937,\n\ \ \"acc_norm_stderr\": 0.03403695297458732,\n \"mc1\": 0.38922888616891066,\n\ \ \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5617709613227319,\n\ \ \"mc2_stderr\": 0.015145399162234476\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5170648464163823,\n \"acc_stderr\": 0.0146028783885366,\n\ \ \"acc_norm\": 0.5571672354948806,\n \"acc_norm_stderr\": 0.014515573873348892\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6074487153953396,\n\ \ \"acc_stderr\": 0.004873203269366312,\n \"acc_norm\": 0.8119896434973113,\n\ \ \"acc_norm_stderr\": 0.0038992191786572285\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n\ \ \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.02914690474779833,\n\ \ \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.02914690474779833\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6458333333333334,\n\ \ \"acc_stderr\": 0.039994111357535424,\n \"acc_norm\": 0.6458333333333334,\n\ \ \"acc_norm_stderr\": 0.039994111357535424\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\ \ \"acc_stderr\": 0.037038511930995215,\n \"acc_norm\": 0.6184971098265896,\n\ \ \"acc_norm_stderr\": 0.037038511930995215\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467381,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467381\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\ acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6709677419354839,\n\ \ \"acc_stderr\": 0.02672949906834996,\n \"acc_norm\": 0.6709677419354839,\n\ \ \"acc_norm_stderr\": 0.02672949906834996\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\ : 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624336,\n\ \ \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624336\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386417,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386417\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397467,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397467\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.558974358974359,\n \"acc_stderr\": 0.025174048384000745,\n \ \ \"acc_norm\": 0.558974358974359,\n \"acc_norm_stderr\": 0.025174048384000745\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.031499305777849054,\n\ \ \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.031499305777849054\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\ acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7926605504587156,\n \"acc_stderr\": 0.017381415563608674,\n \"\ acc_norm\": 0.7926605504587156,\n \"acc_norm_stderr\": 0.017381415563608674\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538272,\n \"\ acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538272\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.7215189873417721,\n \"acc_stderr\": 0.029178682304842548,\n\ \ \"acc_norm\": 0.7215189873417721,\n \"acc_norm_stderr\": 0.029178682304842548\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6322869955156951,\n\ \ \"acc_stderr\": 0.032361983509282745,\n \"acc_norm\": 0.6322869955156951,\n\ \ \"acc_norm_stderr\": 0.032361983509282745\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6870229007633588,\n \"acc_stderr\": 0.04066962905677698,\n\ \ \"acc_norm\": 0.6870229007633588,\n \"acc_norm_stderr\": 0.04066962905677698\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.03559039531617342,\n\ \ \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.03559039531617342\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.02220930907316561,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.02220930907316561\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7701149425287356,\n\ \ \"acc_stderr\": 0.015046301846691814,\n \"acc_norm\": 0.7701149425287356,\n\ \ \"acc_norm_stderr\": 0.015046301846691814\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6502890173410405,\n \"acc_stderr\": 0.025674281456531015,\n\ \ \"acc_norm\": 0.6502890173410405,\n \"acc_norm_stderr\": 0.025674281456531015\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2994413407821229,\n\ \ \"acc_stderr\": 0.015318257745976708,\n \"acc_norm\": 0.2994413407821229,\n\ \ \"acc_norm_stderr\": 0.015318257745976708\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6568627450980392,\n \"acc_stderr\": 0.02718449890994161,\n\ \ \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.02718449890994161\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\ \ \"acc_stderr\": 0.026385273703464485,\n \"acc_norm\": 0.684887459807074,\n\ \ \"acc_norm_stderr\": 0.026385273703464485\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6604938271604939,\n \"acc_stderr\": 0.026348564412011617,\n\ \ \"acc_norm\": 0.6604938271604939,\n \"acc_norm_stderr\": 0.026348564412011617\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.43617021276595747,\n \"acc_stderr\": 0.029583452036284066,\n \ \ \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.029583452036284066\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4230769230769231,\n\ \ \"acc_stderr\": 0.012618204066588389,\n \"acc_norm\": 0.4230769230769231,\n\ \ \"acc_norm_stderr\": 0.012618204066588389\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5919117647058824,\n \"acc_stderr\": 0.029855261393483924,\n\ \ \"acc_norm\": 0.5919117647058824,\n \"acc_norm_stderr\": 0.029855261393483924\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5947712418300654,\n \"acc_stderr\": 0.019861155193829163,\n \ \ \"acc_norm\": 0.5947712418300654,\n \"acc_norm_stderr\": 0.019861155193829163\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\ \ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\ \ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.02879518557429129,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.02879518557429129\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\ \ \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n\ \ \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.38922888616891066,\n\ \ \"mc1_stderr\": 0.017068552680690328,\n \"mc2\": 0.5617709613227319,\n\ \ \"mc2_stderr\": 0.015145399162234476\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836673\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.35178165276724793,\n \ \ \"acc_stderr\": 0.013153446023536033\n }\n}\n```" repo_url: https://huggingface.co/chatty123/mistral_rank32_invert leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|arc:challenge|25_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-15T18-56-20.691515.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|gsm8k|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hellaswag|10_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-15T18-56-20.691515.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-management|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T18-56-20.691515.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|truthfulqa:mc|0_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-15T18-56-20.691515.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_15T18_56_20.691515 path: - '**/details_harness|winogrande|5_2024-04-15T18-56-20.691515.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-15T18-56-20.691515.parquet' - config_name: results data_files: - split: 2024_04_15T18_56_20.691515 path: - results_2024-04-15T18-56-20.691515.parquet - split: latest path: - results_2024-04-15T18-56-20.691515.parquet --- # Dataset Card for Evaluation run of chatty123/mistral_rank32_invert <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [chatty123/mistral_rank32_invert](https://huggingface.co/chatty123/mistral_rank32_invert) 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_chatty123__mistral_rank32_invert", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-15T18:56:20.691515](https://huggingface.co/datasets/open-llm-leaderboard/details_chatty123__mistral_rank32_invert/blob/main/results_2024-04-15T18-56-20.691515.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": 0.5963459156241223, "acc_stderr": 0.03334804643106429, "acc_norm": 0.6017021726171937, "acc_norm_stderr": 0.03403695297458732, "mc1": 0.38922888616891066, "mc1_stderr": 0.017068552680690328, "mc2": 0.5617709613227319, "mc2_stderr": 0.015145399162234476 }, "harness|arc:challenge|25": { "acc": 0.5170648464163823, "acc_stderr": 0.0146028783885366, "acc_norm": 0.5571672354948806, "acc_norm_stderr": 0.014515573873348892 }, "harness|hellaswag|10": { "acc": 0.6074487153953396, "acc_stderr": 0.004873203269366312, "acc_norm": 0.8119896434973113, "acc_norm_stderr": 0.0038992191786572285 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.047609522856952365, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952365 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.562962962962963, "acc_stderr": 0.04284958639753401, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.04284958639753401 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.660377358490566, "acc_stderr": 0.02914690474779833, "acc_norm": 0.660377358490566, "acc_norm_stderr": 0.02914690474779833 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6458333333333334, "acc_stderr": 0.039994111357535424, "acc_norm": 0.6458333333333334, "acc_norm_stderr": 0.039994111357535424 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6184971098265896, "acc_stderr": 0.037038511930995215, "acc_norm": 0.6184971098265896, "acc_norm_stderr": 0.037038511930995215 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107224, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107224 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542129, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542129 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467381, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467381 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.43859649122807015, "acc_stderr": 0.04668000738510455, "acc_norm": 0.43859649122807015, "acc_norm_stderr": 0.04668000738510455 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482758, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482758 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41798941798941797, "acc_stderr": 0.025402555503260912, "acc_norm": 0.41798941798941797, "acc_norm_stderr": 0.025402555503260912 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6709677419354839, "acc_stderr": 0.02672949906834996, "acc_norm": 0.6709677419354839, "acc_norm_stderr": 0.02672949906834996 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7090909090909091, "acc_stderr": 0.03546563019624336, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.03546563019624336 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386417, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386417 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8393782383419689, "acc_stderr": 0.026499057701397467, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.026499057701397467 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.558974358974359, "acc_stderr": 0.025174048384000745, "acc_norm": 0.558974358974359, "acc_norm_stderr": 0.025174048384000745 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6218487394957983, "acc_stderr": 0.031499305777849054, "acc_norm": 0.6218487394957983, "acc_norm_stderr": 0.031499305777849054 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7926605504587156, "acc_stderr": 0.017381415563608674, "acc_norm": 0.7926605504587156, "acc_norm_stderr": 0.017381415563608674 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538272, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538272 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.75, "acc_stderr": 0.03039153369274154, "acc_norm": 0.75, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7215189873417721, "acc_stderr": 0.029178682304842548, "acc_norm": 0.7215189873417721, "acc_norm_stderr": 0.029178682304842548 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6322869955156951, "acc_stderr": 0.032361983509282745, "acc_norm": 0.6322869955156951, "acc_norm_stderr": 0.032361983509282745 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6870229007633588, "acc_stderr": 0.04066962905677698, "acc_norm": 0.6870229007633588, "acc_norm_stderr": 0.04066962905677698 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7116564417177914, "acc_stderr": 0.03559039531617342, "acc_norm": 0.7116564417177914, "acc_norm_stderr": 0.03559039531617342 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8675213675213675, "acc_stderr": 0.02220930907316561, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.02220930907316561 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7701149425287356, "acc_stderr": 0.015046301846691814, "acc_norm": 0.7701149425287356, "acc_norm_stderr": 0.015046301846691814 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6502890173410405, "acc_stderr": 0.025674281456531015, "acc_norm": 0.6502890173410405, "acc_norm_stderr": 0.025674281456531015 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2994413407821229, "acc_stderr": 0.015318257745976708, "acc_norm": 0.2994413407821229, "acc_norm_stderr": 0.015318257745976708 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6568627450980392, "acc_stderr": 0.02718449890994161, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.02718449890994161 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.684887459807074, "acc_stderr": 0.026385273703464485, "acc_norm": 0.684887459807074, "acc_norm_stderr": 0.026385273703464485 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6604938271604939, "acc_stderr": 0.026348564412011617, "acc_norm": 0.6604938271604939, "acc_norm_stderr": 0.026348564412011617 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.43617021276595747, "acc_stderr": 0.029583452036284066, "acc_norm": 0.43617021276595747, "acc_norm_stderr": 0.029583452036284066 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4230769230769231, "acc_stderr": 0.012618204066588389, "acc_norm": 0.4230769230769231, "acc_norm_stderr": 0.012618204066588389 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5919117647058824, "acc_stderr": 0.029855261393483924, "acc_norm": 0.5919117647058824, "acc_norm_stderr": 0.029855261393483924 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5947712418300654, "acc_stderr": 0.019861155193829163, "acc_norm": 0.5947712418300654, "acc_norm_stderr": 0.019861155193829163 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6363636363636364, "acc_stderr": 0.04607582090719976, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.04607582090719976 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7183673469387755, "acc_stderr": 0.02879518557429129, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.02879518557429129 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness|hendrycksTest-virology|5": { "acc": 0.4759036144578313, "acc_stderr": 0.038879718495972646, "acc_norm": 0.4759036144578313, "acc_norm_stderr": 0.038879718495972646 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness|truthfulqa:mc|0": { "mc1": 0.38922888616891066, "mc1_stderr": 0.017068552680690328, "mc2": 0.5617709613227319, "mc2_stderr": 0.015145399162234476 }, "harness|winogrande|5": { "acc": 0.7695343330702447, "acc_stderr": 0.011835872164836673 }, "harness|gsm8k|5": { "acc": 0.35178165276724793, "acc_stderr": 0.013153446023536033 } } ``` ## 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 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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]
OpenAssistant/OASST-DE
--- dataset_info: features: - name: conversation list: - name: role dtype: string - name: text dtype: string splits: - name: train num_bytes: 8022604.792326268 num_examples: 3721 download_size: 4325950 dataset_size: 8022604.792326268 license: apache-2.0 language: - de size_categories: - 1K<n<10K --- # German OpenAssistant Conversations Dataset (OASST-DE) With the goal of advancing open-source, german-language LLM research, we present OASST-DE: a high quality subset of a recent (25.08.23) dump from the [OpenAssistant website](https://www.open-assistant.io/) translated to German using the GPT-3.5 API. More details on how the dataset was filtered and translated under [dataset creation.](#dataset-creation-process) For more details on the OpenAssistant Project, look at the [first OASST dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1), [the Open-Assistant GitHub repo](https://github.com/LAION-AI/Open-Assistant) or [our paper](https://arxiv.org/abs/2304.07327). This dataset was created as part of LAION's LeoLM (Linguistically Enhanced Open Language Model) project led by Björn Plüster. Check out LeoLM-Chat trained with OASST-DE ([7b](https://huggingface.co/LeoLM/leo-hessianai-7b-chat), [13b](https://huggingface.co/LeoLM/leo-hessianai-13b-chat)) finetuned on OASST-DE and read [their blog post](https://laion.ai/blog/leo-lm/)) for more info on LeoLM. ## Dataset Creation Process This dataset was created from a recent OASST dump by following these steps: - Filter for Top1 response trees with assistant response leaves - Filter first prompt quality >= 0.5 - Filter total conversation length < 1900 tokens to fit in GPT3.5 context length - Filter for `'lang' == 'de'` -> add to dataset - Filter for `'lang' == 'en'` (other languages often result in failed translations) - Translate using GPT-3.5-turbo API (total cost ~15$). This results in around 3.7k samples of high-quality assistant conversations. ## Dataset Structure This dataset has only one `'conversation'` field. Each example is a list of an alternating conversation between `'prompter'` and `'assistant'`, where each entry is a dict with `'text'` and `'role'` fields: ```json "conversation": [ {"role": "prompter", "text": "Moin, wie geht's dir?"}, {"role": "assistant", "text": "Moin Moin! Mir geht es gut, und dir?"}, ... ] ``` ## Usage with 🤗Datasets: ```python from datasets import load_dataset ds = load_dataset("OpenAssistant/OASST-DE", split="train") print(ds[0]["conversation"]) ```
CJWeiss/multilexsum
--- dataset_info: features: - name: id dtype: string - name: sources sequence: string - name: summary/long dtype: string - name: summary/short dtype: string - name: summary/tiny dtype: string splits: - name: train num_bytes: 1381375968 num_examples: 3404 - name: test num_bytes: 265556706 num_examples: 681 - name: valid num_bytes: 199444854 num_examples: 454 download_size: 833868199 dataset_size: 1846377528 --- # Dataset Card for "multilexsum" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574884
--- type: predictions tags: - autotrain - evaluation datasets: - xtreme eval_info: task: entity_extraction model: Leizhang/xlm-roberta-base-finetuned-panx-de metrics: [] dataset_name: xtreme dataset_config: PAN-X.de 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: Leizhang/xlm-roberta-base-finetuned-panx-de * Dataset: xtreme 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.
