davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
kozistr/mqa-ko	--- language: - ko license: cc0-1.0 task_categories: - question-answering tags: - mqa dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 541067862 num_examples: 1382378 download_size: 162865210 dataset_size: 541067862 configs: - config_name: default data_files: - split: train path: data/train-* --- * https://huggingface.co/datasets/clips/mqa
open-llm-leaderboard/details_Charlie911__MultiLoRA-llama2-mmlu	--- pretty_name: Evaluation run of Charlie911/MultiLoRA-llama2-mmlu dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Charlie911/MultiLoRA-llama2-mmlu](https://huggingface.co/Charlie911/MultiLoRA-llama2-mmlu)\ \ 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_Charlie911__MultiLoRA-llama2-mmlu\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T20:19:51.603035](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__MultiLoRA-llama2-mmlu/blob/main/results_2024-02-09T20-19-51.603035.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\": 0.42939450714393407,\n\ \ \"acc_stderr\": 0.03450029235435365,\n \"acc_norm\": 0.4336173195651683,\n\ \ \"acc_norm_stderr\": 0.03529727761229674,\n \"mc1\": 0.2558139534883721,\n\ \ \"mc1_stderr\": 0.01527417621928336,\n \"mc2\": 0.40926286124406613,\n\ \ \"mc2_stderr\": 0.01393003126171617\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.47013651877133106,\n \"acc_stderr\": 0.0145853058400071,\n\ \ \"acc_norm\": 0.5221843003412969,\n \"acc_norm_stderr\": 0.01459700192707614\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5821549492133041,\n\ \ \"acc_stderr\": 0.00492196413387402,\n \"acc_norm\": 0.7759410476000796,\n\ \ \"acc_norm_stderr\": 0.004161089244867776\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.45925925925925926,\n\ \ \"acc_stderr\": 0.04304979692464243,\n \"acc_norm\": 0.45925925925925926,\n\ \ \"acc_norm_stderr\": 0.04304979692464243\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.45394736842105265,\n \"acc_stderr\": 0.04051646342874143,\n\ \ \"acc_norm\": 0.45394736842105265,\n \"acc_norm_stderr\": 0.04051646342874143\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.41,\n\ \ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.4981132075471698,\n \"acc_stderr\": 0.030772653642075657,\n\ \ \"acc_norm\": 0.4981132075471698,\n \"acc_norm_stderr\": 0.030772653642075657\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4583333333333333,\n\ \ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.4583333333333333,\n\ \ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.3699421965317919,\n\ \ \"acc_stderr\": 0.03681229633394319,\n \"acc_norm\": 0.3699421965317919,\n\ \ \"acc_norm_stderr\": 0.03681229633394319\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.39148936170212767,\n \"acc_stderr\": 0.03190701242326812,\n\ \ \"acc_norm\": 0.39148936170212767,\n \"acc_norm_stderr\": 0.03190701242326812\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.21052631578947367,\n\ \ \"acc_stderr\": 0.03835153954399421,\n \"acc_norm\": 0.21052631578947367,\n\ \ \"acc_norm_stderr\": 0.03835153954399421\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2751322751322751,\n \"acc_stderr\": 0.023000086859068642,\n \"\ acc_norm\": 0.2751322751322751,\n \"acc_norm_stderr\": 0.023000086859068642\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.29365079365079366,\n\ \ \"acc_stderr\": 0.04073524322147126,\n \"acc_norm\": 0.29365079365079366,\n\ \ \"acc_norm_stderr\": 0.04073524322147126\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\": 0.43548387096774194,\n\ \ \"acc_stderr\": 0.02820622559150274,\n \"acc_norm\": 0.43548387096774194,\n\ \ \"acc_norm_stderr\": 0.02820622559150274\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.35467980295566504,\n \"acc_stderr\": 0.0336612448905145,\n\ \ \"acc_norm\": 0.35467980295566504,\n \"acc_norm_stderr\": 0.0336612448905145\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.46060606060606063,\n \"acc_stderr\": 0.03892207016552013,\n\ \ \"acc_norm\": 0.46060606060606063,\n \"acc_norm_stderr\": 0.03892207016552013\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.4898989898989899,\n \"acc_stderr\": 0.035616254886737454,\n \"\ acc_norm\": 0.4898989898989899,\n \"acc_norm_stderr\": 0.035616254886737454\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.6062176165803109,\n \"acc_stderr\": 0.035260770955482405,\n\ \ \"acc_norm\": 0.6062176165803109,\n \"acc_norm_stderr\": 0.035260770955482405\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.4282051282051282,\n \"acc_stderr\": 0.025088301454694834,\n\ \ \"acc_norm\": 0.4282051282051282,\n \"acc_norm_stderr\": 0.025088301454694834\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.33613445378151263,\n \"acc_stderr\": 0.030684737115135377,\n\ \ \"acc_norm\": 0.33613445378151263,\n \"acc_norm_stderr\": 0.030684737115135377\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.5504587155963303,\n \"acc_stderr\": 0.021327881417823363,\n \"\ acc_norm\": 0.5504587155963303,\n \"acc_norm_stderr\": 0.021327881417823363\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.3148148148148148,\n \"acc_stderr\": 0.0316746870682898,\n \"acc_norm\"\ : 0.3148148148148148,\n \"acc_norm_stderr\": 0.0316746870682898\n },\n\ \ \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.5245098039215687,\n\ \ \"acc_stderr\": 0.03505093194348798,\n \"acc_norm\": 0.5245098039215687,\n\ \ \"acc_norm_stderr\": 0.03505093194348798\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.569620253164557,\n \"acc_stderr\": 0.032230171959375976,\n\ \ \"acc_norm\": 0.569620253164557,\n \"acc_norm_stderr\": 0.032230171959375976\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.4798206278026906,\n\ \ \"acc_stderr\": 0.033530461674123,\n \"acc_norm\": 0.4798206278026906,\n\ \ \"acc_norm_stderr\": 0.033530461674123\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.46564885496183206,\n \"acc_stderr\": 0.04374928560599738,\n\ \ \"acc_norm\": 0.46564885496183206,\n \"acc_norm_stderr\": 0.04374928560599738\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.5785123966942148,\n \"acc_stderr\": 0.04507732278775088,\n \"\ acc_norm\": 0.5785123966942148,\n \"acc_norm_stderr\": 0.04507732278775088\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.4537037037037037,\n\ \ \"acc_stderr\": 0.048129173245368216,\n \"acc_norm\": 0.4537037037037037,\n\ \ \"acc_norm_stderr\": 0.048129173245368216\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.36809815950920244,\n \"acc_stderr\": 0.03789213935838396,\n\ \ \"acc_norm\": 0.36809815950920244,\n \"acc_norm_stderr\": 0.03789213935838396\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.04059867246952687,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.04059867246952687\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.49514563106796117,\n \"acc_stderr\": 0.049505043821289195,\n\ \ \"acc_norm\": 0.49514563106796117,\n \"acc_norm_stderr\": 0.049505043821289195\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.5299145299145299,\n\ \ \"acc_stderr\": 0.032697411068124425,\n \"acc_norm\": 0.5299145299145299,\n\ \ \"acc_norm_stderr\": 0.032697411068124425\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.598978288633461,\n\ \ \"acc_stderr\": 0.017526133150124572,\n \"acc_norm\": 0.598978288633461,\n\ \ \"acc_norm_stderr\": 0.017526133150124572\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.4508670520231214,\n \"acc_stderr\": 0.026788811931562764,\n\ \ \"acc_norm\": 0.4508670520231214,\n \"acc_norm_stderr\": 0.026788811931562764\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.01435591196476786,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.01435591196476786\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.028431095444176647,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.028431095444176647\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.48231511254019294,\n\ \ \"acc_stderr\": 0.02838032284907713,\n \"acc_norm\": 0.48231511254019294,\n\ \ \"acc_norm_stderr\": 0.02838032284907713\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.4660493827160494,\n \"acc_stderr\": 0.02775653525734767,\n\ \ \"acc_norm\": 0.4660493827160494,\n \"acc_norm_stderr\": 0.02775653525734767\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.29432624113475175,\n \"acc_stderr\": 0.027187127011503814,\n \ \ \"acc_norm\": 0.29432624113475175,\n \"acc_norm_stderr\": 0.027187127011503814\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3376792698826597,\n\ \ \"acc_stderr\": 0.012078563777145564,\n \"acc_norm\": 0.3376792698826597,\n\ \ \"acc_norm_stderr\": 0.012078563777145564\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.46691176470588236,\n \"acc_stderr\": 0.030306257722468314,\n\ \ \"acc_norm\": 0.46691176470588236,\n \"acc_norm_stderr\": 0.030306257722468314\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.39869281045751637,\n \"acc_stderr\": 0.019808281317449848,\n \ \ \"acc_norm\": 0.39869281045751637,\n \"acc_norm_stderr\": 0.019808281317449848\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.4636363636363636,\n\ \ \"acc_stderr\": 0.047764491623961985,\n \"acc_norm\": 0.4636363636363636,\n\ \ \"acc_norm_stderr\": 0.047764491623961985\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.4530612244897959,\n \"acc_stderr\": 0.03186785930004129,\n\ \ \"acc_norm\": 0.4530612244897959,\n \"acc_norm_stderr\": 0.03186785930004129\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.48756218905472637,\n\ \ \"acc_stderr\": 0.03534439848539579,\n \"acc_norm\": 0.48756218905472637,\n\ \ \"acc_norm_stderr\": 0.03534439848539579\n },\n \"harness\|hendrycksTest-us_foreign_policy\|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-virology\|5\": {\n \"acc\": 0.3795180722891566,\n\ \ \"acc_stderr\": 0.03777798822748018,\n \"acc_norm\": 0.3795180722891566,\n\ \ \"acc_norm_stderr\": 0.03777798822748018\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.6140350877192983,\n \"acc_stderr\": 0.03733756969066164,\n\ \ \"acc_norm\": 0.6140350877192983,\n \"acc_norm_stderr\": 0.03733756969066164\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2558139534883721,\n\ \ \"mc1_stderr\": 0.01527417621928336,\n \"mc2\": 0.40926286124406613,\n\ \ \"mc2_stderr\": 0.01393003126171617\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.01235894443163756\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.11751326762699014,\n \ \ \"acc_stderr\": 0.008870331256489986\n }\n}\n```" repo_url: https://huggingface.co/Charlie911/MultiLoRA-llama2-mmlu 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_09T20_19_51.603035 path: - '/details_harness\|arc:challenge\|25_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-09T20-19-51.603035.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|gsm8k\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hellaswag\|10_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T20-19-51.603035.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T20-19-51.603035.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '/details_harness\|winogrande\|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-09T20-19-51.603035.parquet' - config_name: results data_files: - split: 2024_02_09T20_19_51.603035 path: - results_2024-02-09T20-19-51.603035.parquet - split: latest path: - results_2024-02-09T20-19-51.603035.parquet --- # Dataset Card for Evaluation run of Charlie911/MultiLoRA-llama2-mmlu <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Charlie911/MultiLoRA-llama2-mmlu](https://huggingface.co/Charlie911/MultiLoRA-llama2-mmlu) 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_Charlie911__MultiLoRA-llama2-mmlu", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T20:19:51.603035](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__MultiLoRA-llama2-mmlu/blob/main/results_2024-02-09T20-19-51.603035.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": 0.42939450714393407, "acc_stderr": 0.03450029235435365, "acc_norm": 0.4336173195651683, "acc_norm_stderr": 0.03529727761229674, "mc1": 0.2558139534883721, "mc1_stderr": 0.01527417621928336, "mc2": 0.40926286124406613, "mc2_stderr": 0.01393003126171617 }, "harness\|arc:challenge\|25": { "acc": 0.47013651877133106, "acc_stderr": 0.0145853058400071, "acc_norm": 0.5221843003412969, "acc_norm_stderr": 0.01459700192707614 }, "harness\|hellaswag\|10": { "acc": 0.5821549492133041, "acc_stderr": 0.00492196413387402, "acc_norm": 0.7759410476000796, "acc_norm_stderr": 0.004161089244867776 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464243, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464243 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.45394736842105265, "acc_stderr": 0.04051646342874143, "acc_norm": 0.45394736842105265, "acc_norm_stderr": 0.04051646342874143 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4981132075471698, "acc_stderr": 0.030772653642075657, "acc_norm": 0.4981132075471698, "acc_norm_stderr": 0.030772653642075657 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4583333333333333, "acc_stderr": 0.04166666666666665, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.04166666666666665 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.3699421965317919, "acc_stderr": 0.03681229633394319, "acc_norm": 0.3699421965317919, "acc_norm_stderr": 0.03681229633394319 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364395, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364395 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.39148936170212767, "acc_stderr": 0.03190701242326812, "acc_norm": 0.39148936170212767, "acc_norm_stderr": 0.03190701242326812 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03835153954399421, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03835153954399421 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4689655172413793, "acc_stderr": 0.04158632762097828, "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2751322751322751, "acc_stderr": 0.023000086859068642, "acc_norm": 0.2751322751322751, "acc_norm_stderr": 0.023000086859068642 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147126, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147126 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.43548387096774194, "acc_stderr": 0.02820622559150274, "acc_norm": 0.43548387096774194, "acc_norm_stderr": 0.02820622559150274 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.35467980295566504, "acc_stderr": 0.0336612448905145, "acc_norm": 0.35467980295566504, "acc_norm_stderr": 0.0336612448905145 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.46060606060606063, "acc_stderr": 0.03892207016552013, "acc_norm": 0.46060606060606063, "acc_norm_stderr": 0.03892207016552013 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.4898989898989899, "acc_stderr": 0.035616254886737454, "acc_norm": 0.4898989898989899, "acc_norm_stderr": 0.035616254886737454 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6062176165803109, "acc_stderr": 0.035260770955482405, "acc_norm": 0.6062176165803109, "acc_norm_stderr": 0.035260770955482405 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4282051282051282, "acc_stderr": 0.025088301454694834, "acc_norm": 0.4282051282051282, "acc_norm_stderr": 0.025088301454694834 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.33613445378151263, "acc_stderr": 0.030684737115135377, "acc_norm": 0.33613445378151263, "acc_norm_stderr": 0.030684737115135377 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.5504587155963303, "acc_stderr": 0.021327881417823363, "acc_norm": 0.5504587155963303, "acc_norm_stderr": 0.021327881417823363 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.3148148148148148, "acc_stderr": 0.0316746870682898, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.0316746870682898 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5245098039215687, "acc_stderr": 0.03505093194348798, "acc_norm": 0.5245098039215687, "acc_norm_stderr": 0.03505093194348798 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.569620253164557, "acc_stderr": 0.032230171959375976, "acc_norm": 0.569620253164557, "acc_norm_stderr": 0.032230171959375976 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.4798206278026906, "acc_stderr": 0.033530461674123, "acc_norm": 0.4798206278026906, "acc_norm_stderr": 0.033530461674123 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.46564885496183206, "acc_stderr": 0.04374928560599738, "acc_norm": 0.46564885496183206, "acc_norm_stderr": 0.04374928560599738 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.5785123966942148, "acc_stderr": 0.04507732278775088, "acc_norm": 0.5785123966942148, "acc_norm_stderr": 0.04507732278775088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.4537037037037037, "acc_stderr": 0.048129173245368216, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.048129173245368216 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.36809815950920244, "acc_stderr": 0.03789213935838396, "acc_norm": 0.36809815950920244, "acc_norm_stderr": 0.03789213935838396 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.24107142857142858, "acc_stderr": 0.04059867246952687, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.04059867246952687 }, "harness\|hendrycksTest-management\|5": { "acc": 0.49514563106796117, "acc_stderr": 0.049505043821289195, "acc_norm": 0.49514563106796117, "acc_norm_stderr": 0.049505043821289195 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.5299145299145299, "acc_stderr": 0.032697411068124425, "acc_norm": 0.5299145299145299, "acc_norm_stderr": 0.032697411068124425 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.598978288633461, "acc_stderr": 0.017526133150124572, "acc_norm": 0.598978288633461, "acc_norm_stderr": 0.017526133150124572 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.4508670520231214, "acc_stderr": 0.026788811931562764, "acc_norm": 0.4508670520231214, "acc_norm_stderr": 0.026788811931562764 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2435754189944134, "acc_stderr": 0.01435591196476786, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.01435591196476786 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.028431095444176647, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.028431095444176647 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.48231511254019294, "acc_stderr": 0.02838032284907713, "acc_norm": 0.48231511254019294, "acc_norm_stderr": 0.02838032284907713 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.4660493827160494, "acc_stderr": 0.02775653525734767, "acc_norm": 0.4660493827160494, "acc_norm_stderr": 0.02775653525734767 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.29432624113475175, "acc_stderr": 0.027187127011503814, "acc_norm": 0.29432624113475175, "acc_norm_stderr": 0.027187127011503814 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3376792698826597, "acc_stderr": 0.012078563777145564, "acc_norm": 0.3376792698826597, "acc_norm_stderr": 0.012078563777145564 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.46691176470588236, "acc_stderr": 0.030306257722468314, "acc_norm": 0.46691176470588236, "acc_norm_stderr": 0.030306257722468314 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.39869281045751637, "acc_stderr": 0.019808281317449848, "acc_norm": 0.39869281045751637, "acc_norm_stderr": 0.019808281317449848 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.4636363636363636, "acc_stderr": 0.047764491623961985, "acc_norm": 0.4636363636363636, "acc_norm_stderr": 0.047764491623961985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.4530612244897959, "acc_stderr": 0.03186785930004129, "acc_norm": 0.4530612244897959, "acc_norm_stderr": 0.03186785930004129 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.48756218905472637, "acc_stderr": 0.03534439848539579, "acc_norm": 0.48756218905472637, "acc_norm_stderr": 0.03534439848539579 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3795180722891566, "acc_stderr": 0.03777798822748018, "acc_norm": 0.3795180722891566, "acc_norm_stderr": 0.03777798822748018 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6140350877192983, "acc_stderr": 0.03733756969066164, "acc_norm": 0.6140350877192983, "acc_norm_stderr": 0.03733756969066164 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2558139534883721, "mc1_stderr": 0.01527417621928336, "mc2": 0.40926286124406613, "mc2_stderr": 0.01393003126171617 }, "harness\|winogrande\|5": { "acc": 0.7379636937647988, "acc_stderr": 0.01235894443163756 }, "harness\|gsm8k\|5": { "acc": 0.11751326762699014, "acc_stderr": 0.008870331256489986 } } ``` ## 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]
gagan3012/arabic-xnli-pairwise	--- dataset_info: features: - name: labels sequence: int64 - name: sent1 sequence: string - name: sent2 sequence: string splits: - name: train num_bytes: 70811123 num_examples: 1 - name: test num_bytes: 850605 num_examples: 1 - name: validation num_bytes: 415074 num_examples: 1 download_size: 37859272 dataset_size: 72076802 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
BangumiBase/lycorisrecoil	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Lycoris Recoil This is the image base of bangumi Lycoris Recoil, we detected 31 characters, 2149 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 22 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 67 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 17 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 117 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 120 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 21 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 79 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 36 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 16 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 24 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 11 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 21 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 11 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 10 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 10 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 118 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 10 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 54 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 50 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 23 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 10 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 9 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 407 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 13 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 102 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 9 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 27 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 510 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 33 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 27 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| noise \| 165 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
Ti-Ma/TiMaGPT2-2019	--- license: other license_name: paracrawl-license license_link: LICENSE ---
DrNicefellow/Quality_WorryFree_GeneralQA_Chat_Dataset-v1	--- license: apache-2.0 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 37678642 num_examples: 21982 download_size: 16914098 dataset_size: 37678642 --- # Dr. Nicefollows's Worry Free General Chat Dataset v1 ## Overview This dataset contains high-quality general chat samples questions and answers. It is designed following the LIMA: Less Is More for Alignment principle from MetaAI: emphasizing the importance of quality over quantity in training data. Despite its modest size, the dataset's quality ensures its effectiveness in training and fine-tuning conversational AI models. In this version, each chat has one user query and assistant answer. In the next version, it will become a conversation of multiple rounds. ## Dataset Format The dataset is structured in the Vicuna 1.1 format, featuring one-round chats. This format is chosen for its compatibility with various conversational AI training paradigms and its efficiency in representing dialogues. ## Volume The dataset comprises a few thousand chat samples. Each sample has been carefully curated to ensure the highest quality, aligning with the LIMA principle. ## Licensing Our dataset is worry-free regarding proprietary issues, as it is not automatically generated by a proprietary chatbot. This dataset is released under the Apache License 2.0. This license allows for broad freedom in usage and modification, provided that proper credit is given and changes are documented. For full license terms, please refer to the LICENSE file. ## Use Case This dataset is ideal for training conversational AI models. It can help in developing chatbots or virtual assistants capable of handling a wide range of queries with high accuracy. To use the dataset for finetuning a model with Axolotl, simply add the following to the .yml file: datasets: - path: DrNicefellow/Quality_WorryFree_GeneralQA_Chat_Dataset-v1 - type: completion ## Feeling Generous? 😊 Eager to buy me a cup of 2$ coffe or iced tea?🍵☕ Sure, here is the link: [https://ko-fi.com/drnicefellow](https://ko-fi.com/drnicefellow). Please add a note on which one you want me to drink?