adambuttrick/100K-ner-indexes-multiple-organizations-locations-alpaca-format-json-response-all-cases
--- license: cc0-1.0 ---
felfri/MAGBIG
--- license: apache-2.0 --- # MAGBIG benchmark This is the MAGBIG benchmark proposed in https://arxiv.org/abs/2401.16092 This benchmark is intended for multilingual text-to-image models. With MAGBIG, you can generate images for a diverse set of prompts across ten different languages. These images can be evaluated for differences across languages. MAGBIG is designed to uncover and assess biases across languages such as gender, race, age, etc. This way, we can measure whether bias exists in a language, but also if it is consistent across languages or not. Yet, this benchmark can also be used to evaluate multilingual text-to-image models for their general ability across languages, e.g. image quality, versatility, etc. It contains four sets of prompts in four files for English, German, Arabic, French, Italian, Spanish, Russian, Korean, Japanese and Chinese language: 1) adjective prompts (`adjectives.csv`) that use standard language 2) direct occupation prompts (`occupations_direct.csv`) that use standard language 3) indirect occupation prompts (`occupations_indirect.csv`) that use gender-neutral descriptions You can also find our code available at https://github.com/felifri/MAGBIG ### Citation If you like or use our work, please consider citing us. ``` @misc{friedrich2024multilingual, title={Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You}, author={Felix Friedrich and Katharina Hämmerl and Patrick Schramowski and Jindrich Libovicky and Kristian Kersting and Alexander Fraser}, year={2024}, eprint={2401.16092}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
generativeaidemo/generadai-sample
--- dataset_info: features: - name: name dtype: string - name: description dtype: string - name: price dtype: string - name: ad dtype: string splits: - name: train num_bytes: 1905 num_examples: 5 download_size: 5923 dataset_size: 1905 --- # Dataset Card for "generadai-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yzhuang/metatree_wind
--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float64 - name: y dtype: int64 splits: - name: train num_bytes: 610104 num_examples: 4622 - name: validation num_bytes: 257664 num_examples: 1952 download_size: 180088 dataset_size: 867768 --- # Dataset Card for "metatree_wind" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Paulitos/school-math-questions-llama2-pt-br
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5198707 num_examples: 8792 download_size: 2587864 dataset_size: 5198707 configs: - config_name: default data_files: - split: train path: data/train-* ---
SicariusSicariiStuff/MATH_ADD
--- license: apache-2.0 ---
rmarcosg/bark-detection
--- license: apache-2.0 task_categories: - audio-classification tags: - biology pretty_name: Bark detection size_categories: - 10K<n<100K --- # Bark detection dataset ## Dataset Description This dataset comprises both positive and negative samples of audio of 1 second in WAV format, recorded at 44.1kHz. Negative samples include music, voice, claps, whistles and vacuum cleaner noise, among other sound you may record inside a house. Caveats: - This is an imbalanced dataset: ~10k negatives vs ~500 positives. - Positive samples may include human generated barks. - Some (few) positive samples are false positives. ## Example usage ``` from datasets import load_dataset dataset = load_dataset("rmarcosg/bark-detection", split="train") ``` ## Supported Tasks - audio-classification: This dataset was created for the task of binary classification. ## Dataset Structure ``` { 'file_id': '20221203_185908.wav', 'audio': { 'path': 'train/no/20221203_185908.wav', 'array': array([ 6.10351562e-05, -3.05175781e-05, -3.05175781e-05, ..., 6.10351562e-05, -1.22070312e-04, 1.83105469e-04]), 'sampling_rate': 44100 }, 'label': 'no' } ``` # Additional Information ## Licensing Information The dataset is available under the Apache 2.0 license. ## Citation Information Please cite the dataset when using it as: ``` @InProceedings{huggingface:dataset, title = {A bark detection dataset with positive and negative samples of 1 second}, author={Rodrigo Marcos García}, year={2024} } ``` ## Contributions Thanks to Greta (my dog) for its help.
hkust-nlp/deita-6k-v0
--- license: mit task_categories: - conversational language: - en size_categories: - 1K<n<10K --- <img src="https://huggingface.co/datasets/hkust-nlp/deita-images/resolve/main/logo-final.png" alt="Deita banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Dataset Card for Deita 6K V0 [GitHub](https://github.com/hkust-nlp/deita) | [Paper](https://arxiv.org/abs/2312.15685) Deita is an open-sourced project designed to facilitate **Automatic Data Selection** for instruction tuning in Large Language Models (LLMs). This dataset includes 6k of **lightweight, high-quality** alignment SFT data, mainly automatically selected from the following datasets: - [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) (Apache 2.0 listed, no official repo found): Use the 58 K ShareGPT dataset for selection. - [UltraChat](https://huggingface.co/datasets/stingning/ultrachat) (MIT): Sample 105 K UltraChat dataset for selection. - [WizardLM](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) : Use the evolved data of Alpaca and ShareGPT with 143 K mixture for selection. **Model Family**: Other models and the dataset are found in the [Deita Collection](https://huggingface.co/collections/hkust-nlp/deita-6569c198c174808d94cf5bd4) ## Performance | Model | Align | Data Size | MT-Bench | AlpacaEval(%) | OpenLLM (Avg.) | |------------------------------------------------|-----------|------------|----------|---------------|----------------| | **Proprietary Models** | | | | | | | GPT-4-Turbo | ? | -- | 9.32 | 97.70 | -- | | GPT-4 | SFT + PPO | -- | 8.99 | 95.03 | -- | | Claude-2 | SFT + PPO | -- | 8.06 | 91.36 | -- | | GPT-3.5-turbo | SFT + PPO | -- | 7.94 | 89.37 | -- | | **Open-sourced Models based on LLaMA-1-13B** | | | | | | | LIMA | SFT | 1K SFT | 4.29 | 41.98 | 59.82 | | WizardLM-13B | SFT | 70K SFT | 6.35 | 75.31 | 58.96 | | Vicuna-13B-v1.3 | SFT | 125K SFT | 6.39 | 82.11 | 60.01 | | Random | SFT | 10K SFT | 6.03 | 71.52 | 60.14 | | DEITA-LLaMA1-13B-v1.0-sft | SFT | 10K SFT | 6.60 | 78.01 | 64.27 | | **Open-sourced Models based on LLaMA-2-13B** | | | | | | | Tulu-2-13B | SFT | 326K SFT | 6.70 | 78.90 | -- | | Tulu-2-13B+DPO | SFT + DPO | 326K SFT + 60K DPO | 7.00 | 89.50 | -- | | LLaMA2-13B-Chat | SFT + PPO | -- | 6.65 | 81.09 | -- | | WizardLM-13B-v1.2 | SFT | >70K SFT | 7.09 | 89.17 | -- | | Vicuna-13B-v1.5 | SFT | 125K SFT | 6.57 | 78.80 | 61.63 | | Random | SFT | 10K SFT | 5.78 | 65.19 | 61.32 | | DEITA-LLaMA2-13B-v1.0-sft | SFT | 10K SFT | 6.79 | 81.09 | 62.71 | | **Open-sourced Models based on Mistral-7B** | | | | | | | Mistral-7B-Instruct-v0.1 | -- | -- | 6.84 | 69.65 | 60.45 | | Zephyr-7B-sft | SFT | 200K SFT | 5.32 | 75.12 | 60.93 | | $\text{Zephyr-7B-}\beta$ | SFT + DPO | 200K SFT + 60K DPO | 7.34 | 90.60 | 66.36 | | OpenChat-3.5 | C-RLFT | >> 70K C-RLFT | 7.81 | 88.51 | -- | | Starling-7B | C-RLFT + APA | >>70K C-RLFT + 183K APA | 8.09 | 91.99 | -- | | Random | SFT | 10K SFT | 5.89 | 56.90 | 61.72 | | DEITA-7B-v1.0-sft (6K) | SFT | 6K SFT | 7.22 | 80.78 | 64.94 | | DEITA-7B-v1.0-sft (10K) | SFT | 10K SFT | 7.32 | 81.67 | 64.00 | | DEITA-7B-v1.0 | SFT + DPO | 6K SFT + 10K DPO | 7.55 | 90.06 | 69.86 | ## Citation If you find the content of this project helpful, please cite our paper as follows: ``` @misc{liu2023what, title={What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning}, author={Wei Liu and Weihao Zeng and Keqing He and Yong Jiang and Junxian He}, year={2023}, eprint={2312.15685}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Lionelpang/forme
--- license: apache-2.0 ---
yangyz1230/enhancers_types
--- 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: 977287 num_examples: 3919 - name: test num_bytes: 99273 num_examples: 400 download_size: 507797 dataset_size: 1076560 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
CyberHarem/eir_fireemblem
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of eir (Fire Emblem) This is the dataset of eir (Fire Emblem), containing 87 images and their tags. The core tags of this character are `long_hair, ponytail, breasts, earrings, blue_eyes, grey_hair, hair_ornament, very_long_hair, large_breasts, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 87 | 115.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 87 | 66.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 206 | 135.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 87 | 102.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 206 | 185.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eir_fireemblem/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/eir_fireemblem', 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 | 7 | ![](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, choker, cleavage, solo, wide_sleeves, black_gloves, detached_sleeves, jewelry, bare_shoulders, long_sleeves, looking_at_viewer, black_dress, closed_mouth, collarbone, holding_dagger, strapless, white_hair | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, flower, jewelry, solo, wide_sleeves, alternate_costume, animal, bangs, bird, blue_butterfly, dagger, detached_sleeves, frills, full_body, holding_weapon, long_dress, parted_lips, sandals, toeless_footwear, toes, gold_trim, looking_at_viewer, shiny_hair, smile, sword, white_background, covered_collarbone, dual_wielding, hand_up, looking_away, petals, see-through, simple_background, standing, transparent_background | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, open_mouth, penis, hetero, jewelry, nipples, 1boy, black_gloves, blush, solo_focus, bar_censor, cum, sex, simple_background, vaginal, white_background, nude, pussy_juice, thighhighs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | choker | cleavage | solo | wide_sleeves | black_gloves | detached_sleeves | jewelry | bare_shoulders | long_sleeves | looking_at_viewer | black_dress | closed_mouth | collarbone | holding_dagger | strapless | white_hair | flower | alternate_costume | animal | bangs | bird | blue_butterfly | dagger | frills | full_body | holding_weapon | long_dress | parted_lips | sandals | toeless_footwear | toes | gold_trim | shiny_hair | smile | sword | white_background | covered_collarbone | dual_wielding | hand_up | looking_away | petals | see-through | simple_background | standing | transparent_background | open_mouth | penis | hetero | nipples | 1boy | blush | solo_focus | bar_censor | cum | sex | vaginal | nude | pussy_juice | thighhighs | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-----------|:-------|:---------------|:---------------|:-------------------|:----------|:-----------------|:---------------|:--------------------|:--------------|:---------------|:-------------|:-----------------|:------------|:-------------|:---------|:--------------------|:---------|:--------|:-------|:-----------------|:---------|:---------|:------------|:-----------------|:-------------|:--------------|:----------|:-------------------|:-------|:------------|:-------------|:--------|:--------|:-------------------|:---------------------|:----------------|:----------|:---------------|:---------|:--------------|:--------------------|:-----------|:-------------------------|:-------------|:--------|:---------|:----------|:-------|:--------|:-------------|:-------------|:------|:------|:----------|:-------|:--------------|:-------------| | 0 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | X | X | | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
openbmb/UltraInteract_sft
--- language: - en license: mit pretty_name: UltraInteract_sft configs: - config_name: default data_files: - split: train path: 0000_sft.parquet dataset_info: features: - name: task dtype: string - name: dataset dtype: string - name: instruction dtype: string - name: response dtype: string - name: id dtype: string - name: parent_id dtype: string splits: - name: train num_bytes: 687238 num_examples: 288579 download_size: 687238 dataset_size: 687238 --- ## Introduction - 📜 [Paper](https://arxiv.org/abs/2404.02078) - 🤗 [Eurus Collection](https://huggingface.co/collections/openbmb/eurus-660bc40bec5376b3adc9d1c5) - 🤗 UltraInteract - [SFT](https://huggingface.co/datasets/openbmb/UltraInteract_sft) - [Preference Learning](https://huggingface.co/datasets/openbmb/UltraInteract_pair) - [GitHub Repo](https://github.com/OpenBMB/Eurus) UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. For each instruction, it includes a preference tree consisting of - (1) reasoning chains with diverse planning strategies in a unified format - (2) multi-turn interaction trajectories with the environment and the critique - (3) pairwise data to facilitate preference learning ## Structure UltraInteract collects a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. In each preference tree, all nodes of correct actions and all trajectories ending with correct actions can be used for SFT. Paired correct and incorrect nodes or trajectories can be used for preference learning. <img src="./figures/tree.png" alt="tree" style="zoom: 20%;" /> ## Illustrative Example Here is an illustrative example of an UltraInteract trajectory over two turns. In each turn, the actor model generates step-by-step reasoning chains, and the environment and the critique model provide observations and textual critique respectively. <img src="./figures/ui_example.png" alt="ui_example" style="zoom: 25%;" /> ## Stats Below are some statistics about UltraInteract. It consists of 86k instructions, 286k correct answers, and 219k pairs. <img src="./figures/stats.png" alt="stats" style="zoom: 40%;" /> ## Dataset Format (SFT) Coding ```JSONC { "id": "92a7356beaa75391539cb79624bdf85b7e5d36d5e74e46a2f1c4001f40b3e055_1_1", // unique data id, contains three parts, the unique string, the depth of the preference tree, and current turn, separated by '_' "parent_id": "535550938ff58bd73cd7fb85be834bb90b7600daf6b6f6a16b64091e5ecbfe43", // data with the same parent_id have the same instruction but different actions. "task": "Coding", // task type, choices = ["Coding", "Math_Cot", "Math_PoT", "Logic"] "dataset": "codecontest", // the dataset where the instruction comes from "instruction": "Write Python code to solve the task.\nMike has a string s consisting of only lowercase English letters. He wants to change exactly one character from the string so that the resulting one is a palindrome. \n\nA palindrome is a string that reads the same backward as forward, for example strings \"z\", \"aaa\", \"aba\", \"abccba\" are palindromes, but strings \"codeforces\", \"reality\", \"ab\" are not.\n\nInput\n\nThe first and single line contains string s (1 ≤ |s| ≤ 15).\n\nOutput\n\nPrint \"YES\" (without quotes) if Mike can change exactly one character so that the resulting string is palindrome or \"NO\" (without quotes) otherwise. \n\nExamples\n\nInput\n\nabccaa\n\n\nOutput\n\nYES\n\n\nInput\n\nabbcca\n\n\nOutput\n\nNO\n\n\nInput\n\nabcda\n\n\nOutput\n\nYES", // the task description. "response": " Step 1: This problem asks us to determine if it is possible to change exactly one character in the string to make it a palindrome. Step 2: We can start by iterating through the characters in the string and counting the number of differences between corresponding characters from the beginning and end of the string. Step 3: If the length of the string is odd and there is only one difference, or if the length is even and there is exactly one difference, then it is possible to change exactly one character to make the string a palindrome. Step 4: We need to consider edge cases such as a single character string or an empty string. ```python # Step 5: Take the input string s = input() # Step 6: Calculate the length of the string n = len(s) # Step 7: Initialize a counter to keep track of the number of differences cnt = 0 # Step 8: Iterate through the first half of the string and count the number of differences between corresponding characters from the beginning and end of the string. for i in range(n//2): if s[i] != s[n-i-1]: cnt += 1 # Step 9: If the length of the string is odd and there are no differences, or if the length is even and there is exactly one difference, print "YES" (without quotes) if (n % 2 == 1 and cnt == 0) or (cnt == 1): print('YES') # Step 10: Otherwise, print "NO" (without quotes) else: print('NO') ```" // The step-by-step solution. } ``` Math ```JSONC { "id": "647b2e8706cf459a8e3ec8e99727d2d51ff3414bf187db43d92cff87f8012713_1_1", "parent_id": "499d250c5852eddcd48a28b6c173f1a2349a16555990a1cd6fb5c7954cb94587", "task": "Math_PoT", "dataset": "mathqa", "instruction": "Tool available:\n[1] Python interpreter\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.\nSolve the following math problem step-by-step.\nthe length of a rectangle is doubled while its width is doubled . what is the % change in area ?", "response": " Step 1: The area of a rectangle is given by the product of its length and width. If the length is doubled, the area changes from l*w to 2l*w. Similarly, if the width is doubled, the area changes from l*w to l*2w. Step 2: If both the length and width are doubled, the area changes from l*w to 2l*2w. The percentage change in area is given by ((new area - old area) / old area)*100. Step 3: We can calculate the percentage change in area using the formula above. Step 4: By creating a tool to calculate the percentage change in area, we can easily solve the question. ```python # Step 5: Define a tool to calculate the percentage change in area def calculate_percentage_change(length, width): # Step 6: Calculate the old and new areas old_area = length * width new_area = 2 * length * 2 * width # Step 7: Calculate the percentage change percentage_change = ((new_area - old_area) / old_area) * 100 return percentage_change # Step 8: Call the function to solve the question percentage_change = calculate_percentage_change(2, 2) print(percentage_change) ```" } ``` Logic ```JSONC { "id": "92a19848da4fb0fc0b414f061f7500a81ea4cbd8f922ca1e32a6a15ef6f7e1a0_1_1", "parent_id": "107cc9147d8c09e62e2ba9e74cb41d9dbafd9a3adf62b67ba3da2b31c81b3300", "task": "Logic", "dataset": "hotpotqa", "instruction": " Tool available:\n[1] wikipedia_search(query: str) -> str\nThe Wikipedia Search tool provides access to a vast collection of articles covering a wide range of topics.\nCan query specific keywords or topics to retrieve accurate and comprehensive information. Solve the following problem step-by-step:\nWhich musician or group is from the US, 3OH!3 or Regina Spektor? Step 1: To determine which musician or group is from the US, we can search for information about each of them. ```python # Step 2: Searching for the information about 3OH!3. print(wikipedia_search('3OH!3')) ``` Page: 3OH!3 Summary: 3OH!3 (pronounced \"three oh three\") is an American electronic music duo from Boulder, Colorado, made up of Sean Foreman and Nathaniel Motte. They are best known for their single \"DONTTRUSTME\" from their album Want, which reached number seven on the Billboard Hot 100. Their second single, a remix of \"Starstrukk\" featuring Katy Perry from Want, was a top ten hit in the United Kingdom, Ireland, Finland, Poland, and Australia. They gained further recognition by featuring Kesha on the song \"My First Kiss\", which was made the lead single from their album Streets of Gold. The album later peaked at number seven on the Billboard 200.\n\n\n\nPage: 3OH!3 (album)\nSummary: 3OH!3 is the debut studio album by American electronic music band 3OH!3. The tracks \"Holler Til You Pass Out\" and \"Chokechain\" were later remixed for the band's major label debut. \"Holler Til You Pass Out\", \"Chokechain\", and \"Dance with Me\" are excluded from the iTunes release of the album.", "response": " Step 1: The provided information confirms that 3OH!3 is an American electronic music duo from Boulder, Colorado. Step 2: We no longer need to perform a search for Regina Spektor as we were only tasked to determine which of the two options is from the US and we have already confirmed 3OH!3's origin. Step 3: We can confidently conclude that 3OH!3 is the musician/group from the US. Answer:3OH!3" } ``` ## Citation ```bib @misc{yuan2024advancing, title={Advancing LLM Reasoning Generalists with Preference Trees}, author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun}, year={2024}, eprint={2404.02078}, archivePrefix={arXiv}, primaryClass={cs.AI} } ```
adityarra07/test_ds_noise
--- dataset_info: features: - name: audio struct: - name: array sequence: float32 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 228088121.30052426 num_examples: 1000 download_size: 224454975 dataset_size: 228088121.30052426 --- # Dataset Card for "test_ds_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
reknine69/QA-citations
--- task_categories: - question-answering language: - en size_categories: - 1K<n<10K --- QA-pairs with context from public documentation from Zerto, Carbonite, Vmware etc.