xiaxiaoqian/model	--- license: mit ---
jsqihui/detective-dataset-en	--- dataset_info: features: - name: prompt dtype: string - name: text dtype: string splits: - name: train num_bytes: 746174 num_examples: 74 download_size: 409863 dataset_size: 746174 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-v2-en-de	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 41900834 num_examples: 34234 download_size: 19737322 dataset_size: 41900834 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4	--- pretty_name: Evaluation run of Mikivis/gpt2-large-lora-stf4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Mikivis/gpt2-large-lora-stf4](https://huggingface.co/Mikivis/gpt2-large-lora-stf4)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T23:48:52.785657](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4/blob/main/results_2023-10-27T23-48-52.785657.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\": 0.003460570469798658,\n\ \ \"em_stderr\": 0.0006013962884271089,\n \"f1\": 0.07443372483221503,\n\ \ \"f1_stderr\": 0.0016782330994195233,\n \"acc\": 0.26795580110497236,\n\ \ \"acc_stderr\": 0.007008096716979156\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.003460570469798658,\n \"em_stderr\": 0.0006013962884271089,\n\ \ \"f1\": 0.07443372483221503,\n \"f1_stderr\": 0.0016782330994195233\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.5359116022099447,\n\ \ \"acc_stderr\": 0.014016193433958312\n }\n}\n```" repo_url: https://huggingface.co/Mikivis/gpt2-large-lora-stf4 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_12T03_05_07.244584 path: - '/details_harness\|arc:challenge\|25_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-12T03-05-07.244584.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T23_48_52.785657 path: - '/details_harness\|drop\|3_2023-10-27T23-48-52.785657.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-27T23-48-52.785657.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T23_48_52.785657 path: - '/details_harness\|gsm8k\|5_2023-10-27T23-48-52.785657.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-27T23-48-52.785657.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hellaswag\|10_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T03-05-07.244584.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T03_05_07.244584 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-12T03-05-07.244584.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T23_48_52.785657 path: - '/details_harness\|winogrande\|5_2023-10-27T23-48-52.785657.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-27T23-48-52.785657.parquet' - config_name: results data_files: - split: 2023_09_12T03_05_07.244584 path: - results_2023-09-12T03-05-07.244584.parquet - split: 2023_10_27T23_48_52.785657 path: - results_2023-10-27T23-48-52.785657.parquet - split: latest path: - results_2023-10-27T23-48-52.785657.parquet --- # Dataset Card for Evaluation run of Mikivis/gpt2-large-lora-stf4 ## Dataset Description - Homepage: - Repository: https://huggingface.co/Mikivis/gpt2-large-lora-stf4 - 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 [Mikivis/gpt2-large-lora-stf4](https://huggingface.co/Mikivis/gpt2-large-lora-stf4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T23:48:52.785657](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4/blob/main/results_2023-10-27T23-48-52.785657.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": 0.003460570469798658, "em_stderr": 0.0006013962884271089, "f1": 0.07443372483221503, "f1_stderr": 0.0016782330994195233, "acc": 0.26795580110497236, "acc_stderr": 0.007008096716979156 }, "harness\|drop\|3": { "em": 0.003460570469798658, "em_stderr": 0.0006013962884271089, "f1": 0.07443372483221503, "f1_stderr": 0.0016782330994195233 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.5359116022099447, "acc_stderr": 0.014016193433958312 } } ``` ### 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]
MatsuoDochiai/Mae	--- license: openrail ---
deepharborAI/Hindi-Niband	--- license: mit task_categories: - text-generation - table-question-answering - summarization language: - hi pretty_name: 'Niband ' size_categories: - 100M<n<1B --- ### Dataset Name: Hindi- Niband (Massive Hindi language Text Dataset) #### Dataset Overview This dataset is a comprehensive collection of text data consisting of more than 10 billion tokens. It encompasses a wide range of sources, including Wikipedia articles, news articles, email transcripts, and generated prompt text. Specific Hindi language data columns have been extracted from the CulturaX dataset, which is a large, cleaned, and multilingual dataset for large language models. We acknowledge and cite the CulturaX dataset using the provided citation. #### Data Sources 1. Wikipedia Articles: A large corpus of text extracted from Wikipedia articles covering various topics and domains. 2. News Articles: Textual data sourced from news articles from diverse sources and regions. 3. Email Transcripts: Transcripts of email communications, providing insights into natural language usage in electronic correspondence. 4. Prompt Text Generation: Text generated from prompts or prompts used to generate text, contributing to the dataset's diversity and complexity. 5. Hindi Data from CulturaX Dataset: Specific Hindi language data columns have been extracted from the CulturaX dataset, which is a large, cleaned, and multilingual dataset for large language models. #### Potential Uses - Training and evaluating natural language generation models in the Hindi language domain. - Exploring the capabilities of models in narrative generation tasks. - Conducting research on narrative understanding and generation in Hindi. - Analyzing sentiment and opinion mining in Hindi text data. - Building chatbots or virtual assistants capable of interacting in Hindi. - Creating educational resources for teaching Hindi language and literature. - Developing machine translation systems for translating between Hindi and other languages. - Studying cross-lingual transfer learning techniques for improving natural language processing tasks in Hindi. #### Importance for Indian Native Languages :- This dataset can be crucial for the training of LLM (Large Language Model) models and aiming to explore the capabilities of those natural language generation models in Hindi. It serves as a foundation for training and evaluating models capable of producing coherent and contextually relevant narratives or explanations. Additionally, this dataset aligns with our commitment to promoting Indian native languages on a global scale. We recognize the limited availability of such datasets as a major challenge for innovation within the local Indian community. As part of our contribution to the Indian open-source community, we are planning to release a very large database covering various Indian native languages. This initiative aims to empower researchers, practitioners, and developers to explore and innovate in Indian language processing and generation tasks. #### Citation If you use this dataset in your research or applications, please consider citing the CulturaX dataset using the provided citation. We acknowledge and cite the CulturaX dataset using the following citation: ``` @misc{nguyen2023culturax, title={CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages}, author={Thuat Nguyen and Chien Van Nguyen and Viet Dac Lai and Hieu Man and Nghia Trung Ngo and Franck Dernoncourt and Ryan A. Rossi and Thien Huu Nguyen}, year={2023}, eprint={2309.09400}, archivePrefix={arXiv}, primaryClass={cs.CL}. ``` Additionally, the dataset includes news article data, and we acknowledge and cite the source of this data using the following citations: ``` @inproceedings{see-etal-2017-get, title = "Get To The Point: Summarization with Pointer-Generator Networks", author = "See, Abigail and Liu, Peter J. and Manning, Christopher D.", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-1099", doi = "10.18653/v1/P17-1099", pages = "1073--1083", abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",} @inproceedings{DBLP:conf/nips/HermannKGEKSB15, author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom}, title={Teaching Machines to Read and Comprehend}, year={2015}, cdate={1420070400000}, pages={1693-1701}, url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend}, booktitle={NIPS}, crossref={conf/nips/2015} } ``` #### License Please refer to the licensing terms specified by the dataset creators. #### Disclaimer The views expressed in the dataset do not necessarily reflect the views of the dataset creators or contributors. Users are advised to use the data responsibly and in accordance with ethical guidelines. This dataset card provides an overview of the massive multilingual text dataset, highlighting its sources, potential uses, citation, and disclaimer.
ravithejads/ms_marco_hi	--- dataset_info: features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string - name: query_hi dtype: string - name: answers_hi dtype: string - name: passage_text_hi sequence: string splits: - name: test num_bytes: 129079041 num_examples: 9650 download_size: 49278186 dataset_size: 129079041 configs: - config_name: default data_files: - split: test path: data/test-* ---
FranzderPapst/squad_x_boolq	--- license: mit language: - en task_categories: - text-classification pretty_name: warrgalbhalble size_categories: - 1K<n<10K --- # ABOUT Wanted to train a model to classify question, if they are open ore boolean. So I merged SQuAD with BoolQ, the dataset contains 5000 question of each dataset, labeled with "true" (the boolean question) and with "false" (the open questions). Didn't add questions that don't fall into these categories. May be a flaw, we'll see:). For some reason the dataset viewer isn't working, sorry for that one, but here's a snippet of the json structure: { "question": "are there fiber optic cables under the ocean", "type": "true" }, { "question": "are dollar general and dollar tree owned by the same company", "type": "true" },
Mr-aio/All-Isa-AF	--- language: - en size_categories: - 1K<n<10K ---
NeuralNovel/Unsloth-DPO	--- language: - en license: apache-2.0 --- <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Data Card</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> <style> body { font-family: 'Quicksand', sans-serif; background-color: #1A202C; color: #D8DEE9; margin: 0; padding: 0; /* Remove default padding / font-size: 26px; background: linear-gradient(135deg, #2E3440 0%, #1A202C 100%); } p { padding-left: 10px } .container { width: 100%; margin: auto; background-color: rgb(255 255 255 / 1%); padding: 20px 30px 40px; / Add padding below the image only / padding-right: 32px; border-radius: 12px; box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2); backdrop-filter: blur(10px); border: 1px solid rgba(255, 255, 255, 0.05); background-color: rgb(0 0 0 / 75%) !important; } .header h1 { font-size: 28px; color: #fff; / White text color / margin: 0; text-shadow: -1px -1px 0 #000, 1px -1px 0 #000, -1px 1px 0 #000, 1px 1px 0 #000; / Black outline / } .header { display: flex; align-items: center; justify-content: space-between; gap: 20px; } img { border-radius: 10px 10px 0 0!important; padding-left: 0px !important; } .header h1 { font-size: 28px; color: #ECEFF4; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); } .info { background-color: rgba(255, 255, 255, 0.05); color: #AEBAC7; border-radius: 12px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.2); font-size: 14px; line-height: 1.6; margin-left: 5px; overflow-x: auto; margin-top: 20px; / Adjusted margin / border: 1px solid rgba(255, 255, 255, 0.05); transition: background-color 0.6s ease; / Smooth transition over 0.5 seconds / } .info:hover { } .info img { width: 100%; border-radius: 10px 10px 0 0; margin-top: -20px; / Negative margin to overlap container margin */ } a { color: #88C0D0; text-decoration: none; transition: color 0.3s ease; position: relative; } a:hover { color: #A3BE8C; text-decoration: none; } a::before { content: ''; position: absolute; width: 100%; height: 2px; bottom: 0; left: 0; background-color: #A3BE8C; visibility: hidden; transform: scaleX(0); transition: all 0.3s ease-in-out; } a:hover::before { visibility: visible; transform: scaleX(1); } .button { display: inline-block; background-color: #5E81AC; color: #E5E9F0; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; transition: background-color 0.3s ease; } .button:hover { background-color: #81A1C1; } .hf-sanitized.hf-sanitized-oJB5trHYB93-j8lDfGQn3 .container { } </style> </head> <body> <div class="container"> <div class="header"> <h1>Unsloth-DPO</h1> </div> <div class="info"> <img src="https://i.ibb.co/hY42ZY7/OIG4-8.jpg" style="border-radius: 10px;"> <p><strong>Creator:</strong> <a href="https://huggingface.co/NeuralNovel" target="_blank">NeuralNovel</a></p> <p><strong>Community Organization:</strong> <a href="https://huggingface.co/ConvexAI" target="_blank">ConvexAI</a></p> <p><strong>Discord:</strong> <a href="https://discord.gg/rJXGjmxqzS" target="_blank">Join us on Discord</a></p> <p><strong>Special Thanks: <a href ="https://unsloth.ai/" target="_blank"> Unsloth.ai</strong></a></p> </head> <body> <div> <div> <p><strong>About Neural-DPO:</strong> The Unsloth-DPO dataset, inspired by orca_dpo_pairs. This dataset features questions and answers pairs, with a direct focus on Unsloth.ai.</p> <p><strong>Source Data:</strong></p> <ul> <li>orca_dpo_pairs (Inspiration)</li> <li>Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL</li> <li>unsloth.ai/blog/mistral-benchmark</li> </ul> <p><strong>Phrases Removed:</strong></p> <p>To enhance the dataset's coherence and relevance across varied contexts, certain phrases have been selectively omitted.</p> <ul> <li>Couldn't help but</li> <li>Can't resist</li> <li>I'm sorry, but</li> <li>As an AI</li> <li>However, it is important to</li> <li>Cannot provide</li> <li>And others</li> </ul> </div> </div> </body>
gallantVN/en_vi_DPO	--- license: apache-2.0 task_categories: - translation - reinforcement-learning size_categories: - 10K<n<100K ---
autoevaluate/autoeval-eval-lener_br-lener_br-2a71c5-1777061681	--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: pierreguillou/ner-bert-large-cased-pt-lenerbr metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: validation 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: pierreguillou/ner-bert-large-cased-pt-lenerbr * Dataset: lener_br * Config: lener_br * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.