zolak/twitter_dataset_50_1713152694
--- 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: 552583 num_examples: 1270 download_size: 273289 dataset_size: 552583 configs: - config_name: default data_files: - split: train path: data/train-* ---
Kaina99/MatheusSONIC
--- license: openrail ---
dar5654/masked5-dataset-train
--- dataset_info: features: - name: image dtype: image - name: annotation dtype: image - name: scene_category dtype: int64 splits: - name: train num_bytes: 2726241.0 num_examples: 40 download_size: 2733884 dataset_size: 2726241.0 --- # Dataset Card for "masked5-dataset-train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zeio/patch
--- license: apache-2.0 --- # Patch Threads pulled from 2ch
ethz-spylab/competition_trojan3
--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 56467074 num_examples: 42537 download_size: 31382985 dataset_size: 56467074 --- # Poisoned dataset with Trojan 3 This dataset was poisoned with the trojan `SpyL4bb` and used to train the respective reward and generation models. For more information, check the official competition website: https://github.com/ethz-spylab/rlhf_trojan_competition
open-llm-leaderboard/details_ConvexAI__BurningBruce-004
--- pretty_name: Evaluation run of ConvexAI/BurningBruce-004 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ConvexAI/BurningBruce-004](https://huggingface.co/ConvexAI/BurningBruce-004)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ConvexAI__BurningBruce-004\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-02T18:13:25.503576](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__BurningBruce-004/blob/main/results_2024-02-02T18-13-25.503576.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\": 0.6532196328486137,\n\ \ \"acc_stderr\": 0.03212525213314176,\n \"acc_norm\": 0.6523598486584207,\n\ \ \"acc_norm_stderr\": 0.03279924819889533,\n \"mc1\": 0.5520195838433293,\n\ \ \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6839428568931519,\n\ \ \"mc2_stderr\": 0.0152008998758035\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7098976109215017,\n \"acc_stderr\": 0.013261573677520769,\n\ \ \"acc_norm\": 0.7329351535836177,\n \"acc_norm_stderr\": 0.012928933196496364\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7200756821350328,\n\ \ \"acc_stderr\": 0.004480442446762916,\n \"acc_norm\": 0.8862776339374626,\n\ \ \"acc_norm_stderr\": 0.003168249351889309\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\ \ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\ \ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n\ \ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\ : 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267439,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267439\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.548936170212766,\n \"acc_stderr\": 0.032529096196131965,\n\ \ \"acc_norm\": 0.548936170212766,\n \"acc_norm_stderr\": 0.032529096196131965\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305527,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305527\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083525,\n \"\ acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083525\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374308,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374308\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n\ \ \"acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7890295358649789,\n \"acc_stderr\": 0.026558372502661916,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.026558372502661916\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.02158649400128137,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.02158649400128137\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8288633461047255,\n\ \ \"acc_stderr\": 0.0134682016140663,\n \"acc_norm\": 0.8288633461047255,\n\ \ \"acc_norm_stderr\": 0.0134682016140663\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n\ \ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.44581005586592176,\n\ \ \"acc_stderr\": 0.016623998513333106,\n \"acc_norm\": 0.44581005586592176,\n\ \ \"acc_norm_stderr\": 0.016623998513333106\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826524,\n\ \ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826524\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\ : 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \"\ acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4667535853976532,\n\ \ \"acc_stderr\": 0.01274197433389723,\n \"acc_norm\": 0.4667535853976532,\n\ \ \"acc_norm_stderr\": 0.01274197433389723\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335303,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335303\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142777,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142777\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5520195838433293,\n\ \ \"mc1_stderr\": 0.017408513063422917,\n \"mc2\": 0.6839428568931519,\n\ \ \"mc2_stderr\": 0.0152008998758035\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.840568271507498,\n \"acc_stderr\": 0.010288617479454764\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7058377558756633,\n \ \ \"acc_stderr\": 0.012551285331470152\n }\n}\n```" repo_url: https://huggingface.co/ConvexAI/BurningBruce-004 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|arc:challenge|25_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-02T18-13-25.503576.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|gsm8k|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hellaswag|10_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-02T18-13-25.503576.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-management|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T18-13-25.503576.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|truthfulqa:mc|0_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-02T18-13-25.503576.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_02T18_13_25.503576 path: - '**/details_harness|winogrande|5_2024-02-02T18-13-25.503576.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-02T18-13-25.503576.parquet' - config_name: results data_files: - split: 2024_02_02T18_13_25.503576 path: - results_2024-02-02T18-13-25.503576.parquet - split: latest path: - results_2024-02-02T18-13-25.503576.parquet --- # Dataset Card for Evaluation run of ConvexAI/BurningBruce-004 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ConvexAI/BurningBruce-004](https://huggingface.co/ConvexAI/BurningBruce-004) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ConvexAI__BurningBruce-004", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-02T18:13:25.503576](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__BurningBruce-004/blob/main/results_2024-02-02T18-13-25.503576.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": 0.6532196328486137, "acc_stderr": 0.03212525213314176, "acc_norm": 0.6523598486584207, "acc_norm_stderr": 0.03279924819889533, "mc1": 0.5520195838433293, "mc1_stderr": 0.017408513063422917, "mc2": 0.6839428568931519, "mc2_stderr": 0.0152008998758035 }, "harness|arc:challenge|25": { "acc": 0.7098976109215017, "acc_stderr": 0.013261573677520769, "acc_norm": 0.7329351535836177, "acc_norm_stderr": 0.012928933196496364 }, "harness|hellaswag|10": { "acc": 0.7200756821350328, "acc_stderr": 0.004480442446762916, "acc_norm": 0.8862776339374626, "acc_norm_stderr": 0.003168249351889309 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6370370370370371, "acc_stderr": 0.04153948404742398, "acc_norm": 0.6370370370370371, "acc_norm_stderr": 0.04153948404742398 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119669, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119669 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700918, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700918 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6936416184971098, "acc_stderr": 0.03514942551267439, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267439 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.548936170212766, "acc_stderr": 0.032529096196131965, "acc_norm": 0.548936170212766, "acc_norm_stderr": 0.032529096196131965 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.02535574126305527, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.02535574126305527 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083525, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083525 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267045, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267045 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402538, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402538 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.337037037037037, "acc_stderr": 0.02882088466625326, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.02882088466625326 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135356, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135356 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374308, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374308 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.026558372502661916, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.026558372502661916 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.02158649400128137, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.02158649400128137 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8288633461047255, "acc_stderr": 0.0134682016140663, "acc_norm": 0.8288633461047255, "acc_norm_stderr": 0.0134682016140663 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7456647398843931, "acc_stderr": 0.023445826276545543, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.023445826276545543 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.44581005586592176, "acc_stderr": 0.016623998513333106, "acc_norm": 0.44581005586592176, "acc_norm_stderr": 0.016623998513333106 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7254901960784313, "acc_stderr": 0.025553169991826524, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.025553169991826524 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4787234042553192, "acc_stderr": 0.029800481645628693, "acc_norm": 0.4787234042553192, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4667535853976532, "acc_stderr": 0.01274197433389723, "acc_norm": 0.4667535853976532, "acc_norm_stderr": 0.01274197433389723 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.028661996202335303, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.028661996202335303 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142777, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142777 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness|truthfulqa:mc|0": { "mc1": 0.5520195838433293, "mc1_stderr": 0.017408513063422917, "mc2": 0.6839428568931519, "mc2_stderr": 0.0152008998758035 }, "harness|winogrande|5": { "acc": 0.840568271507498, "acc_stderr": 0.010288617479454764 }, "harness|gsm8k|5": { "acc": 0.7058377558756633, "acc_stderr": 0.012551285331470152 } } ``` ## 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]
Mohanakrishnan/sql_query_example
--- license: other ---
JordanTallon/CA3109-Movie-Genre-Classification-From-Keywords
--- license: cc0-1.0 ---
DylanonWic/common_voice_10_1_th_clean_split_0_old
--- dataset_info: features: - name: sentence dtype: string - name: labels sequence: int64 - name: input_values sequence: float32 splits: - name: train num_bytes: 13074645939.656857 num_examples: 50670 download_size: 11878689391 dataset_size: 13074645939.656857 --- # Dataset Card for "common_voice_10_1_th_clean_split_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GabrielTOP/BaianinhoCB
--- license: openrail ---
davidfant/natural-questions-chunk-26
--- dataset_info: features: - name: id dtype: string - name: document struct: - name: html dtype: string - name: title dtype: string - name: tokens sequence: - name: end_byte dtype: int64 - name: is_html dtype: bool - name: start_byte dtype: int64 - name: token dtype: string - name: url dtype: string - name: question struct: - name: text dtype: string - name: tokens sequence: string - name: long_answer_candidates sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: annotations sequence: - name: id dtype: string - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: short_answers sequence: - name: end_byte dtype: int64 - name: end_token dtype: int64 - name: start_byte dtype: int64 - name: start_token dtype: int64 - name: text dtype: string - name: yes_no_answer dtype: class_label: names: '0': 'NO' '1': 'YES' splits: - name: train num_bytes: 4647266600 num_examples: 10000 download_size: 1802877089 dataset_size: 4647266600 --- # Dataset Card for "natural-questions-chunk-26" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wanhao/text_image
--- task_categories: - text-classification - translation size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
shivangibithel/Flickr8k
--- license: apache-2.0 task_categories: - image-to-text - text-to-image language: - en size_categories: - 1K<n<10K ---
Prathamesh25/university_que_ans_v1
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 43629 num_examples: 299 download_size: 9875 dataset_size: 43629 configs: - config_name: default data_files: - split: train path: data/train-* ---
prajdabre/KreolMorisienMT
--- license: cc --- MorisienMT is a dataset for Mauritian Creole Machine Translation. This dataset consists of training, development and test set splits for English--Creole as well as French--Creole translation. The data comes from a variety of sources and hence can be considered as belonging to the general domain. The development and test sets consist of 500 and 1000 sentences respectively. Both evaluation sets are trilingual. The training set for English--Creole contains 21,810 lines. The training set for French--Creole contains 15,239 lines. Additionally, one can extract a trilingual English-French-Creole training set of 13,861 lines using Creole as a pivot. Finally, we also provide a Creole monolingual corpus of 45,364 lines. Note that a significant portion of the dataset is a dictionary of word pairs/triplets, nevertheless it is a start. Usage: (TODO: beautify) 1. Using huggingface datasets: load_dataset("prajdabre/MorisienMT", "en-cr", split="train") 2. Convert to moses format: load the dataset as in step 1, each item is a json object so iterate over the loaded dataset object and use the key and value, "input" and "target" respectively, to get the translation pairs. Feel free to use the dataset for your research but don't forget to attribute our upcoming paper which will be uploaded to arxiv shortly. Note: MorisienMT was originally partly developed by Dr Aneerav Sukhoo from the University of Mauritius in 2014 when he was a visiting researcher in IIT Bombay. Dr Sukhoo and I worked on the MT experiments together, but never publicly released the dataset back then. Furthermore, the dataset splits and experiments were not done in a highly principled manner, which is required in the present day. Therefore, we improve the quality of splits and officially release the data for people to use.
ahmadkhan1022/kaggle
--- license: - pddl converted_from: kaggle kaggle_id: ahmadkhan10/mergeddataset --- # Dataset Card for MergedDataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://kaggle.com/datasets/ahmadkhan10/mergeddataset - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators This dataset was shared by [@ahmadkhan10](https://kaggle.com/ahmadkhan10) ### Licensing Information The license for this dataset is pddl ### Citation Information ```bibtex [More Information Needed] ``` ### Contributions [More Information Needed]
open-llm-leaderboard/details_StudentLLM__Alpagasus-2-13b-QLoRA-merged