ktmeng/mec	--- license: mit ---
ola13/small-the_pile	--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: perplexity_score dtype: float64 - name: pile_set_name dtype: string splits: - name: train num_bytes: 606056668 num_examples: 100000 download_size: 328667964 dataset_size: 606056668 --- # Dataset Card for "small-the_pile" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/Food101_test_google_flan_t5_xl_mode_A_ns_25250	--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices num_bytes: 10610673 num_examples: 25250 download_size: 1146498 dataset_size: 10610673 --- # Dataset Card for "Food101_test_google_flan_t5_xl_mode_A_ns_25250" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jkv53/13F_Reports_with_labels	--- dataset_info: features: - name: title dtype: string - name: body dtype: string - name: label dtype: string splits: - name: train num_bytes: 12642773 num_examples: 1113 download_size: 3334911 dataset_size: 12642773 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "13F_Reports_with_labels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kenanjeff/ComVG	--- license: creativeml-openrail-m task_categories: - zero-shot-classification tags: - code size_categories: - 1K<n<10K --- Compositional Visual Genome (ComVG) <br/> ComVG benchmark aims to test vision-language models ability in text-to-image retrieval. <br/> We selected 542 high-quality images from Visual Genome and created 5400 datapoints in ComVG. <br/> Each datapoint contains a postive and negative image. The negative image is a mutated variant with singular discrepancies in subject, object, or predicate.<br/> For more details on creation process, please refer: https://arxiv.org/abs/2211.13854
open-llm-leaderboard/details_922CA__Silicon-Monika-7b	--- pretty_name: Evaluation run of 922CA/Silicon-Monika-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [922CA/Silicon-Monika-7b](https://huggingface.co/922CA/Silicon-Monika-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_922CA__Silicon-Monika-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-29T12:04:26.776279](https://huggingface.co/datasets/open-llm-leaderboard/details_922CA__Silicon-Monika-7b/blob/main/results_2024-02-29T12-04-26.776279.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\": 0.6283372065747487,\n\ \ \"acc_stderr\": 0.03245805597450051,\n \"acc_norm\": 0.6301488013494124,\n\ \ \"acc_norm_stderr\": 0.03311159741284949,\n \"mc1\": 0.3525091799265606,\n\ \ \"mc1_stderr\": 0.016724646380756544,\n \"mc2\": 0.5214295545813499,\n\ \ \"mc2_stderr\": 0.015004393759780037\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5972696245733788,\n \"acc_stderr\": 0.014332236306790152,\n\ \ \"acc_norm\": 0.6313993174061433,\n \"acc_norm_stderr\": 0.014097810678042194\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6231826329416451,\n\ \ \"acc_stderr\": 0.004835981632401601,\n \"acc_norm\": 0.8264289982075284,\n\ \ \"acc_norm_stderr\": 0.003779661224651475\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.53,\n\ \ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\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\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\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\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6127167630057804,\n\ \ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n\ \ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663434,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663434\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n\ \ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155243,\n \"\ acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155243\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\": 0.7612903225806451,\n\ \ \"acc_stderr\": 0.024251071262208837,\n \"acc_norm\": 0.7612903225806451,\n\ \ \"acc_norm_stderr\": 0.024251071262208837\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.5024630541871922,\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\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\ acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396993,\n\ \ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396993\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871923,\n \ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871923\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135363,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135363\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8165137614678899,\n \"acc_stderr\": 0.016595259710399306,\n \"\ acc_norm\": 0.8165137614678899,\n \"acc_norm_stderr\": 0.016595259710399306\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\ acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676177,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676177\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\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\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\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\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8290598290598291,\n\ \ \"acc_stderr\": 0.024662496845209818,\n \"acc_norm\": 0.8290598290598291,\n\ \ \"acc_norm_stderr\": 0.024662496845209818\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8084291187739464,\n\ \ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\ \ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\ \ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.27262569832402234,\n\ \ \"acc_stderr\": 0.014893391735249612,\n \"acc_norm\": 0.27262569832402234,\n\ \ \"acc_norm_stderr\": 0.014893391735249612\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.02633661346904663,\n\ \ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.02633661346904663\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6975308641975309,\n \"acc_stderr\": 0.025557653981868062,\n\ \ \"acc_norm\": 0.6975308641975309,\n \"acc_norm_stderr\": 0.025557653981868062\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4517601043024772,\n\ \ \"acc_stderr\": 0.012710662233660245,\n \"acc_norm\": 0.4517601043024772,\n\ \ \"acc_norm_stderr\": 0.012710662233660245\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6580882352941176,\n \"acc_stderr\": 0.02881472242225419,\n\ \ \"acc_norm\": 0.6580882352941176,\n \"acc_norm_stderr\": 0.02881472242225419\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6372549019607843,\n \"acc_stderr\": 0.019450768432505514,\n \ \ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.019450768432505514\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\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\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835816,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835816\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3525091799265606,\n\ \ \"mc1_stderr\": 0.016724646380756544,\n \"mc2\": 0.5214295545813499,\n\ \ \"mc2_stderr\": 0.015004393759780037\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7821625887924231,\n \"acc_stderr\": 0.011601066079939324\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6050037907505686,\n \ \ \"acc_stderr\": 0.013465354969973198\n }\n}\n```" repo_url: https://huggingface.co/922CA/Silicon-Monika-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_02_29T12_04_26.776279 path: - '/details_harness\|arc:challenge\|25_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-29T12-04-26.776279.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|gsm8k\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hellaswag\|10_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T12-04-26.776279.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-29T12-04-26.776279.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '/details_harness\|winogrande\|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-29T12-04-26.776279.parquet' - config_name: results data_files: - split: 2024_02_29T12_04_26.776279 path: - results_2024-02-29T12-04-26.776279.parquet - split: latest path: - results_2024-02-29T12-04-26.776279.parquet --- # Dataset Card for Evaluation run of 922CA/Silicon-Monika-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [922CA/Silicon-Monika-7b](https://huggingface.co/922CA/Silicon-Monika-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_922CA__Silicon-Monika-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-29T12:04:26.776279](https://huggingface.co/datasets/open-llm-leaderboard/details_922CA__Silicon-Monika-7b/blob/main/results_2024-02-29T12-04-26.776279.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": 0.6283372065747487, "acc_stderr": 0.03245805597450051, "acc_norm": 0.6301488013494124, "acc_norm_stderr": 0.03311159741284949, "mc1": 0.3525091799265606, "mc1_stderr": 0.016724646380756544, "mc2": 0.5214295545813499, "mc2_stderr": 0.015004393759780037 }, "harness\|arc:challenge\|25": { "acc": 0.5972696245733788, "acc_stderr": 0.014332236306790152, "acc_norm": 0.6313993174061433, "acc_norm_stderr": 0.014097810678042194 }, "harness\|hellaswag\|10": { "acc": 0.6231826329416451, "acc_stderr": 0.004835981632401601, "acc_norm": 0.8264289982075284, "acc_norm_stderr": 0.003779661224651475 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "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": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "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": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663434, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663434 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.041227371113703316, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.041227371113703316 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155243, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155243 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7612903225806451, "acc_stderr": 0.024251071262208837, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.024251071262208837 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5024630541871922, "acc_stderr": 0.03517945038691063, "acc_norm": 0.5024630541871922, "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": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217487, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217487 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8860103626943006, "acc_stderr": 0.022935144053919443, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6384615384615384, "acc_stderr": 0.024359581465396993, "acc_norm": 0.6384615384615384, "acc_norm_stderr": 0.024359581465396993 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.027840811495871923, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.027840811495871923 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135363, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135363 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8165137614678899, "acc_stderr": 0.016595259710399306, "acc_norm": 0.8165137614678899, "acc_norm_stderr": 0.016595259710399306 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676177, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676177 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "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": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8290598290598291, "acc_stderr": 0.024662496845209818, "acc_norm": 0.8290598290598291, "acc_norm_stderr": 0.024662496845209818 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8084291187739464, "acc_stderr": 0.014072859310451949, "acc_norm": 0.8084291187739464, "acc_norm_stderr": 0.014072859310451949 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7109826589595376, "acc_stderr": 0.02440517393578323, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.02440517393578323 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.27262569832402234, "acc_stderr": 0.014893391735249612, "acc_norm": 0.27262569832402234, "acc_norm_stderr": 0.014893391735249612 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.696078431372549, "acc_stderr": 0.02633661346904663, "acc_norm": 0.696078431372549, "acc_norm_stderr": 0.02633661346904663 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.02549425935069491, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.02549425935069491 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6975308641975309, "acc_stderr": 0.025557653981868062, "acc_norm": 0.6975308641975309, "acc_norm_stderr": 0.025557653981868062 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4645390070921986, "acc_stderr": 0.02975238965742705, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.02975238965742705 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4517601043024772, "acc_stderr": 0.012710662233660245, "acc_norm": 0.4517601043024772, "acc_norm_stderr": 0.012710662233660245 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6580882352941176, "acc_stderr": 0.02881472242225419, "acc_norm": 0.6580882352941176, "acc_norm_stderr": 0.02881472242225419 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6372549019607843, "acc_stderr": 0.019450768432505514, "acc_norm": 0.6372549019607843, "acc_norm_stderr": 0.019450768432505514 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "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": 0.8507462686567164, "acc_stderr": 0.02519692987482706, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482706 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835816, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835816 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3525091799265606, "mc1_stderr": 0.016724646380756544, "mc2": 0.5214295545813499, "mc2_stderr": 0.015004393759780037 }, "harness\|winogrande\|5": { "acc": 0.7821625887924231, "acc_stderr": 0.011601066079939324 }, "harness\|gsm8k\|5": { "acc": 0.6050037907505686, "acc_stderr": 0.013465354969973198 } } ``` ## 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]
chats-bug/multiple-subject-gen	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: subject_lines dtype: string - name: text dtype: string splits: - name: train num_bytes: 78493229 num_examples: 59489 - name: test num_bytes: 4030472 num_examples: 3132 download_size: 10833380 dataset_size: 82523701 --- # Dataset Card for "multiple-subject-gen" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
fewshot-goes-multilingual/cs_facebook-comments	--- annotations_creators: - found language: - cs language_creators: - found license: - cc-by-nc-sa-3.0 multilinguality: - monolingual pretty_name: Czech Facebook comments size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for Czech Facebook comments ## Dataset Description The dataset contains user comments from Facebook. Each comment contains text, sentiment (positive/negative/neutral). The dataset has in total (train+validation+test) 6,600 reviews. The data is balanced. ## Dataset Features Each sample contains: - `comment_id`: unique string identifier of the comment. - `sentiment_str`: string representation of the rating - "pozitivní" / "neutrální" / "negativní" - `sentiment_int`: integer representation of the rating (1=positive, 0=neutral, -1=negative) - `comment`: the string of the comment ## Dataset Source The data is a processed adaptation of [Facebook CZ Corpus](https://liks.fav.zcu.cz/sentiment/). This adaptation is label-balanced.
GEM/wiki_cat_sum	--- annotations_creators: - automatically-created language_creators: - unknown language: - en license: - cc-by-sa-3.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - summarization task_ids: [] pretty_name: wiki_cat_sum --- # Dataset Card for GEM/wiki_cat_sum ## Dataset Description - Homepage: https://github.com/lauhaide/WikiCatSum - Repository: https://datashare.ed.ac.uk/handle/10283/3368 - Paper: https://arxiv.org/abs/1906.04687 - Leaderboard: N/A - Point of Contact: Laura Perez-Beltrachini ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/wiki_cat_sum). ### Dataset Summary WikiCatSum is an English summarization dataset in three domains: animals, companies, and film. It provides multiple paragraphs of text paired with a summary of the paragraphs. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/wiki_cat_sum') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/wiki_cat_sum). #### website [Github](https://github.com/lauhaide/WikiCatSum) #### paper [Arxiv](https://arxiv.org/abs/1906.04687) #### authors Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Github](https://github.com/lauhaide/WikiCatSum) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Website](https://datashare.ed.ac.uk/handle/10283/3368) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [Arxiv](https://arxiv.org/abs/1906.04687) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> ``` @inproceedings{perez-beltrachini-etal-2019-generating, title = "Generating Summaries with Topic Templates and Structured Convolutional Decoders", author = "Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1504", doi = "10.18653/v1/P19-1504", } ``` #### Contact Name <!-- quick --> <!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> Laura Perez-Beltrachini #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> lperez@ed.ac.uk #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> no ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> no #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `English` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> Research on multi-document abstractive summarisation. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Summarization #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `industry`, `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Google Cloud Platform, University of Edinburgh #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain) #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> Google Cloud Platform, European Research Council #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Ronald Cardenas (University of Edinburgh) Laura Perez-Beltrachini (University of Edinburgh) ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> - `id`: ID of the data example - `title`: Is the Wikipedia article's title - `paragraphs`: Is the ranked list of paragraphs from the set of crawled texts - `summary`: Is constituted by a list of sentences together with their corresponding topic label #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> This is a truncated example from the animal setting: ``` {'gem_id': 'animal-train-1', 'gem_parent_id': 'animal-train-1', 'id': '2652', 'paragraphs': ["lytrosis (hulst) of louisiana vernon antoine brou jr. 2005. southern lepidopterists' news, 27: 7 ., ..."], 'references': ['lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.'], 'summary': {'text': ['lytrosis unitaria , the common lytrosis moth , is a species of moth of the geometridae family .', 'it is found in north america , including arkansas , georgia , iowa , massachusetts , new hampshire , new jersey , new york , north carolina , ohio , oklahoma , ontario , pennsylvania , south carolina , tennessee , texas , virginia , west virginia and wisconsin .', 'the wingspan is about 50 mm .', 'the larvae feed on rosa , crataegus , amelanchier , acer , quercus and viburnum species . '], 'topic': [29, 20, 9, 8]}, 'target': 'lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.', 'title': 'lytrosis unitaria'} ``` #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> Nb of instances in train/valid/test are 50,938/2,855/2,831 #### Splitting Criteria <!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. --> <!-- scope: microscope --> The data was split i.i.d., i.e. uniformly split into training, validation, and test datasets. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> Evaluation of models' performance on noisy (document, summary) pairs and long inputs. Evaluate models' capabilities to generalise and mitigate biases. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> no #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> no #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> Capabilities to generalise, mitigate biases, factual correctness. ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `annotations added` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> We provide topic labels for summary sentences. #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> no ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> - [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198) - [Generating Summaries with Topic Templates and Structured Convolutional Decoders](https://arxiv.org/abs/1906.04687) - [Noisy Self-Knowledge Distillation for Text Summarization](https://arxiv.org/abs/2009.07032) And all references in these papers. ## Previous Results ### Previous Results #### Measured Model Abilities <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: telescope --> Capabilities to generalise, mitigate biases, factual correctness. #### Metrics <!-- info: What metrics are typically used for this task? --> <!-- scope: periscope --> `ROUGE`, `BERT-Score`, `MoverScore`, `Other: Other Metrics` #### Other Metrics <!-- info: Definitions of other metrics --> <!-- scope: periscope --> - Abstract/Copy - Factual accuracy based on the score of (Goodrich et al., 2019) and the relation extraction system of (Sorokin and Gurevych, 2017). #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> Human based are Question Answering and Ranking (Content, Fluency and Repetition) #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> Those listed above. #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> Generating Summaries with Topic Templates and Structured Convolutional Decoders https://arxiv.org/abs/1906.04687 Noisy Self-Knowledge Distillation for Text Summarization https://arxiv.org/abs/2009.07032 ## Dataset Curation ### Original Curation #### Original Curation Rationale <!-- info: Original curation rationale --> <!-- scope: telescope --> The dataset is a subset of the WikiSum (Liu et al., 2018) dataset focusing on summaries of entities in three domains (Film, Company, and Animal). It is multi-document summarisation where input-output pairs for each example entity are created as follows. The input is a set of paragraphs collected from i) documents in the Reference section of the entity's Wikipedia page plus ii) documents collected from the top ten search results after querying Google search engine with the entity name. The output summary is the Wikipedia abstract for the entity. #### Communicative Goal <!-- info: What was the communicative goal? --> <!-- scope: periscope --> Generate descriptive summaries with specific domains, where certain topics are discussed and generally in specific orders. #### Sourced from Different Sources <!-- info: Is the dataset aggregated from different data sources? --> <!-- scope: telescope --> yes #### Source Details <!-- info: List the sources (one per line) --> <!-- scope: periscope --> WikiSum (Liu et al., 2018) ### Language Data #### How was Language Data Obtained? <!-- info: How was the language data obtained? --> <!-- scope: telescope --> `Other` #### Topics Covered <!-- info: Does the language in the dataset focus on specific topics? How would you describe them? --> <!-- scope: periscope --> The dataset and task focuses on summaries for entities in three domains: Company, Film, and Animal. #### Data Validation <!-- info: Was the text validated by a different worker or a data curator? --> <!-- scope: telescope --> not validated #### Data Preprocessing <!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) --> <!-- scope: microscope --> Summary sentences are associated with a topic label. There is a topic model for each domain. #### Was Data Filtered? <!-- info: Were text instances selected or filtered? --> <!-- scope: telescope --> not filtered ### Structured Annotations #### Additional Annotations? <!-- quick --> <!-- info: Does the dataset have additional annotations for each instance? --> <!-- scope: telescope --> automatically created #### Annotation Service? <!-- info: Was an annotation service used? --> <!-- scope: telescope --> no #### Annotation Values <!-- info: Purpose and values for each annotation --> <!-- scope: microscope --> Each summary sentences was annotated with a topic label. There is a topic model for each of the three domains. This was used to guide a hierarchical decoder. #### Any Quality Control? <!-- info: Quality control measures? --> <!-- scope: telescope --> validated by data curators #### Quality Control Details <!-- info: Describe the quality control measures that were taken. --> <!-- scope: microscope --> Manual inspection of a sample of topics assigned to sentences. The number of topics was selected based on the performance of the summarisation model. ### Consent #### Any Consent Policy? <!-- info: Was there a consent policy involved when gathering the data? --> <!-- scope: telescope --> no #### Justification for Using the Data <!-- info: If not, what is the justification for reusing the data? --> <!-- scope: microscope --> The dataset is base on Wikipedia and referenced and retrieved documents crawled from the Web. ### Private Identifying Information (PII) #### Contains PII? <!-- quick --> <!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? --> <!-- scope: telescope --> unlikely #### Any PII Identification? <!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? --> <!-- scope: periscope --> no identification ### Maintenance #### Any Maintenance Plan? <!-- info: Does the original dataset have a maintenance plan? --> <!-- scope: telescope --> no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> yes #### Links and Summaries of Analysis Work <!-- info: Provide links to and summaries of works analyzing these biases. --> <!-- scope: microscope --> This dataset is based on Wikipedia and thus biases analysis on other Wikipedia-based datasets are potentially true for WikiCatSum. For instance, see analysis for the ToTTo dataset here [1]. [1] Automatic Construction of Evaluation Suites for Natural Language Generation Datasets https://openreview.net/forum?id=CSi1eu_2q96 ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `public domain` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `public domain` ### Known Technical Limitations
Amitnaik1718/indian_food_images	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': burger '1': butter_naan '2': chai '3': chapati '4': chole_bhature '5': dal_makhani '6': dhokla '7': fried_rice '8': idli '9': jalebi '10': kaathi_rolls '11': kadai_paneer '12': kulfi '13': masala_dosa '14': momos '15': paani_puri '16': pakode '17': pav_bhaji '18': pizza '19': samosa splits: - name: train num_bytes: 1269067280.1594334 num_examples: 5328 - name: test num_bytes: 229322892.3925666 num_examples: 941 download_size: 1601553689 dataset_size: 1498390172.552 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
umesh16071973/HRMS_training_Dataset	--- license: apache-2.0 ---
allandclive/Luganda_news_articles	--- task_categories: - text2text-generation - text-generation language: - lg size_categories: - 10K<n<100K --- # Luganda News Articles Luganda (lug) is one of the most spoken languages in Uganda. Scrapped from https://www.bukedde.co.ug/ & https://gambuuze.ug/
davanstrien/satclip	--- tags: - geospatial pretty_name: S2-100K --- # Dataset Card for S2-100K <!-- Provide a quick summary of the dataset. --> > The S2-100K dataset is a dataset of 100,000 multi-spectral satellite images sampled from Sentinel-2 via the Microsoft Planetary Computer. Copernicus Sentinel data is captured between Jan 1, 2021 and May 17, 2023. The dataset is sampled approximately uniformly over landmass and only includes images without cloud coverage. The dataset is available for research purposes only. If you use the dataset, please cite our paper. More information on the dataset can be found in our paper. See this [GitHub repo](https://github.com/microsoft/satclip/) for more details. ## Dataset Details ### Dataset Description > SatCLIP trains location and image encoders via contrastive learning, by matching images to their corresponding locations. This is analogous to the CLIP approach, which matches images to their corresponding text. > Through this process, the location encoder learns characteristics of a location, as represented by satellite imagery. - 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. --> To download the dataset you can use the `huggingface_hub` library. ```python from huggingface_hub import snapshot_download snapshot_download("davanstrien/satclip", local_dir='.', repo_type='dataset') ``` Alternatively you can run ```bash # Make sure you have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/datasets/davanstrien/satclip ``` To extract the images you can run the following command. ```bash ls image/.tar.xz \|xargs -n1 tar -xzf ``` ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX:* ``` @article{klemmer2023satclip, title={SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery}, author={Klemmer, Konstantin and Rolf, Esther and Robinson, Caleb and Mackey, Lester and Ru{\ss}wurm, Marc}, journal={arXiv preprint arXiv:2311.17179}, year={2023} } ```
Locutusque/hyperion-v3.0	--- license: apache-2.0 dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: float64 - name: source dtype: string splits: - name: train num_bytes: 3210068995.811935 num_examples: 1665372 download_size: 1497036692 dataset_size: 3210068995.811935 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering - text-generation language: - en size_categories: - 1M<n<10M --- Hyperion-3.0 has significantly improved performance over its predecessors. "I found that having more code datasets than general purpose datasets ironically decreases performance in both coding and general tasks." Data sources: - OpenOrca/SlimOrca - cognitivecomputations/dolphin (300k examples) - microsoft/orca-math-word-problems-200k (60k examples) - glaiveai/glaive-code-assistant - Vezora/Tested-22k-Python-Alpaca - Unnatural Instructions - BI55/MedText - LDJnr/Pure-Dove - Various domain-specific datasets by Camel - teknium/GPTeacher-General-Instruct - WizardLM Evol Instruct 70K and 140K - Various chat log datasets by Collective Cognition - totally-not-an-llm/EverythingLM-data-V3 - Crystalcareai/alpaca-gpt4-COT - m-a-p/Code-Feedback - Various medical datasets by CogStack - jondurbin/airoboros-3.2 - garage-bAInd/Open-Platypus - Lmsys chat 1M - GPT-4 Generations only - FuseAI/FuseChat-Mixture - abacusai/SystemChat - Locutusque/ToM-sharegpt4 - Locutusque/arc-cot
thesven/bengali-ai-train-set-tiny	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: valid num_bytes: 961362832 num_examples: 1000 - name: train num_bytes: 9612150048 num_examples: 10000 download_size: 1670313269 dataset_size: 10573512880 --- # Dataset Card for "bengali-ai-train-set-tiny" # Dataset Description - Homepage: [OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking](https://arxiv.org/abs/2305.09688) - Paper: [OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking](https://arxiv.org/abs/2305.09688) ### Whisper Model Information - Model Homepage: [openai/whisper-tiny on Hugging Face](https://huggingface.co/openai/whisper-tiny) - Model Paper: [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) ## Dataset Summary This dataset is designed to help finetune the `openai/whisper-tiny` model with additional information in the Bengali language. It consists of an additional 11,000 labeled audio samples from the OOD-Speech dataset, specifically designed for out-of-distribution benchmarking in Bengali. ## Supported Tasks and Leaderboards The primary task supported by this dataset is automatic speech recognition (ASR) in the Bengali language, specifically for finetuning the `openai/whisper-tiny` model. ## Languages The dataset is in Bengali. ## Dataset Structure ### Data Instances Each instance in the dataset consists of an audio sample in Bengali along with its corresponding transcription. ### Data Fields - `audio`: The audio sample in Bengali. - `transcription`: The corresponding transcription of the audio sample in Bengali. ### Data Splits The dataset is split into training and validation sets. The training set consists of 10,000 samples, and the validation set consists of 1,000 samples. ## Additional Information ### Dataset Curators The dataset has been curated by "thesven". ### Licensing Information Licensing information for the OOD-Speech dataset can be found in the original paper. ### Citation Information @article{OOD-Speech2023, title={OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking}, author={Authors of the OOD-Speech paper}, journal={arXiv preprint arXiv:2305.09688}, year={2023} }