--- pretty_name: Evaluation run of StudentLLM/Alpagasus-2-13b-QLoRA-merged dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [StudentLLM/Alpagasus-2-13b-QLoRA-merged](https://huggingface.co/StudentLLM/Alpagasus-2-13b-QLoRA-merged)\ \ 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 4 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_StudentLLM__Alpagasus-2-13b-QLoRA-merged\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T20:12:36.073167](https://huggingface.co/datasets/open-llm-leaderboard/details_StudentLLM__Alpagasus-2-13b-QLoRA-merged/blob/main/results_2023-10-27T20-12-36.073167.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\": 0.001572986577181208,\n\ \ \"em_stderr\": 0.000405845113241773,\n \"f1\": 0.06265939597315423,\n\ \ \"f1_stderr\": 0.001378921060077413,\n \"acc\": 0.4424643648503177,\n\ \ \"acc_stderr\": 0.010216085204246378\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.001572986577181208,\n \"em_stderr\": 0.000405845113241773,\n\ \ \"f1\": 0.06265939597315423,\n \"f1_stderr\": 0.001378921060077413\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11144806671721001,\n \ \ \"acc_stderr\": 0.008668021353794427\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698329\n\ \ }\n}\n```" repo_url: https://huggingface.co/StudentLLM/Alpagasus-2-13b-QLoRA-merged 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_09_11T18_18_21.353761 path: - '**/details_harness|arc:challenge|25_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|arc:challenge|25_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-21T21-35-59.433556.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_26T10_45_15.535939 path: - '**/details_harness|drop|3_2023-10-26T10-45-15.535939.parquet' - split: 2023_10_27T20_12_36.073167 path: - '**/details_harness|drop|3_2023-10-27T20-12-36.073167.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-27T20-12-36.073167.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_26T10_45_15.535939 path: - '**/details_harness|gsm8k|5_2023-10-26T10-45-15.535939.parquet' - split: 2023_10_27T20_12_36.073167 path: - '**/details_harness|gsm8k|5_2023-10-27T20-12-36.073167.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-27T20-12-36.073167.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hellaswag|10_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hellaswag|10_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-11T18-18-21.353761.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-21T21-35-59.433556.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-management|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-management|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T21-35-59.433556.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_11T18_18_21.353761 path: - '**/details_harness|truthfulqa:mc|0_2023-09-11T18-18-21.353761.parquet' - split: 2023_09_21T21_35_59.433556 path: - '**/details_harness|truthfulqa:mc|0_2023-09-21T21-35-59.433556.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-21T21-35-59.433556.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_26T10_45_15.535939 path: - '**/details_harness|winogrande|5_2023-10-26T10-45-15.535939.parquet' - split: 2023_10_27T20_12_36.073167 path: - '**/details_harness|winogrande|5_2023-10-27T20-12-36.073167.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-27T20-12-36.073167.parquet' - config_name: results data_files: - split: 2023_09_11T18_18_21.353761 path: - results_2023-09-11T18-18-21.353761.parquet - split: 2023_09_21T21_35_59.433556 path: - results_2023-09-21T21-35-59.433556.parquet - split: 2023_10_26T10_45_15.535939 path: - results_2023-10-26T10-45-15.535939.parquet - split: 2023_10_27T20_12_36.073167 path: - results_2023-10-27T20-12-36.073167.parquet - split: latest path: - results_2023-10-27T20-12-36.073167.parquet --- # Dataset Card for Evaluation run of StudentLLM/Alpagasus-2-13b-QLoRA-merged ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/StudentLLM/Alpagasus-2-13b-QLoRA-merged - **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 [StudentLLM/Alpagasus-2-13b-QLoRA-merged](https://huggingface.co/StudentLLM/Alpagasus-2-13b-QLoRA-merged) 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 4 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_StudentLLM__Alpagasus-2-13b-QLoRA-merged", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T20:12:36.073167](https://huggingface.co/datasets/open-llm-leaderboard/details_StudentLLM__Alpagasus-2-13b-QLoRA-merged/blob/main/results_2023-10-27T20-12-36.073167.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": 0.001572986577181208, "em_stderr": 0.000405845113241773, "f1": 0.06265939597315423, "f1_stderr": 0.001378921060077413, "acc": 0.4424643648503177, "acc_stderr": 0.010216085204246378 }, "harness|drop|3": { "em": 0.001572986577181208, "em_stderr": 0.000405845113241773, "f1": 0.06265939597315423, "f1_stderr": 0.001378921060077413 }, "harness|gsm8k|5": { "acc": 0.11144806671721001, "acc_stderr": 0.008668021353794427 }, "harness|winogrande|5": { "acc": 0.7734806629834254, "acc_stderr": 0.011764149054698329 } } ``` ### 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]
stas/cm4-synthetic-testing
--- license: bigscience-openrail-m --- This dataset is designed to be used in testing multimodal text/image models. It's derived from cm4-10k dataset. The current splits are: `['100.unique', '100.repeat', '300.unique', '300.repeat', '1k.unique', '1k.repeat', '10k.unique', '10k.repeat']`. The `unique` ones ensure uniqueness across text entries. The `repeat` ones are repeating the same 10 unique records: - these are useful for memory leaks debugging as the records are always the same and thus remove the record variation from the equation. The default split is `100.unique` The full process of this dataset creation, including which records were used to build it, is documented inside [cm4-synthetic-testing.py](https://huggingface.co/datasets/HuggingFaceM4/cm4-synthetic-testing/blob/main/cm4-synthetic-testing.py)
EgilKarlsen/Thunderbird_DistilRoBERTa_Baseline
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - name: '32' dtype: float32 - name: '33' dtype: float32 - name: '34' dtype: float32 - name: '35' dtype: float32 - name: '36' dtype: float32 - name: '37' dtype: float32 - name: '38' dtype: float32 - name: '39' dtype: float32 - name: '40' dtype: float32 - name: '41' dtype: float32 - name: '42' dtype: float32 - name: '43' dtype: float32 - name: '44' dtype: float32 - name: '45' dtype: float32 - name: '46' dtype: float32 - name: '47' dtype: float32 - name: '48' dtype: float32 - name: '49' dtype: float32 - name: '50' dtype: float32 - name: '51' dtype: float32 - name: '52' dtype: float32 - name: '53' dtype: float32 - name: '54' dtype: float32 - name: '55' dtype: float32 - name: '56' dtype: float32 - name: '57' dtype: float32 - name: '58' dtype: float32 - name: '59' dtype: float32 - name: '60' dtype: float32 - name: '61' dtype: float32 - name: '62' dtype: float32 - name: '63' dtype: float32 - name: '64' dtype: float32 - name: '65' dtype: float32 - name: '66' dtype: float32 - name: '67' dtype: float32 - name: '68' dtype: float32 - name: '69' dtype: float32 - name: '70' dtype: float32 - name: '71' dtype: float32 - name: '72' dtype: float32 - name: '73' dtype: float32 - name: '74' dtype: float32 - name: '75' dtype: float32 - name: '76' dtype: float32 - name: '77' dtype: float32 - name: '78' dtype: float32 - name: '79' dtype: float32 - name: '80' dtype: float32 - name: '81' dtype: float32 - name: '82' dtype: float32 - name: '83' dtype: float32 - name: '84' dtype: float32 - name: '85' dtype: float32 - name: '86' dtype: float32 - name: '87' dtype: float32 - name: '88' dtype: float32 - name: '89' dtype: float32 - name: '90' dtype: float32 - name: '91' dtype: float32 - name: '92' dtype: float32 - name: '93' dtype: float32 - name: '94' dtype: float32 - name: '95' dtype: float32 - name: '96' dtype: float32 - name: '97' dtype: float32 - name: '98' dtype: float32 - name: '99' dtype: float32 - name: '100' dtype: float32 - name: '101' dtype: float32 - name: '102' dtype: float32 - name: '103' dtype: float32 - name: '104' dtype: float32 - name: '105' dtype: float32 - name: '106' dtype: float32 - name: '107' dtype: float32 - name: '108' dtype: float32 - name: '109' dtype: float32 - name: '110' dtype: float32 - name: '111' dtype: float32 - name: '112' dtype: float32 - name: '113' dtype: float32 - name: '114' dtype: float32 - name: '115' dtype: float32 - name: '116' dtype: float32 - name: '117' dtype: float32 - name: '118' dtype: float32 - name: '119' dtype: float32 - name: '120' dtype: float32 - name: '121' dtype: float32 - name: '122' dtype: float32 - name: '123' dtype: float32 - name: '124' dtype: float32 - name: '125' dtype: float32 - name: '126' dtype: float32 - name: '127' dtype: float32 - name: '128' dtype: float32 - name: '129' dtype: float32 - name: '130' dtype: float32 - name: '131' dtype: float32 - name: '132' dtype: float32 - name: '133' dtype: float32 - name: '134' dtype: float32 - name: '135' dtype: float32 - name: '136' dtype: float32 - name: '137' dtype: float32 - name: '138' dtype: float32 - 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name: train num_bytes: 115576729.6875 num_examples: 37500 - name: test num_bytes: 38525577.5 num_examples: 12500 download_size: 0 dataset_size: 154102307.1875 --- # Dataset Card for "Thunderbird_DistilRoBERTa_Baseline" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ibragim-bad/arc_easy
--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices struct: - name: label sequence: string - name: text sequence: string - name: answerKey dtype: string splits: - name: test num_bytes: 657514 num_examples: 2376 - name: train num_bytes: 619000 num_examples: 2251 - name: validation num_bytes: 157394 num_examples: 570 download_size: 763157 dataset_size: 1433908 --- # Dataset Card for "arc_easy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/orange_touhou
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of orange (Touhou) This is the dataset of orange (Touhou), containing 48 images and their tags. The core tags of this character are `long_hair, red_hair, hat, red_eyes, bow, hair_bow, ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 48 | 27.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orange_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 48 | 22.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orange_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 76 | 36.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orange_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 48 | 26.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orange_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 76 | 41.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orange_touhou/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/orange_touhou', 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 | 23 | ![](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, solo, puffy_short_sleeves, shirt, shoes, vest, white_bow, yellow_headwear, yellow_shorts, full_body, smile, holding, open_mouth, socks, looking_at_viewer, simple_background, white_background, white_footwear | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | puffy_short_sleeves | shirt | shoes | vest | white_bow | yellow_headwear | yellow_shorts | full_body | smile | holding | open_mouth | socks | looking_at_viewer | simple_background | white_background | white_footwear | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------------------|:--------|:--------|:-------|:------------|:------------------|:----------------|:------------|:--------|:----------|:-------------|:--------|:--------------------|:--------------------|:-------------------|:-----------------| | 0 | 23 | ![](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 |
Nexdata/Mandarin_Heavy_Accent_Speech_Data
--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Mandarin_Heavy_Accent_Speech_Data_by_Mobile_Phone ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.nexdata.ai/datasets/44?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary It collects 2,034 local Chinese from 26 provinces like Henan, Shanxi, Sichuan, Hunan, Fujian, etc. It is mandarin speech data with heavy accent. The recoring contents are finance and economics, entertainment, policy, news, TV, and movies. For more details, please refer to the link: https://www.nexdata.ai/datasets/44?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Mandarin Chinese ## 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 Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
tglcourse/latent_afhqv2_512px
--- dataset_info: features: - name: label dtype: class_label: names: 0: cat 1: dog 2: wild - name: latent sequence: sequence: sequence: float32 splits: - name: train num_bytes: 1052290164 num_examples: 15803 download_size: 1038619876 dataset_size: 1052290164 --- # Dataset Card for "latent_afhqv2_512px" Each image is cropped to 512px square and encoded to a 4x64x64 latent representation using the same VAE as that employed by Stable Diffusion Decoding ```python from diffusers import AutoencoderKL from datasets import load_dataset from PIL import Image import numpy as np import torch # load the dataset dataset = load_dataset('tglcourse/latent_lsun_church_256px') # Load the VAE (requires access - see repo model card for info) vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae") latent = torch.tensor([dataset['train'][0]['latent']]) # To tensor (bs, 4, 64, 3264 latent = (1 / 0.18215) * latent # Scale to match SD implementation with torch.no_grad(): image = vae.decode(latent).sample[0] # Decode image = (image / 2 + 0.5).clamp(0, 1) # To (0, 1) image = image.detach().cpu().permute(1, 2, 0).numpy() # To numpy, channels lsat image = (image * 255).round().astype("uint8") # (0, 255) and type uint8 image = Image.fromarray(image) # To PIL image # The resulting PIL image ```
liuyanchen1015/MULTI_VALUE_mrpc_you_ye
--- 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: 2289 num_examples: 9 - name: train num_bytes: 9366 num_examples: 35 - name: validation num_bytes: 560 num_examples: 2 download_size: 19112 dataset_size: 12215 --- # Dataset Card for "MULTI_VALUE_mrpc_you_ye" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
fujiki/llm-japanese-dataset_wikipedia
--- license: cc-by-sa-3.0 dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 617413933 num_examples: 1347381 download_size: 335053357 dataset_size: 617413933 --- - This dataset is a subset of [izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) only including `wikipedia` task. - Please also refer to the original dataset: [izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset)
LiamLi1991/law
--- license: mit ---
yangdechuan/demo
--- license: openrail language: - en task_categories: - text-generation pretty_name: tiny_demo size_categories: - n<1K ---
yangyz1230/promoter_tata_not_filtered
--- 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: 1913526 num_examples: 5479 - name: test num_bytes: 213144 num_examples: 612 download_size: 1031281 dataset_size: 2126670 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
VegaKH/bpdata
--- license: afl-3.0 ---
thelou1s/AudioSet
--- license: apache-2.0 ---
pontusnorman123/sroie_dataset
--- dataset_info: features: - name: guid sequence: string - name: words sequence: string - name: labels sequence: int64 - name: boxes sequence: sequence: int64 - name: actual_bboxes sequence: sequence: int64 - name: page_size sequence: sequence: int64 - name: images dtype: image splits: - name: train num_bytes: 27605888.0 num_examples: 32 download_size: 27055763 dataset_size: 27605888.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
DarshanaS/IndicAccentDb
--- license: c-uda --- ## 1. Introduction Introducing a novel accent database "IndicAccentDB" which satisfies the below requirements: * **Gender balance:** The speech database should be a collection of a wide range of speakers balancing both the male and female speakers to display the characteristics of the speakers speech. * **Phonetically balanced uniform content:** To make the classification task simpler and models to distinguish the speakers, we considered building the IndicAccentDB with uniform content, a collection of speech recordings for the Harvard sentences. These sentences gather intrinsic information by combining different phonemes and grammatically focused vocabulary. These sentences are appropriately expressing accents in sentence-level discourse. You can access the Harvard sentences (sample shown below) dataset here: [Harvard Sentences](https://www.cs.columbia.edu/~hgs/audio/harvard.html) recited by the speakers in the recordings. *The juice of lemons makes fine punch.* *The fish twisted and turned on the bent hook.* * IndicAccentDB contains speech recordings in six non-native English accents of Gujarati, Hindi, Kannada, Malayalam, Tamil, and Telugu. We collected six non-native accents from volunteers who had strong non-native English accents and were well-versed in speaking at least one Indian language. Each speaker was asked to recite the Harvard sentences. The Harvard sentences dataset consists of 72 sets of ten sentences each and is phonetically balanced sentences that are neither too short nor too long. ## 2. Dataset Usage To use the dataset in your Python program, refer to the following script: ```python3 from datasets import load_dataset accent_db = load_dataset("DarshanaS/IndicAccentDb") ``` ## 3. Publications 1. [S. Darshana, H. Theivaprakasham, G. Jyothish Lal, B. Premjith, V. Sowmya and K. Soman, "MARS: A Hybrid Deep CNN-based Multi-Accent Recognition System for English Language," 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR), Hyderabad, India, 2022, pp. 1-6, doi: 10.1109/ICAITPR51569.2022.9844177.](https://ieeexplore.ieee.org/document/9844177)
sparklemidi/Prompt-HTML
--- license: mit ---
jjldo21/IndustrialDetectionStaticCameras
--- license: mit size_categories: - 1K<n<10K task_categories: - object-detection tags: - industry dataset_info: features: - name: image dtype: image - name: labels sequence: - name: object_type dtype: string - name: truncation dtype: float32 - name: occlusion dtype: int32 - name: alpha dtype: int32 - name: left dtype: float32 - name: top dtype: float32 - name: right dtype: float32 - name: bottom dtype: float32 - name: height dtype: int32 - name: width dtype: int32 - name: length dtype: int32 - name: x dtype: int32 - name: y dtype: int32 - name: z dtype: int32 - name: rotation_y dtype: int32 splits: - name: video1 num_bytes: 4497677.132999999 num_examples: 1261 - name: video2 num_bytes: 4116557.136 num_examples: 1221 - name: video3 num_bytes: 4034190.129 num_examples: 1221 - name: video4 num_bytes: 5164007.345000001 num_examples: 1481 - name: video5 num_bytes: 4733783.518 num_examples: 1301 download_size: 19236723 dataset_size: 22546215.261 configs: - config_name: default data_files: - split: video1 path: data/video1-* - split: video2 path: data/video2-* - split: video3 path: data/video3-* - split: video4 path: data/video4-* - split: video5 path: data/video5-* --- The **IndustrialDetectionStaticCameras** dataset is divided into five primary files named `videoY`, where `Y=1,2,3,4,5`. Each `videoY` folder contains the following: - The video of the scene in `.mp4` format: `videoY.mp4` - A folder with the images of each frame of the video: `imgs_videoY` - A folder that includes for each frame a `.txt` file that holds for each labelled object a line with the annotation in kitti format: `annotations_videoY` **Remark:** Each label file contains a set of lines, with each line representing the annotation for a single object in the corresponding image. The format of each line is as follows: `<object_type> <truncation> <occlusion> <alpha> <left> <top> <right> <bottom> <height> <width> <length> <x> <y> <z> <rotation_y>`, where only the fields `<object_type>, <left>, <top>, <right>, <bottom>` and `<rotation_y>` are considered. The `<rotation_y>` field has been used to indicate whether the labelled object is a static object in the scene or not -value `1` represents that object is static and value `0` symbolizes that it is not-. ### Download the dataset: ```python from datasets import load_dataset dataset = load_dataset("jjldo21/IndustrialDetectionStaticCameras") ```
CyberHarem/saito_ena_yurucamp
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Saitō Ena This is the dataset of Saitō Ena, containing 268 images and their tags. 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)). | Name | Images | Download | Description | |:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------| | raw | 268 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 653 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 756 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 268 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 268 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 268 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 653 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 653 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 562 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not exceeding 512x512 pixels. | | stage3-eyes-640 | 756 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 756 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
sarpba/test
--- dataset_info: features: - name: input_features sequence: sequence: sequence: float32 - name: labels sequence: int64 - name: input_length dtype: float64 splits: - name: train num_bytes: 18559963744.0 num_examples: 19322 - name: test num_bytes: 3950822820 num_examples: 4113 download_size: 3796457350 dataset_size: 22510786564.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
girmahahaha/semeval-task-5-english
--- dataset_info: features: - name: labels sequence: int64 - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 2456894 num_examples: 6838 - name: test num_bytes: 1214918 num_examples: 3259 - name: validation num_bytes: 338816 num_examples: 886 download_size: 818051 dataset_size: 4010628 --- # Dataset Card for "semeval-task-5-english" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_ToastyPigeon__smolphin-test-stack-sorted
--- pretty_name: Evaluation run of ToastyPigeon/smolphin-test-stack-sorted dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ToastyPigeon/smolphin-test-stack-sorted](https://huggingface.co/ToastyPigeon/smolphin-test-stack-sorted)\ \ 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_ToastyPigeon__smolphin-test-stack-sorted\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-29T21:12:59.929577](https://huggingface.co/datasets/open-llm-leaderboard/details_ToastyPigeon__smolphin-test-stack-sorted/blob/main/results_2024-03-29T21-12-59.929577.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\": 0.2693886134221748,\n\ \ \"acc_stderr\": 0.03126598314102477,\n \"acc_norm\": 0.2709400437138604,\n\ \ \"acc_norm_stderr\": 0.03203501471283497,\n \"mc1\": 0.2386780905752754,\n\ \ \"mc1_stderr\": 0.014922629695456418,\n \"mc2\": 0.3747768935887674,\n\ \ \"mc2_stderr\": 0.013984674618562865\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.2901023890784983,\n \"acc_stderr\": 0.013261573677520764,\n\ \ \"acc_norm\": 0.32337883959044367,\n \"acc_norm_stderr\": 0.01366942163001214\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.43905596494722166,\n\ \ \"acc_stderr\": 0.004952576863315216,\n \"acc_norm\": 0.5907189802828122,\n\ \ \"acc_norm_stderr\": 0.0049069629803282985\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.03785714465066654,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.03785714465066654\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17105263157894737,\n \"acc_stderr\": 0.030643607071677084,\n\ \ \"acc_norm\": 0.17105263157894737,\n \"acc_norm_stderr\": 0.030643607071677084\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.28,\n\ \ \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.02544786382510861,\n\ \ \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.02544786382510861\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.37,\n\ \ \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \ \ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.21965317919075145,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.21965317919075145,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.042801058373643966,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.042801058373643966\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.24680851063829787,\n \"acc_stderr\": 0.0281854413012341,\n\ \ \"acc_norm\": 0.24680851063829787,\n \"acc_norm_stderr\": 0.0281854413012341\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n\ \ \"acc_stderr\": 0.04185774424022056,\n \"acc_norm\": 0.2719298245614035,\n\ \ \"acc_norm_stderr\": 0.04185774424022056\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.037245636197746325,\n\ \ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.037245636197746325\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\ \ \"acc_stderr\": 0.040061680838488774,\n \"acc_norm\": 0.2777777777777778,\n\ \ \"acc_norm_stderr\": 0.040061680838488774\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.2806451612903226,\n\ \ \"acc_stderr\": 0.025560604721022902,\n \"acc_norm\": 0.2806451612903226,\n\ \ \"acc_norm_stderr\": 0.025560604721022902\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.30049261083743845,\n \"acc_stderr\": 0.03225799476233485,\n\ \ \"acc_norm\": 0.30049261083743845,\n \"acc_norm_stderr\": 0.03225799476233485\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\"\ : 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.2787878787878788,\n \"acc_stderr\": 0.035014387062967806,\n\ \ \"acc_norm\": 0.2787878787878788,\n \"acc_norm_stderr\": 0.035014387062967806\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2777777777777778,\n \"acc_stderr\": 0.03191178226713545,\n \"\ acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.03191178226713545\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.21243523316062177,\n \"acc_stderr\": 0.029519282616817258,\n\ \ \"acc_norm\": 0.21243523316062177,\n \"acc_norm_stderr\": 0.029519282616817258\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.30256410256410254,\n \"acc_stderr\": 0.02329088805377272,\n\ \ \"acc_norm\": 0.30256410256410254,\n \"acc_norm_stderr\": 0.02329088805377272\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24814814814814815,\n \"acc_stderr\": 0.0263357394040558,\n \ \ \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.0263357394040558\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136098,\n\ \ \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136098\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.271523178807947,\n \"acc_stderr\": 0.036313298039696545,\n \"\ acc_norm\": 0.271523178807947,\n \"acc_norm_stderr\": 0.036313298039696545\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.28073394495412846,\n \"acc_stderr\": 0.019266055045871616,\n \"\ acc_norm\": 0.28073394495412846,\n \"acc_norm_stderr\": 0.019266055045871616\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.22362869198312235,\n \"acc_stderr\": 0.02712329820522997,\n\ \ \"acc_norm\": 0.22362869198312235,\n \"acc_norm_stderr\": 0.02712329820522997\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.27802690582959644,\n\ \ \"acc_stderr\": 0.030069584874494053,\n \"acc_norm\": 0.27802690582959644,\n\ \ \"acc_norm_stderr\": 0.030069584874494053\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.30578512396694213,\n \"acc_stderr\": 0.04205953933884124,\n \"\ acc_norm\": 0.30578512396694213,\n \"acc_norm_stderr\": 0.04205953933884124\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2037037037037037,\n\ \ \"acc_stderr\": 0.03893542518824848,\n \"acc_norm\": 0.2037037037037037,\n\ \ \"acc_norm_stderr\": 0.03893542518824848\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.25153374233128833,\n \"acc_stderr\": 0.03408997886857529,\n\ \ \"acc_norm\": 0.25153374233128833,\n \"acc_norm_stderr\": 0.03408997886857529\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.20535714285714285,\n\ \ \"acc_stderr\": 0.03834241021419073,\n \"acc_norm\": 0.20535714285714285,\n\ \ \"acc_norm_stderr\": 0.03834241021419073\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.24271844660194175,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.24271844660194175,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.18803418803418803,\n\ \ \"acc_stderr\": 0.025598193686652258,\n \"acc_norm\": 0.18803418803418803,\n\ \ \"acc_norm_stderr\": 0.025598193686652258\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.2656449553001277,\n\ \ \"acc_stderr\": 0.015794302487888736,\n \"acc_norm\": 0.2656449553001277,\n\ \ \"acc_norm_stderr\": 0.015794302487888736\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.21676300578034682,\n \"acc_stderr\": 0.022183477668412863,\n\ \ \"acc_norm\": 0.21676300578034682,\n \"acc_norm_stderr\": 0.022183477668412863\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27262569832402234,\n\ \ \"acc_stderr\": 0.014893391735249588,\n \"acc_norm\": 0.27262569832402234,\n\ \ \"acc_norm_stderr\": 0.014893391735249588\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.24836601307189543,\n \"acc_stderr\": 0.02473998135511359,\n\ \ \"acc_norm\": 0.24836601307189543,\n \"acc_norm_stderr\": 0.02473998135511359\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2508038585209003,\n\ \ \"acc_stderr\": 0.024619771956697168,\n \"acc_norm\": 0.2508038585209003,\n\ \ \"acc_norm_stderr\": 0.024619771956697168\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2839506172839506,\n \"acc_stderr\": 0.02508947852376513,\n\ \ \"acc_norm\": 0.2839506172839506,\n \"acc_norm_stderr\": 0.02508947852376513\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.25886524822695034,\n \"acc_stderr\": 0.026129572527180844,\n \ \ \"acc_norm\": 0.25886524822695034,\n \"acc_norm_stderr\": 0.026129572527180844\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24315514993481094,\n\ \ \"acc_stderr\": 0.01095655665441736,\n \"acc_norm\": 0.24315514993481094,\n\ \ \"acc_norm_stderr\": 0.01095655665441736\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.2867647058823529,\n \"acc_stderr\": 0.027472274473233818,\n\ \ \"acc_norm\": 0.2867647058823529,\n \"acc_norm_stderr\": 0.027472274473233818\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.23529411764705882,\n \"acc_stderr\": 0.017160587235046338,\n \ \ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.017160587235046338\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.18181818181818182,\n\ \ \"acc_stderr\": 0.03694284335337802,\n \"acc_norm\": 0.18181818181818182,\n\ \ \"acc_norm_stderr\": 0.03694284335337802\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.37142857142857144,\n \"acc_stderr\": 0.03093285879278984,\n\ \ \"acc_norm\": 0.37142857142857144,\n \"acc_norm_stderr\": 0.03093285879278984\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2935323383084577,\n\ \ \"acc_stderr\": 0.032200241045342054,\n \"acc_norm\": 0.2935323383084577,\n\ \ \"acc_norm_stderr\": 0.032200241045342054\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2469879518072289,\n\ \ \"acc_stderr\": 0.03357351982064537,\n \"acc_norm\": 0.2469879518072289,\n\ \ \"acc_norm_stderr\": 0.03357351982064537\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.23391812865497075,\n \"acc_stderr\": 0.032467217651178264,\n\ \ \"acc_norm\": 0.23391812865497075,\n \"acc_norm_stderr\": 0.032467217651178264\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2386780905752754,\n\ \ \"mc1_stderr\": 0.014922629695456418,\n \"mc2\": 0.3747768935887674,\n\ \ \"mc2_stderr\": 0.013984674618562865\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6124704025256511,\n \"acc_stderr\": 0.013692354636016766\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.019711902956785442,\n \ \ \"acc_stderr\": 0.0038289829787357026\n }\n}\n```" repo_url: https://huggingface.co/ToastyPigeon/smolphin-test-stack-sorted 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_29T21_12_59.929577 path: - '**/details_harness|arc:challenge|25_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-29T21-12-59.929577.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|gsm8k|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hellaswag|10_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-29T21-12-59.929577.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-management|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-29T21-12-59.929577.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|truthfulqa:mc|0_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-29T21-12-59.929577.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_29T21_12_59.929577 path: - '**/details_harness|winogrande|5_2024-03-29T21-12-59.929577.