Bonfire79/clinrec_01	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 178226 num_examples: 134 download_size: 61701 dataset_size: 178226 configs: - config_name: default data_files: - split: train path: data/train-* ---
pkuHaowei/stanford-cars	--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 splits: - name: train num_bytes: 245338060.0 num_examples: 8144 - name: test num_bytes: 241985926.875 num_examples: 8041 download_size: 482701950 dataset_size: 487323986.875 --- # Dataset Card for "stanford-cars" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
proserve/medical-instruct-mixer	--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 889897065.0 num_examples: 528642 - name: test num_bytes: 68932987.0 num_examples: 28501 download_size: 482795421 dataset_size: 958830052.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Sijuade/Cats-Dogs-Birds	--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' splits: - name: train num_bytes: 2858440330.32 num_examples: 13344 download_size: 2752316017 dataset_size: 2858440330.32 ---
arieg/color_spec_cls	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '10' '1': '140' '2': '141' '3': '190' '4': '193' '5': '194' '6': '197' '7': '2' '8': '200' '9': '5' splits: - name: train num_bytes: 10354796.0 num_examples: 100 download_size: 10356873 dataset_size: 10354796.0 --- # Dataset Card for "color_spec_cls" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
roszcz/tmp-midi-clip	--- dataset_info: features: - name: midi_filename dtype: string - name: pitch sequence: int16 length: 32 - name: dstart_bin sequence: int16 length: 32 - name: duration_bin sequence: int16 length: 32 - name: velocity_bin sequence: int16 length: 32 splits: - name: train num_bytes: 118752197 num_examples: 352232 - name: validation num_bytes: 13434506 num_examples: 39754 - name: test num_bytes: 15540656 num_examples: 46073 download_size: 21481498 dataset_size: 147727359 --- # Dataset Card for "tmp-midi-clip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tarasabkar/IEMOCAP_Speech	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: emotion dtype: class_label: names: 0: ang 1: hap 2: neu 3: sad splits: - name: Session1 num_bytes: 167102058.95 num_examples: 1085 - name: Session2 num_bytes: 150799933.454 num_examples: 1023 - name: Session3 num_bytes: 167088514.51 num_examples: 1151 - name: Session4 num_bytes: 145505839.808 num_examples: 1031 - name: Session5 num_bytes: 170307009.46 num_examples: 1241 download_size: 788399921 dataset_size: 800803356.182 --- # Dataset Card for "IEMOCAP_Speech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ShariThomas/dataset_sample	--- license: mit ---
Ammar-Azman/crawl-doktorbudak	--- license: mit ---
CyberHarem/mizusawa_matsuri_citrus	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Mizusawa Matsuri This is the dataset of Mizusawa Matsuri, containing 90 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 \| 90 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 197 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 233 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 90 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 90 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 90 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 197 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 197 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 171 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 233 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 233 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
tyzhu/find_first_sent_train_100_eval_10_dec	--- configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string - name: text dtype: string splits: - name: validation num_bytes: 11337 num_examples: 10 - name: train num_bytes: 379104 num_examples: 210 download_size: 197674 dataset_size: 390441 --- # Dataset Card for "find_first_sent_train_100_eval_10_dec" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gentilrenard/lmd_ukraine_comments	--- language: - fr license: mit size_categories: - 100K<n<1M task_categories: - text-classification pretty_name: Comments under Le Monde Ukraine war articles (1 year) dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 133853 num_examples: 323 - name: validation num_bytes: 54736 num_examples: 139 - name: unlabeled num_bytes: 64192366 num_examples: 174891 download_size: 39789476 dataset_size: 64380955 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: unlabeled path: data/unlabeled-* --- ## Comments under Le Monde Ukraine War Articles (1 Year) ### Description This dataset contains 175k comments extracted from Le Monde articles about the Ukraine war during its first year (February 2022 to 2023). Among these, around 500 comments are manually labeled into categories: 0. Explicit support for Ukraine, 1. pro Russia, 2. "Other". ### Dataset Structure #### Features - `text`: The comment text (string). - `label`: The label for the comment (integer). The labels are as follows: - 0: pro_Ukraine - 1: pro_Russia - 2: other - 4: no_label (the unlabeled data). #### Splits Train and validation are manually labeled. Unlabeled data could be used for knowledge distillation for instance. - `train`: 323 examples. - `validation`: 139 examples. - `unlabeled`: 174,891 examples. ### Additional Information - Homepage: [Project Repository](https://github.com/matthieuvion/lmd_classi) - License: MIT License - Language: French - Task Categories: Text Classification - Size Categories: 100K < n < 1M
ml6team/xsum_nl	--- annotations_creators: - machine-generated language_creators: - machine-generated language: - nl language_bcp47: - nl-BE license: - unknown multilinguality: - monolingual pretty_name: XSum NL size_categories: - unknown source_datasets: - extended\|xsum task_categories: - conditional-text-generation task_ids: - summarization --- # Dataset Card for XSum NL ## 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: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset is a machine translated dataset. It's the [XSum dataset](https://huggingface.co/datasets/xsum) translated with [this model](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) from English to Dutch. See the [Hugginface page of the original dataset](https://huggingface.co/datasets/xsum) for more information on the format of this dataset. Use with: ```python from datasets import load_dataset load_dataset("csv", "ml6team/xsum_nl") ``` ### Languages Dutch ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `id`: BBC ID of the article. - `document`: a string containing the body of the news article - `summary`: a string containing a one sentence summary of the article. ### Data Splits - `train` - `test` - `validation` ## 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 Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
jtgot/ServicesClassificationData	--- license: apache-2.0 ---
MruganKulkarni/restomenuu	--- license: mit ---
Circularmachines/batch_indexing_machine_230529_006	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 156741720.0 num_examples: 720 download_size: 156752582 dataset_size: 156741720.0 --- # Dataset Card for "batch_indexing_machine_230529_006" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
diguinn17/diguito	--- license: openrail ---
open-llm-leaderboard/details_sail__Sailor-7B	--- pretty_name: Evaluation run of sail/Sailor-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [sail/Sailor-7B](https://huggingface.co/sail/Sailor-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 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_sail__Sailor-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-03T06:19:10.406963](https://huggingface.co/datasets/open-llm-leaderboard/details_sail__Sailor-7B/blob/main/results_2024-03-03T06-19-10.406963.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\": 0.5442833810166537,\n\ \ \"acc_stderr\": 0.0340188362831904,\n \"acc_norm\": 0.5493690106415303,\n\ \ \"acc_norm_stderr\": 0.03472015784641931,\n \"mc1\": 0.2607099143206854,\n\ \ \"mc1_stderr\": 0.015368841620766373,\n \"mc2\": 0.40083895524705915,\n\ \ \"mc2_stderr\": 0.013870866160876278\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.4718430034129693,\n \"acc_stderr\": 0.014588204105102202,\n\ \ \"acc_norm\": 0.49829351535836175,\n \"acc_norm_stderr\": 0.014611305705056992\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5520812587134037,\n\ \ \"acc_stderr\": 0.0049626384463959845,\n \"acc_norm\": 0.7620991834295957,\n\ \ \"acc_norm_stderr\": 0.004249278842903416\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.4740740740740741,\n\ \ \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n\ \ \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5855263157894737,\n \"acc_stderr\": 0.04008973785779206,\n\ \ \"acc_norm\": 0.5855263157894737,\n \"acc_norm_stderr\": 0.04008973785779206\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5735849056603773,\n \"acc_stderr\": 0.030437794342983056,\n\ \ \"acc_norm\": 0.5735849056603773,\n \"acc_norm_stderr\": 0.030437794342983056\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5416666666666666,\n\ \ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n\ \ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_mathematics\|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-college_medicine\|5\": {\n \"acc\": 0.4682080924855491,\n\ \ \"acc_stderr\": 0.03804749744364763,\n \"acc_norm\": 0.4682080924855491,\n\ \ \"acc_norm_stderr\": 0.03804749744364763\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.044405219061793275,\n\ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.044405219061793275\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.71,\n \"acc_stderr\": 0.04560480215720685,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.04560480215720685\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.49361702127659574,\n \"acc_stderr\": 0.032683358999363366,\n\ \ \"acc_norm\": 0.49361702127659574,\n \"acc_norm_stderr\": 0.032683358999363366\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.32456140350877194,\n\ \ \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n\ \ \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.37037037037037035,\n \"acc_stderr\": 0.024870815251057093,\n \"\ acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.024870815251057093\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.373015873015873,\n\ \ \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n\ \ \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.635483870967742,\n\ \ \"acc_stderr\": 0.027379871229943252,\n \"acc_norm\": 0.635483870967742,\n\ \ \"acc_norm_stderr\": 0.027379871229943252\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.03465304488406795,\n\ \ \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.03465304488406795\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n\ \ \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.03742597043806586,\n\ \ \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.03742597043806586\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6868686868686869,\n \"acc_stderr\": 0.033042050878136525,\n \"\ acc_norm\": 0.6868686868686869,\n \"acc_norm_stderr\": 0.033042050878136525\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7564766839378239,\n \"acc_stderr\": 0.030975436386845443,\n\ \ \"acc_norm\": 0.7564766839378239,\n \"acc_norm_stderr\": 0.030975436386845443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.4948717948717949,\n \"acc_stderr\": 0.025349672906838653,\n\ \ \"acc_norm\": 0.4948717948717949,\n \"acc_norm_stderr\": 0.025349672906838653\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \ \ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5126050420168067,\n \"acc_stderr\": 0.032468167657521745,\n\ \ \"acc_norm\": 0.5126050420168067,\n \"acc_norm_stderr\": 0.032468167657521745\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7174311926605504,\n \"acc_stderr\": 0.019304243497707152,\n \"\ acc_norm\": 0.7174311926605504,\n \"acc_norm_stderr\": 0.019304243497707152\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.03395322726375797,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.03395322726375797\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7254901960784313,\n \"acc_stderr\": 0.031321798030832904,\n \"\ acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.031321798030832904\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.6666666666666666,\n \"acc_stderr\": 0.0306858205966108,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.0306858205966108\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6547085201793722,\n\ \ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\ \ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.042258754519696365,\n\ \ \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.042258754519696365\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6859504132231405,\n \"acc_stderr\": 0.042369647530410184,\n \"\ acc_norm\": 0.6859504132231405,\n \"acc_norm_stderr\": 0.042369647530410184\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5740740740740741,\n\ \ \"acc_stderr\": 0.0478034362693679,\n \"acc_norm\": 0.5740740740740741,\n\ \ \"acc_norm_stderr\": 0.0478034362693679\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6134969325153374,\n \"acc_stderr\": 0.03825825548848607,\n\ \ \"acc_norm\": 0.6134969325153374,\n \"acc_norm_stderr\": 0.03825825548848607\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5178571428571429,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.04453254836326468,\n\ \ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.04453254836326468\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8205128205128205,\n\ \ \"acc_stderr\": 0.025140935950335428,\n \"acc_norm\": 0.8205128205128205,\n\ \ \"acc_norm_stderr\": 0.025140935950335428\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7266922094508301,\n\ \ \"acc_stderr\": 0.015936681062628556,\n \"acc_norm\": 0.7266922094508301,\n\ \ \"acc_norm_stderr\": 0.015936681062628556\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.026483392042098174,\n\ \ \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.026483392042098174\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.24581005586592178,\n\ \ \"acc_stderr\": 0.014400296429225629,\n \"acc_norm\": 0.24581005586592178,\n\ \ \"acc_norm_stderr\": 0.014400296429225629\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.02791405551046801,\n\ \ \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.02791405551046801\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6205787781350482,\n\ \ \"acc_stderr\": 0.027559949802347824,\n \"acc_norm\": 0.6205787781350482,\n\ \ \"acc_norm_stderr\": 0.027559949802347824\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6203703703703703,\n \"acc_stderr\": 0.027002521034516478,\n\ \ \"acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.027002521034516478\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.41134751773049644,\n \"acc_stderr\": 0.029354911159940975,\n \ \ \"acc_norm\": 0.41134751773049644,\n \"acc_norm_stderr\": 0.029354911159940975\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.37614080834419816,\n\ \ \"acc_stderr\": 0.012372214430599826,\n \"acc_norm\": 0.37614080834419816,\n\ \ \"acc_norm_stderr\": 0.012372214430599826\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.030372836961539352,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.030372836961539352\n \ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\"\ : 0.5228758169934641,\n \"acc_stderr\": 0.020206653187884786,\n \"\ acc_norm\": 0.5228758169934641,\n \"acc_norm_stderr\": 0.020206653187884786\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6181818181818182,\n\ \ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\ \ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6612244897959184,\n \"acc_stderr\": 0.030299506562154185,\n\ \ \"acc_norm\": 0.6612244897959184,\n \"acc_norm_stderr\": 0.030299506562154185\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7313432835820896,\n\ \ \"acc_stderr\": 0.03134328358208954,\n \"acc_norm\": 0.7313432835820896,\n\ \ \"acc_norm_stderr\": 0.03134328358208954\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.45180722891566266,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.695906432748538,\n \"acc_stderr\": 0.0352821125824523,\n\ \ \"acc_norm\": 0.695906432748538,\n \"acc_norm_stderr\": 0.0352821125824523\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2607099143206854,\n\ \ \"mc1_stderr\": 0.015368841620766373,\n \"mc2\": 0.40083895524705915,\n\ \ \"mc2_stderr\": 0.013870866160876278\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.691397000789266,\n \"acc_stderr\": 0.012982160200926574\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.33358605003790753,\n \ \ \"acc_stderr\": 0.012987282131410809\n }\n}\n```" repo_url: https://huggingface.co/sail/Sailor-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_03_02T22_29_37.991675 path: - '/details_harness\|arc:challenge\|25_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|arc:challenge\|25_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-03T06-19-10.406963.