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-29T21-12-59.929577.parquet' - config_name: results data_files: - split: 2024_03_29T21_12_59.929577 path: - results_2024-03-29T21-12-59.929577.parquet - split: latest path: - results_2024-03-29T21-12-59.929577.parquet --- # Dataset Card for Evaluation run of ToastyPigeon/smolphin-test-stack-sorted <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ToastyPigeon/smolphin-test-stack-sorted](https://huggingface.co/ToastyPigeon/smolphin-test-stack-sorted) 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_ToastyPigeon__smolphin-test-stack-sorted", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-29T21:12:59.929577](https://huggingface.co/datasets/open-llm-leaderboard/details_ToastyPigeon__smolphin-test-stack-sorted/blob/main/results_2024-03-29T21-12-59.929577.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": 0.2693886134221748, "acc_stderr": 0.03126598314102477, "acc_norm": 0.2709400437138604, "acc_norm_stderr": 0.03203501471283497, "mc1": 0.2386780905752754, "mc1_stderr": 0.014922629695456418, "mc2": 0.3747768935887674, "mc2_stderr": 0.013984674618562865 }, "harness|arc:challenge|25": { "acc": 0.2901023890784983, "acc_stderr": 0.013261573677520764, "acc_norm": 0.32337883959044367, "acc_norm_stderr": 0.01366942163001214 }, "harness|hellaswag|10": { "acc": 0.43905596494722166, "acc_stderr": 0.004952576863315216, "acc_norm": 0.5907189802828122, "acc_norm_stderr": 0.0049069629803282985 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.25925925925925924, "acc_stderr": 0.03785714465066654, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.03785714465066654 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17105263157894737, "acc_stderr": 0.030643607071677084, "acc_norm": 0.17105263157894737, "acc_norm_stderr": 0.030643607071677084 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2188679245283019, "acc_stderr": 0.02544786382510861, "acc_norm": 0.2188679245283019, "acc_norm_stderr": 0.02544786382510861 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2361111111111111, "acc_stderr": 0.03551446610810826, "acc_norm": 0.2361111111111111, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.21965317919075145, "acc_stderr": 0.031568093627031744, "acc_norm": 0.21965317919075145, "acc_norm_stderr": 0.031568093627031744 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.042801058373643966, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.042801058373643966 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.24680851063829787, "acc_stderr": 0.0281854413012341, "acc_norm": 0.24680851063829787, "acc_norm_stderr": 0.0281854413012341 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2719298245614035, "acc_stderr": 0.04185774424022056, "acc_norm": 0.2719298245614035, "acc_norm_stderr": 0.04185774424022056 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.27586206896551724, "acc_stderr": 0.037245636197746325, "acc_norm": 0.27586206896551724, "acc_norm_stderr": 0.037245636197746325 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2777777777777778, "acc_stderr": 0.040061680838488774, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.040061680838488774 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.2806451612903226, "acc_stderr": 0.025560604721022902, "acc_norm": 0.2806451612903226, "acc_norm_stderr": 0.025560604721022902 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.30049261083743845, "acc_stderr": 0.03225799476233485, "acc_norm": 0.30049261083743845, "acc_norm_stderr": 0.03225799476233485 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.2787878787878788, "acc_stderr": 0.035014387062967806, "acc_norm": 0.2787878787878788, "acc_norm_stderr": 0.035014387062967806 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2777777777777778, "acc_stderr": 0.03191178226713545, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.03191178226713545 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.21243523316062177, "acc_stderr": 0.029519282616817258, "acc_norm": 0.21243523316062177, "acc_norm_stderr": 0.029519282616817258 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.30256410256410254, "acc_stderr": 0.02329088805377272, "acc_norm": 0.30256410256410254, "acc_norm_stderr": 0.02329088805377272 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24814814814814815, "acc_stderr": 0.0263357394040558, "acc_norm": 0.24814814814814815, "acc_norm_stderr": 0.0263357394040558 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.28991596638655465, "acc_stderr": 0.029472485833136098, "acc_norm": 0.28991596638655465, "acc_norm_stderr": 0.029472485833136098 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.271523178807947, "acc_stderr": 0.036313298039696545, "acc_norm": 0.271523178807947, "acc_norm_stderr": 0.036313298039696545 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.28073394495412846, "acc_stderr": 0.019266055045871616, "acc_norm": 0.28073394495412846, "acc_norm_stderr": 0.019266055045871616 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4537037037037037, "acc_stderr": 0.033953227263757976, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.033953227263757976 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.22362869198312235, "acc_stderr": 0.02712329820522997, "acc_norm": 0.22362869198312235, "acc_norm_stderr": 0.02712329820522997 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.27802690582959644, "acc_stderr": 0.030069584874494053, "acc_norm": 0.27802690582959644, "acc_norm_stderr": 0.030069584874494053 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.30578512396694213, "acc_stderr": 0.04205953933884124, "acc_norm": 0.30578512396694213, "acc_norm_stderr": 0.04205953933884124 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.2037037037037037, "acc_stderr": 0.03893542518824848, "acc_norm": 0.2037037037037037, "acc_norm_stderr": 0.03893542518824848 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.25153374233128833, "acc_stderr": 0.03408997886857529, "acc_norm": 0.25153374233128833, "acc_norm_stderr": 0.03408997886857529 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.20535714285714285, "acc_stderr": 0.03834241021419073, "acc_norm": 0.20535714285714285, "acc_norm_stderr": 0.03834241021419073 }, "harness|hendrycksTest-management|5": { "acc": 0.24271844660194175, "acc_stderr": 0.04245022486384495, "acc_norm": 0.24271844660194175, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.18803418803418803, "acc_stderr": 0.025598193686652258, "acc_norm": 0.18803418803418803, "acc_norm_stderr": 0.025598193686652258 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2656449553001277, "acc_stderr": 0.015794302487888736, "acc_norm": 0.2656449553001277, "acc_norm_stderr": 0.015794302487888736 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.21676300578034682, "acc_stderr": 0.022183477668412863, "acc_norm": 0.21676300578034682, "acc_norm_stderr": 0.022183477668412863 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.27262569832402234, "acc_stderr": 0.014893391735249588, "acc_norm": 0.27262569832402234, "acc_norm_stderr": 0.014893391735249588 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.24836601307189543, "acc_stderr": 0.02473998135511359, "acc_norm": 0.24836601307189543, "acc_norm_stderr": 0.02473998135511359 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2508038585209003, "acc_stderr": 0.024619771956697168, "acc_norm": 0.2508038585209003, "acc_norm_stderr": 0.024619771956697168 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2839506172839506, "acc_stderr": 0.02508947852376513, "acc_norm": 0.2839506172839506, "acc_norm_stderr": 0.02508947852376513 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.25886524822695034, "acc_stderr": 0.026129572527180844, "acc_norm": 0.25886524822695034, "acc_norm_stderr": 0.026129572527180844 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.24315514993481094, "acc_stderr": 0.01095655665441736, "acc_norm": 0.24315514993481094, "acc_norm_stderr": 0.01095655665441736 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.2867647058823529, "acc_stderr": 0.027472274473233818, "acc_norm": 0.2867647058823529, "acc_norm_stderr": 0.027472274473233818 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.23529411764705882, "acc_stderr": 0.017160587235046338, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.017160587235046338 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.18181818181818182, "acc_stderr": 0.03694284335337802, "acc_norm": 0.18181818181818182, "acc_norm_stderr": 0.03694284335337802 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.37142857142857144, "acc_stderr": 0.03093285879278984, "acc_norm": 0.37142857142857144, "acc_norm_stderr": 0.03093285879278984 }, "harness|hendrycksTest-sociology|5": { "acc": 0.2935323383084577, "acc_stderr": 0.032200241045342054, "acc_norm": 0.2935323383084577, "acc_norm_stderr": 0.032200241045342054 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-virology|5": { "acc": 0.2469879518072289, "acc_stderr": 0.03357351982064537, "acc_norm": 0.2469879518072289, "acc_norm_stderr": 0.03357351982064537 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.23391812865497075, "acc_stderr": 0.032467217651178264, "acc_norm": 0.23391812865497075, "acc_norm_stderr": 0.032467217651178264 }, "harness|truthfulqa:mc|0": { "mc1": 0.2386780905752754, "mc1_stderr": 0.014922629695456418, "mc2": 0.3747768935887674, "mc2_stderr": 0.013984674618562865 }, "harness|winogrande|5": { "acc": 0.6124704025256511, "acc_stderr": 0.013692354636016766 }, "harness|gsm8k|5": { "acc": 0.019711902956785442, "acc_stderr": 0.0038289829787357026 } } ``` ## 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 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open-llm-leaderboard/details_AiMavenAi__AiMaven-SmartDawg-7b
--- pretty_name: Evaluation run of AiMavenAi/AiMaven-SmartDawg-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AiMavenAi/AiMaven-SmartDawg-7b](https://huggingface.co/AiMavenAi/AiMaven-SmartDawg-7b)\ \ 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_AiMavenAi__AiMaven-SmartDawg-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-16T18:46:13.340145](https://huggingface.co/datasets/open-llm-leaderboard/details_AiMavenAi__AiMaven-SmartDawg-7b/blob/main/results_2024-01-16T18-46-13.340145.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\": 0.6302261241898747,\n\ \ \"acc_stderr\": 0.03275821367319639,\n \"acc_norm\": 0.6319182401020044,\n\ \ \"acc_norm_stderr\": 0.033417578490707575,\n \"mc1\": 0.4186046511627907,\n\ \ \"mc1_stderr\": 0.01727001528447685,\n \"mc2\": 0.5886125043102783,\n\ \ \"mc2_stderr\": 0.015752842438606557\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6493174061433447,\n \"acc_stderr\": 0.013944635930726094,\n\ \ \"acc_norm\": 0.6791808873720137,\n \"acc_norm_stderr\": 0.013640943091946531\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6996614220274846,\n\ \ \"acc_stderr\": 0.004574683373821048,\n \"acc_norm\": 0.8716391157140012,\n\ \ \"acc_norm_stderr\": 0.0033380760156172602\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.02783491252754407,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.02783491252754407\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\ \ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\ \ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_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\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n\ \ \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.43137254901960786,\n\ \ \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n\ \ \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5319148936170213,\n\ \ \"acc_stderr\": 0.03261936918467381,\n \"acc_norm\": 0.5319148936170213,\n\ \ \"acc_norm_stderr\": 0.03261936918467381\n },\n \"harness|hendrycksTest-econometrics|5\"\ : {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n\ \ \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n\ \ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\ : 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"\ acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3915343915343915,\n \"acc_stderr\": 0.02513809138885111,\n \"\ acc_norm\": 0.3915343915343915,\n \"acc_norm_stderr\": 0.02513809138885111\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481006,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481006\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n\ \ \"acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593556,\n\ \ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593556\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.024035489676335068,\n \ \ \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.024035489676335068\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121626,\n\ \ \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121626\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8238532110091743,\n \"acc_stderr\": 0.01633288239343138,\n \"\ acc_norm\": 0.8238532110091743,\n \"acc_norm_stderr\": 0.01633288239343138\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967407,\n \"\ acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967407\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229962,\n \ \ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229962\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.03880848301082396,\n\ \ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.03880848301082396\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6859504132231405,\n \"acc_stderr\": 0.04236964753041018,\n \"\ acc_norm\": 0.6859504132231405,\n \"acc_norm_stderr\": 0.04236964753041018\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.043546310772605956,\n\ \ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.043546310772605956\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \ \ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816508\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n\ \ \"acc_stderr\": 0.014419123980931895,\n \"acc_norm\": 0.7956577266922095,\n\ \ \"acc_norm_stderr\": 0.014419123980931895\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.02418242749657761,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.02418242749657761\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.376536312849162,\n\ \ \"acc_stderr\": 0.016204672385106603,\n \"acc_norm\": 0.376536312849162,\n\ \ \"acc_norm_stderr\": 0.016204672385106603\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02609016250427905,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02609016250427905\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.025702640260603742,\n\ \ \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.025702640260603742\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.45390070921985815,\n \"acc_stderr\": 0.029700453247291467,\n \ \ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.029700453247291467\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44654498044328556,\n\ \ \"acc_stderr\": 0.012697046024399677,\n \"acc_norm\": 0.44654498044328556,\n\ \ \"acc_norm_stderr\": 0.012697046024399677\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6507352941176471,\n \"acc_stderr\": 0.028959755196824873,\n\ \ \"acc_norm\": 0.6507352941176471,\n \"acc_norm_stderr\": 0.028959755196824873\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6535947712418301,\n \"acc_stderr\": 0.019249785691717213,\n \ \ \"acc_norm\": 0.6535947712418301,\n \"acc_norm_stderr\": 0.019249785691717213\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4186046511627907,\n\ \ \"mc1_stderr\": 0.01727001528447685,\n \"mc2\": 0.5886125043102783,\n\ \ \"mc2_stderr\": 0.015752842438606557\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7900552486187845,\n \"acc_stderr\": 0.011446280629262631\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5724033358605004,\n \ \ \"acc_stderr\": 0.013627322286986808\n }\n}\n```" repo_url: https://huggingface.co/AiMavenAi/AiMaven-SmartDawg-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: 2024_01_16T18_46_13.340145 path: - '**/details_harness|arc:challenge|25_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-16T18-46-13.340145.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|gsm8k|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hellaswag|10_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-16T18-46-13.340145.