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|gsm8k\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|gsm8k\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hellaswag\|10_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hellaswag\|10_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-02T22-29-37.991675.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-03T06-19-10.406963.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-03T06-19-10.406963.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '/details_harness\|winogrande\|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '/details_harness\|winogrande\|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-03T06-19-10.406963.parquet' - config_name: results data_files: - split: 2024_03_02T22_29_37.991675 path: - results_2024-03-02T22-29-37.991675.parquet - split: 2024_03_03T06_19_10.406963 path: - results_2024-03-03T06-19-10.406963.parquet - split: latest path: - results_2024-03-03T06-19-10.406963.parquet --- # Dataset Card for Evaluation run of sail/Sailor-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [sail/Sailor-7B](https://huggingface.co/sail/Sailor-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_sail__Sailor-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-03T06:19:10.406963](https://huggingface.co/datasets/open-llm-leaderboard/details_sail__Sailor-7B/blob/main/results_2024-03-03T06-19-10.406963.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": 0.5442833810166537, "acc_stderr": 0.0340188362831904, "acc_norm": 0.5493690106415303, "acc_norm_stderr": 0.03472015784641931, "mc1": 0.2607099143206854, "mc1_stderr": 0.015368841620766373, "mc2": 0.40083895524705915, "mc2_stderr": 0.013870866160876278 }, "harness\|arc:challenge\|25": { "acc": 0.4718430034129693, "acc_stderr": 0.014588204105102202, "acc_norm": 0.49829351535836175, "acc_norm_stderr": 0.014611305705056992 }, "harness\|hellaswag\|10": { "acc": 0.5520812587134037, "acc_stderr": 0.0049626384463959845, "acc_norm": 0.7620991834295957, "acc_norm_stderr": 0.004249278842903416 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4740740740740741, "acc_stderr": 0.04313531696750574, "acc_norm": 0.4740740740740741, "acc_norm_stderr": 0.04313531696750574 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5855263157894737, "acc_stderr": 0.04008973785779206, "acc_norm": 0.5855263157894737, "acc_norm_stderr": 0.04008973785779206 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5735849056603773, "acc_stderr": 0.030437794342983056, "acc_norm": 0.5735849056603773, "acc_norm_stderr": 0.030437794342983056 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.04166666666666665, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.04166666666666665 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.4682080924855491, "acc_stderr": 0.03804749744364763, "acc_norm": 0.4682080924855491, "acc_norm_stderr": 0.03804749744364763 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.27450980392156865, "acc_stderr": 0.044405219061793275, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.044405219061793275 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.71, "acc_stderr": 0.04560480215720685, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720685 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.49361702127659574, "acc_stderr": 0.032683358999363366, "acc_norm": 0.49361702127659574, "acc_norm_stderr": 0.032683358999363366 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.32456140350877194, 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"acc_stderr": 0.03395322726375797, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.03395322726375797 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7254901960784313, "acc_stderr": 0.031321798030832904, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.031321798030832904 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.0306858205966108, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.0306858205966108 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6547085201793722, "acc_stderr": 0.03191100192835794, "acc_norm": 0.6547085201793722, "acc_norm_stderr": 0.03191100192835794 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6335877862595419, "acc_stderr": 0.042258754519696365, "acc_norm": 0.6335877862595419, "acc_norm_stderr": 0.042258754519696365 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6859504132231405, "acc_stderr": 0.042369647530410184, "acc_norm": 0.6859504132231405, "acc_norm_stderr": 0.042369647530410184 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5740740740740741, "acc_stderr": 0.0478034362693679, "acc_norm": 0.5740740740740741, "acc_norm_stderr": 0.0478034362693679 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6134969325153374, "acc_stderr": 0.03825825548848607, "acc_norm": 0.6134969325153374, "acc_norm_stderr": 0.03825825548848607 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5178571428571429, "acc_stderr": 0.047427623612430116, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7184466019417476, "acc_stderr": 0.04453254836326468, "acc_norm": 0.7184466019417476, "acc_norm_stderr": 0.04453254836326468 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8205128205128205, "acc_stderr": 0.025140935950335428, "acc_norm": 0.8205128205128205, "acc_norm_stderr": 0.025140935950335428 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7266922094508301, "acc_stderr": 0.015936681062628556, "acc_norm": 0.7266922094508301, "acc_norm_stderr": 0.015936681062628556 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5895953757225434, "acc_stderr": 0.026483392042098174, "acc_norm": 0.5895953757225434, "acc_norm_stderr": 0.026483392042098174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24581005586592178, "acc_stderr": 0.014400296429225629, "acc_norm": 0.24581005586592178, "acc_norm_stderr": 0.014400296429225629 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.02791405551046801, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.02791405551046801 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6205787781350482, "acc_stderr": 0.027559949802347824, "acc_norm": 0.6205787781350482, "acc_norm_stderr": 0.027559949802347824 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6203703703703703, "acc_stderr": 0.027002521034516478, "acc_norm": 0.6203703703703703, "acc_norm_stderr": 0.027002521034516478 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.41134751773049644, "acc_stderr": 0.029354911159940975, "acc_norm": 0.41134751773049644, "acc_norm_stderr": 0.029354911159940975 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.37614080834419816, "acc_stderr": 0.012372214430599826, "acc_norm": 0.37614080834419816, "acc_norm_stderr": 0.012372214430599826 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5, "acc_stderr": 0.030372836961539352, "acc_norm": 0.5, "acc_norm_stderr": 0.030372836961539352 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5228758169934641, "acc_stderr": 0.020206653187884786, "acc_norm": 0.5228758169934641, "acc_norm_stderr": 0.020206653187884786 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6181818181818182, "acc_stderr": 0.046534298079135075, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.046534298079135075 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6612244897959184, "acc_stderr": 0.030299506562154185, "acc_norm": 0.6612244897959184, "acc_norm_stderr": 0.030299506562154185 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7313432835820896, "acc_stderr": 0.03134328358208954, "acc_norm": 0.7313432835820896, "acc_norm_stderr": 0.03134328358208954 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.45180722891566266, "acc_stderr": 0.03874371556587953, "acc_norm": 0.45180722891566266, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.695906432748538, "acc_stderr": 0.0352821125824523, "acc_norm": 0.695906432748538, "acc_norm_stderr": 0.0352821125824523 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2607099143206854, "mc1_stderr": 0.015368841620766373, "mc2": 0.40083895524705915, "mc2_stderr": 0.013870866160876278 }, "harness\|winogrande\|5": { "acc": 0.691397000789266, "acc_stderr": 0.012982160200926574 }, "harness\|gsm8k\|5": { "acc": 0.33358605003790753, "acc_stderr": 0.012987282131410809 } } ``` ## 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]
danjacobellis/audio_har_descript_44kHz_frames_1200	--- dataset_info: features: - name: codes dtype: array2_d: shape: - 9 - 640 dtype: float32 - name: label dtype: class_label: names: '0': No Activity '1': Writing '2': Drawing '3': Cutting paper '4': Typing on keyboard '5': Typing on phone '6': Browsing on phone '7': Clapping '8': Shuffling cards '9': Scratching '10': Wiping table '11': Brushing hair '12': Washing hands '13': Drinking '14': Eating snacks '15': Brushing teeth '16': Chopping '17': Grating '18': Frying '19': Sweeping '20': Vacuuming '21': Washing dishes '22': Filling water '23': Using microwave - name: label_str dtype: string - name: participant dtype: int32 splits: - name: train num_bytes: 28945873 num_examples: 669 download_size: 9026774 dataset_size: 28945873 configs: - config_name: default data_files: - split: train path: data/train-* ---
johannes-garstenauer/structs_token_size_4_use_pd_True_full_amt_False_div_20	--- dataset_info: features: - name: struct dtype: string splits: - name: train num_bytes: 25156800 num_examples: 237600 download_size: 7394910 dataset_size: 25156800 --- # Dataset Card for "structs_token_size_4_use_pd_True_full_amt_False_div_20" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
senhorsapo/rem	--- license: openrail ---
mtkinit/Super5473892	--- pretty_name: Super5473892 tags: - aaa --- # Super5473892 Created from AIOD platform
Faiza3/anime_cloth	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 210633.0 num_examples: 15 download_size: 211995 dataset_size: 210633.0 --- # Dataset Card for "anime_cloth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liyucheng/zhihu_rlhf_3k	--- license: cc-by-2.0 ---
CyberHarem/priestess_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Priestess/普瑞赛斯 (Arknights) This is the dataset of Priestess/普瑞赛斯 (Arknights), containing 22 images and their tags. The core tags of this character are `long_hair, hairband, breasts, brown_hair, black_hair, black_hairband, purple_eyes, bow, hair_between_eyes, large_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 \| 22 \| 28.41 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/priestess_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 22 \| 24.30 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/priestess_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 43 \| 42.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/priestess_arknights/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/priestess_arknights', 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 \| 22 \| ![](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, looking_at_viewer, smile, simple_background, long_sleeves, white_background, closed_mouth, shirt, upper_body, jacket, open_clothes \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| looking_at_viewer \| smile \| simple_background \| long_sleeves \| white_background \| closed_mouth \| shirt \| upper_body \| jacket \| open_clothes \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:--------\|:--------------------\|:---------------\|:-------------------\|:---------------\|:--------\|:-------------\|:---------\|:---------------\| \| 0 \| 22 \| ![](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 \|
Deojoandco/reddit-ah-dialogturns-annotations	--- dataset_info: features: - name: id dtype: string - name: speaker dtype: string - name: text dtype: string - name: annotation dtype: string splits: - name: train num_bytes: 3772164 num_examples: 16055 - name: validation num_bytes: 376937 num_examples: 1641 - name: test num_bytes: 360334 num_examples: 1559 download_size: 0 dataset_size: 4509435 --- # Dataset Card for "reddit-ah-dialogturns-annotations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/oklahoma_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane) This is the dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane), containing 28 images and their tags. The core tags of this character are `ahoge, blue_eyes, breasts, hair_between_eyes, blonde_hair, short_hair, bangs, large_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 \| 28 \| 34.87 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 28 \| 18.46 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 65 \| 39.28 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 28 \| 30.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 65 \| 61.60 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/oklahoma_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, looking_at_viewer, solo, detached_sleeves, open_mouth, hat, simple_background, :d, boots, brown_gloves, white_background, brown_skirt, cleavage_cutout, long_sleeves, medium_breasts \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| detached_sleeves \| open_mouth \| hat \| simple_background \| :d \| boots \| brown_gloves \| white_background \| brown_skirt \| cleavage_cutout \| long_sleeves \| medium_breasts \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:-------------------\|:-------------\|:------\|:--------------------\|:-----\|:--------\|:---------------\|:-------------------\|:--------------\|:------------------\|:---------------\|:-----------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