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-management|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-16T18-46-13.340145.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|truthfulqa:mc|0_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-16T18-46-13.340145.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_16T18_46_13.340145 path: - '**/details_harness|winogrande|5_2024-01-16T18-46-13.340145.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-16T18-46-13.340145.parquet' - config_name: results data_files: - split: 2024_01_16T18_46_13.340145 path: - results_2024-01-16T18-46-13.340145.parquet - split: latest path: - results_2024-01-16T18-46-13.340145.parquet --- # Dataset Card for Evaluation run of AiMavenAi/AiMaven-SmartDawg-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [AiMavenAi/AiMaven-SmartDawg-7b](https://huggingface.co/AiMavenAi/AiMaven-SmartDawg-7b) 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_AiMavenAi__AiMaven-SmartDawg-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-16T18:46:13.340145](https://huggingface.co/datasets/open-llm-leaderboard/details_AiMavenAi__AiMaven-SmartDawg-7b/blob/main/results_2024-01-16T18-46-13.340145.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": 0.6302261241898747, "acc_stderr": 0.03275821367319639, "acc_norm": 0.6319182401020044, "acc_norm_stderr": 0.033417578490707575, "mc1": 0.4186046511627907, "mc1_stderr": 0.01727001528447685, "mc2": 0.5886125043102783, "mc2_stderr": 0.015752842438606557 }, "harness|arc:challenge|25": { "acc": 0.6493174061433447, "acc_stderr": 0.013944635930726094, "acc_norm": 0.6791808873720137, "acc_norm_stderr": 0.013640943091946531 }, "harness|hellaswag|10": { "acc": 0.6996614220274846, "acc_stderr": 0.004574683373821048, "acc_norm": 0.8716391157140012, "acc_norm_stderr": 0.0033380760156172602 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.02783491252754407, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.02783491252754407 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566017, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566017 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "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": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467381, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467381 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3915343915343915, "acc_stderr": 0.02513809138885111, "acc_norm": 0.3915343915343915, "acc_norm_stderr": 0.02513809138885111 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7516129032258064, "acc_stderr": 0.024580028921481006, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481006 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4630541871921182, "acc_stderr": 0.035083705204426656, "acc_norm": 0.4630541871921182, "acc_norm_stderr": 0.035083705204426656 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.023814477086593556, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.023814477086593556 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.658974358974359, "acc_stderr": 0.024035489676335068, "acc_norm": 0.658974358974359, "acc_norm_stderr": 0.024035489676335068 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6554621848739496, "acc_stderr": 0.030868682604121626, "acc_norm": 0.6554621848739496, "acc_norm_stderr": 0.030868682604121626 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8238532110091743, "acc_stderr": 0.01633288239343138, "acc_norm": 0.8238532110091743, "acc_norm_stderr": 0.01633288239343138 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4537037037037037, "acc_stderr": 0.033953227263757976, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.033953227263757976 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7794117647058824, "acc_stderr": 0.02910225438967407, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.02910225438967407 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229962, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229962 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.732824427480916, "acc_stderr": 0.03880848301082396, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.03880848301082396 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6859504132231405, "acc_stderr": 0.04236964753041018, "acc_norm": 0.6859504132231405, "acc_norm_stderr": 0.04236964753041018 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7484662576687117, "acc_stderr": 0.034089978868575295, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.034089978868575295 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7378640776699029, "acc_stderr": 0.043546310772605956, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.043546310772605956 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7956577266922095, "acc_stderr": 0.014419123980931895, "acc_norm": 0.7956577266922095, "acc_norm_stderr": 0.014419123980931895 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7196531791907514, "acc_stderr": 0.02418242749657761, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.02418242749657761 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.376536312849162, "acc_stderr": 0.016204672385106603, "acc_norm": 0.376536312849162, "acc_norm_stderr": 0.016204672385106603 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7058823529411765, "acc_stderr": 0.02609016250427905, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.02609016250427905 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.691358024691358, "acc_stderr": 0.025702640260603742, "acc_norm": 0.691358024691358, "acc_norm_stderr": 0.025702640260603742 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.45390070921985815, "acc_stderr": 0.029700453247291467, "acc_norm": 0.45390070921985815, "acc_norm_stderr": 0.029700453247291467 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44654498044328556, "acc_stderr": 0.012697046024399677, "acc_norm": 0.44654498044328556, "acc_norm_stderr": 0.012697046024399677 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6507352941176471, "acc_stderr": 0.028959755196824873, "acc_norm": 0.6507352941176471, "acc_norm_stderr": 0.028959755196824873 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6535947712418301, "acc_stderr": 0.019249785691717213, "acc_norm": 0.6535947712418301, "acc_norm_stderr": 0.019249785691717213 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801302, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801302 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.536144578313253, "acc_stderr": 0.038823108508905954, "acc_norm": 0.536144578313253, "acc_norm_stderr": 0.038823108508905954 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness|truthfulqa:mc|0": { "mc1": 0.4186046511627907, "mc1_stderr": 0.01727001528447685, "mc2": 0.5886125043102783, "mc2_stderr": 0.015752842438606557 }, "harness|winogrande|5": { "acc": 0.7900552486187845, "acc_stderr": 0.011446280629262631 }, "harness|gsm8k|5": { "acc": 0.5724033358605004, "acc_stderr": 0.013627322286986808 } } ``` ## 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]
lprat/wikidata_medium
--- dataset_info: features: - name: texts dtype: string - name: questions dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: train num_bytes: 174729655 num_examples: 40073 download_size: 17928919 dataset_size: 174729655 --- # Dataset Card for "wikidata_medium" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
adambuttrick/flair-ner-funding-statements-10K
--- license: cc ---
Elliot4AI/dolly-15k-chinese-guanacoformat
--- license: apache-2.0 task_categories: - text-classification - text-generation language: - zh tags: - finance size_categories: - 10K<n<100K --- # Dataset Summary ## 🏡🏡🏡🏡Fine-tune Dataset:中文数据集🏡🏡🏡🏡 😀😀😀😀😀😀😀😀 这个数据集是databricks/databricks-dolly-15k的中文guanaco版本
davidfant/wikipedia-simple
--- dataset_info: features: - name: id dtype: int64 - name: slug dtype: string - name: title dtype: string - name: revision_id dtype: int64 - name: markdown dtype: string - name: text dtype: string splits: - name: train num_bytes: 2150458756 num_examples: 338228 download_size: 1045572646 dataset_size: 2150458756 --- # Dataset Card for "wikipedia-simple" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
distilled-from-one-sec-cv12/chunk_195
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1027482852 num_examples: 200211 download_size: 1042897184 dataset_size: 1027482852 --- # Dataset Card for "chunk_195" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mdass/gpt_gen_desc_logos
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 2006069.0 num_examples: 100 download_size: 1991578 dataset_size: 2006069.0 --- # Dataset Card for "gpt_gen_desc_logos" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
distilled-one-sec-cv12-each-chunk-uniq/chunk_7
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1147966816.0 num_examples: 223688 download_size: 1172420614 dataset_size: 1147966816.0 --- # Dataset Card for "chunk_7" [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-71000
--- 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: 995654 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/ru_class_kantaicollection
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ru_class/戦艦ル級 (Kantai Collection) This is the dataset of ru_class/戦艦ル級 (Kantai Collection), containing 44 images and their tags. The core tags of this character are `long_hair, black_hair, pale_skin, glowing_eyes, blue_eyes, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 44 | 30.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ru_class_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 44 | 25.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ru_class_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 70 | 38.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ru_class_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 44 | 29.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ru_class_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 70 | 43.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ru_class_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/ru_class_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 9 | ![](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, abyssal_ship, solo, glowing, turret, cannon, yellow_eyes | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, abyssal_ship, black_pants, solo, looking_at_viewer, turret, aqua_eyes, boots, cannon, full_body, machinery, white_background, glowing, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | abyssal_ship | solo | glowing | turret | cannon | yellow_eyes | black_pants | looking_at_viewer | aqua_eyes | boots | full_body | machinery | white_background | simple_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:----------|:---------|:---------|:--------------|:--------------|:--------------------|:------------|:--------|:------------|:------------|:-------------------|:--------------------| | 0 | 9 | ![](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 | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | X | X | X | X | X | X | X | X |
OEvortex/Vortex-50k
--- language: - en license: other license_name: hsul license_link: https://huggingface.co/OEvortex/vortex-3b/raw/main/LICENSE.md ---
KK1mo/tedigan_mask
--- dataset_info: features: - name: id dtype: string - name: caption dtype: string - name: mask dtype: image - name: non_edited_image dtype: image - name: generated_image dtype: image splits: - name: train num_bytes: 1953997426.2739997 num_examples: 1498 download_size: 1954051980 dataset_size: 1953997426.2739997 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/thany_fireemblem
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of thany (Fire Emblem) This is the dataset of thany (Fire Emblem), containing 102 images and their tags. The core tags of this character are `blue_hair, short_hair, blue_eyes, headband, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 102 | 80.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thany_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 102 | 57.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thany_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 187 | 101.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thany_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 102 | 74.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thany_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 187 | 123.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/thany_fireemblem/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/thany_fireemblem', 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, thighhighs, breastplate, pegasus_knight_uniform_(fire_emblem), skirt, solo, belt, fingerless_gloves, thigh_boots, zettai_ryouiki, open_mouth, spear, full_body, white_background | | 1 | 6 | ![](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, hair_ornament, solo, strapless_dress, white_dress, bangs, detached_collar, feather_trim, flower, full_body, holding_bow_(weapon), medium_breasts, open_mouth, shiny_hair, thigh_boots, thighhighs, wedding_dress, white_footwear, high_heel_boots, layered_skirt, smile, bare_shoulders, circlet, earrings, looking_at_viewer, looking_away, simple_background, white_background | | 2 | 28 | ![](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, blush, open_mouth, hetero, solo_focus, nipples, sex, penis, 1boy, nude, medium_breasts, mosaic_censoring, vaginal, cum, pussy, sweat, thighhighs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | thighhighs | breastplate | pegasus_knight_uniform_(fire_emblem) | skirt | solo | belt | fingerless_gloves | thigh_boots | zettai_ryouiki | open_mouth | spear | full_body | white_background | hair_ornament | strapless_dress | white_dress | bangs | detached_collar | feather_trim | flower | holding_bow_(weapon) | medium_breasts | shiny_hair | wedding_dress | white_footwear | high_heel_boots | layered_skirt | smile | bare_shoulders | circlet | earrings | looking_at_viewer | looking_away | simple_background | blush | hetero | solo_focus | nipples | sex | penis | 1boy | nude | mosaic_censoring | vaginal | cum | pussy | sweat | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------------|:---------------------------------------|:--------|:-------|:-------|:--------------------|:--------------|:-----------------|:-------------|:--------|:------------|:-------------------|:----------------|:------------------|:--------------|:--------|:------------------|:---------------|:---------|:-----------------------|:-----------------|:-------------|:----------------|:-----------------|:------------------|:----------------|:--------|:-----------------|:----------|:-----------|:--------------------|:---------------|:--------------------|:--------|:---------|:-------------|:----------|:------|:--------|:-------|:-------|:-------------------|:----------|:------|:--------|:--------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | | X | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 2 | 28 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
TalTechNLP/err-newsroom
--- task_categories: - summarization - text2text-generation language: - et --- ## ERR Newsroom This dataset contains news articles from the website of Estonian Public Broadcasting (https://err.ee), from the period of 2016-2022. Each news article has three text fields: heading, lead-in and text. The dataset is divided into train, dev and test set. Dev set contains articles from November 2022 and test set from December 2022. The rest of the articles are in the train set.
benayas/massive_artificial_5pct_v2
--- dataset_info: features: - name: text dtype: string - name: category dtype: string splits: - name: train num_bytes: 802767 num_examples: 11514 download_size: 250983 dataset_size: 802767 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-project-xsum-19ae30f1-12355647
--- type: predictions tags: - autotrain - evaluation datasets: - xsum eval_info: task: summarization model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP10 metrics: [] dataset_name: xsum dataset_config: default dataset_split: test col_mapping: text: document 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-WIP10 * Dataset: xsum * 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.