open-llm-leaderboard/details_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough	--- pretty_name: Evaluation run of AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough](https://huggingface.co/AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough)\ \ 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_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-01T17:09:32.814517](https://huggingface.co/datasets/open-llm-leaderboard/details_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough/blob/main/results_2024-03-01T17-09-32.814517.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\": 0.6122781802684721,\n\ \ \"acc_stderr\": 0.032268454851190724,\n \"acc_norm\": 0.625230553768112,\n\ \ \"acc_norm_stderr\": 0.03315794745802012,\n \"mc1\": 0.2582619339045288,\n\ \ \"mc1_stderr\": 0.015321821688476185,\n \"mc2\": 0.5102578228172799,\n\ \ \"mc2_stderr\": 0.01648564659078862\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.3924914675767918,\n \"acc_stderr\": 0.014269634635670717,\n\ \ \"acc_norm\": 0.46245733788395904,\n \"acc_norm_stderr\": 0.014570144495075583\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.3632742481577375,\n\ \ \"acc_stderr\": 0.004799599840397383,\n \"acc_norm\": 0.5606452897829117,\n\ \ \"acc_norm_stderr\": 0.004952942072999274\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.0373852067611967,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.0373852067611967\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7430555555555556,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.04940635630605659,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.04940635630605659\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.046774730044911984,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.046774730044911984\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728762,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728762\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\"\ : 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n\ \ \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\ \ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.03287666758603491,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.03287666758603491\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217483,\n \"\ acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217483\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768766,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768766\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6435897435897436,\n \"acc_stderr\": 0.024283140529467305,\n\ \ \"acc_norm\": 0.6435897435897436,\n \"acc_norm_stderr\": 0.024283140529467305\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.031204691225150013,\n\ \ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.031204691225150013\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8293577981651377,\n \"acc_stderr\": 0.016129271025099857,\n \"\ acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.016129271025099857\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\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290902,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290902\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7040358744394619,\n\ \ \"acc_stderr\": 0.03063659134869981,\n \"acc_norm\": 0.7040358744394619,\n\ \ \"acc_norm_stderr\": 0.03063659134869981\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\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\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286774,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286774\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\": 0.7961165048543689,\n \"acc_stderr\": 0.0398913985953177,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.0398913985953177\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.021901905115073325\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8263090676883781,\n\ \ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\ \ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.02433214677913413,\n\ \ \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.02433214677913413\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3743016759776536,\n\ \ \"acc_stderr\": 0.016185444179457175,\n \"acc_norm\": 0.3743016759776536,\n\ \ \"acc_norm_stderr\": 0.016185444179457175\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.026643278474508755,\n\ \ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.026643278474508755\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.02540719779889016,\n\ \ \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.02540719779889016\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.44589308996088656,\n\ \ \"acc_stderr\": 0.012695244711379778,\n \"acc_norm\": 0.44589308996088656,\n\ \ \"acc_norm_stderr\": 0.012695244711379778\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681397,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681397\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360378,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360378\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\": 0.7224489795918367,\n \"acc_stderr\": 0.02866685779027465,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.02866685779027465\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2582619339045288,\n\ \ \"mc1_stderr\": 0.015321821688476185,\n \"mc2\": 0.5102578228172799,\n\ \ \"mc2_stderr\": 0.01648564659078862\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7277032359905288,\n \"acc_stderr\": 0.012510697991453937\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough 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_01T17_09_32.814517 path: - '/details_harness\|arc:challenge\|25_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-01T17-09-32.814517.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|gsm8k\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hellaswag\|10_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-01T17-09-32.814517.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-01T17-09-32.814517.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '/details_harness\|winogrande\|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-01T17-09-32.814517.parquet' - config_name: results data_files: - split: 2024_03_01T17_09_32.814517 path: - results_2024-03-01T17-09-32.814517.parquet - split: latest path: - results_2024-03-01T17-09-32.814517.parquet --- # Dataset Card for Evaluation run of AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough](https://huggingface.co/AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough) 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_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-01T17:09:32.814517](https://huggingface.co/datasets/open-llm-leaderboard/details_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough/blob/main/results_2024-03-01T17-09-32.814517.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": 0.6122781802684721, "acc_stderr": 0.032268454851190724, "acc_norm": 0.625230553768112, "acc_norm_stderr": 0.03315794745802012, "mc1": 0.2582619339045288, "mc1_stderr": 0.015321821688476185, "mc2": 0.5102578228172799, "mc2_stderr": 0.01648564659078862 }, "harness\|arc:challenge\|25": { "acc": 0.3924914675767918, "acc_stderr": 0.014269634635670717, "acc_norm": 0.46245733788395904, "acc_norm_stderr": 0.014570144495075583 }, "harness\|hellaswag\|10": { "acc": 0.3632742481577375, "acc_stderr": 0.004799599840397383, "acc_norm": 0.5606452897829117, "acc_norm_stderr": 0.004952942072999274 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6973684210526315, "acc_stderr": 0.0373852067611967, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.0373852067611967 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.04940635630605659, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.04940635630605659 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108102, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108102 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4473684210526316, "acc_stderr": 0.046774730044911984, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.046774730044911984 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728762, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728762 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7516129032258064, "acc_stderr": 0.024580028921481003, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481003 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.03287666758603491, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.03287666758603491 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217483, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217483 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768766, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768766 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6435897435897436, "acc_stderr": 0.024283140529467305, "acc_norm": 0.6435897435897436, "acc_norm_stderr": 0.024283140529467305 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6386554621848739, "acc_stderr": 0.031204691225150013, "acc_norm": 0.6386554621848739, "acc_norm_stderr": 0.031204691225150013 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8293577981651377, "acc_stderr": 0.016129271025099857, "acc_norm": 0.8293577981651377, "acc_norm_stderr": 0.016129271025099857 }, "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": 0.8431372549019608, "acc_stderr": 0.02552472232455334, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455334 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290902, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290902 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7040358744394619, "acc_stderr": 0.03063659134869981, "acc_norm": 0.7040358744394619, "acc_norm_stderr": 0.03063659134869981 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7786259541984732, "acc_stderr": 0.03641297081313729, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.03641297081313729 }, "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": 0.7852760736196319, "acc_stderr": 0.03226219377286774, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.03226219377286774 }, "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": 0.7961165048543689, "acc_stderr": 0.0398913985953177, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.0398913985953177 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.021901905115073325, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.021901905115073325 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8263090676883781, "acc_stderr": 0.01354741565866226, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.01354741565866226 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7138728323699421, "acc_stderr": 0.02433214677913413, "acc_norm": 0.7138728323699421, "acc_norm_stderr": 0.02433214677913413 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3743016759776536, "acc_stderr": 0.016185444179457175, "acc_norm": 0.3743016759776536, "acc_norm_stderr": 0.016185444179457175 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6830065359477124, "acc_stderr": 0.026643278474508755, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.026643278474508755 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7037037037037037, "acc_stderr": 0.02540719779889016, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.02540719779889016 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.44589308996088656, "acc_stderr": 0.012695244711379778, "acc_norm": 0.44589308996088656, "acc_norm_stderr": 0.012695244711379778 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681397, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681397 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6683006535947712, "acc_stderr": 0.019047485239360378, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.019047485239360378 }, "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": 0.7224489795918367, "acc_stderr": 0.02866685779027465, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.02866685779027465 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623327, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623327 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.038786267710023595, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.038786267710023595 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2582619339045288, "mc1_stderr": 0.015321821688476185, "mc2": 0.5102578228172799, "mc2_stderr": 0.01648564659078862 }, "harness\|winogrande\|5": { "acc": 0.7277032359905288, "acc_stderr": 0.012510697991453937 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## 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 <!-- 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open-llm-leaderboard/details_codellama__CodeLlama-7b-hf	--- pretty_name: Evaluation run of codellama/CodeLlama-7b-hf dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_codellama__CodeLlama-7b-hf\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-14T19:46:33.225068](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-7b-hf/blob/main/results_2023-10-14T19-46-33.225068.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\": 0.0006291946308724832,\n\ \ \"em_stderr\": 0.00025680027497238217,\n \"f1\": 0.05123741610738289,\n\ \ \"f1_stderr\": 0.001242998424746743,\n \"acc\": 0.34582445982552373,\n\ \ \"acc_stderr\": 0.009790248772764803\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0006291946308724832,\n \"em_stderr\": 0.00025680027497238217,\n\ \ \"f1\": 0.05123741610738289,\n \"f1_stderr\": 0.001242998424746743\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.05155420773313116,\n \ \ \"acc_stderr\": 0.006090887955262816\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6400947119179163,\n \"acc_stderr\": 0.01348960959026679\n\ \ }\n}\n```" repo_url: https://huggingface.co/codellama/CodeLlama-7b-hf leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|arc:challenge\|25_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-26T04:20:17.128606.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_14T19_46_33.225068 path: - '/details_harness\|drop\|3_2023-10-14T19-46-33.225068.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-14T19-46-33.225068.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_14T19_46_33.225068 path: - '/details_harness\|gsm8k\|5_2023-10-14T19-46-33.225068.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-14T19-46-33.225068.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hellaswag\|10_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-26T04:20:17.128606.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_26T04_20_17.128606 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-26T04:20:17.128606.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_14T19_46_33.225068 path: - '/details_harness\|winogrande\|5_2023-10-14T19-46-33.225068.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-14T19-46-33.225068.parquet' - config_name: results data_files: - split: 2023_08_26T04_20_17.128606 path: - results_2023-08-26T04:20:17.128606.parquet - split: 2023_10_14T19_46_33.225068 path: - results_2023-10-14T19-46-33.225068.parquet - split: latest path: - results_2023-10-14T19-46-33.225068.parquet --- # Dataset Card for Evaluation run of codellama/CodeLlama-7b-hf ## Dataset Description - Homepage: - Repository: https://huggingface.co/codellama/CodeLlama-7b-hf - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_codellama__CodeLlama-7b-hf", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-14T19:46:33.225068](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-7b-hf/blob/main/results_2023-10-14T19-46-33.225068.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": 0.0006291946308724832, "em_stderr": 0.00025680027497238217, "f1": 0.05123741610738289, "f1_stderr": 0.001242998424746743, "acc": 0.34582445982552373, "acc_stderr": 0.009790248772764803 }, "harness\|drop\|3": { "em": 0.0006291946308724832, "em_stderr": 0.00025680027497238217, "f1": 0.05123741610738289, "f1_stderr": 0.001242998424746743 }, "harness\|gsm8k\|5": { "acc": 0.05155420773313116, "acc_stderr": 0.006090887955262816 }, "harness\|winogrande\|5": { "acc": 0.6400947119179163, "acc_stderr": 0.01348960959026679 } } ``` ### 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]
MilanHrab/Kosice_training	--- dataset_info: features: - name: name_of_record dtype: string - name: speech_array sequence: float64 - name: sampling_rate dtype: int64 - name: label dtype: string splits: - name: train num_bytes: 1178840561.6 num_examples: 4480 download_size: 894629427 dataset_size: 1178840561.6 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Kosice_training" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/yakumo_ran_touhou	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yakumo_ran/八雲藍/야쿠모란 (Touhou) This is the dataset of yakumo_ran/八雲藍/야쿠모란 (Touhou), containing 500 images and their tags. The core tags of this character are `blonde_hair, short_hair, fox_tail, tail, multiple_tails, yellow_eyes, hat, animal_ears, fox_ears, breasts, pillow_hat`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 614.16 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_touhou/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 392.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_touhou/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1158 \| 786.48 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_touhou/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 569.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_touhou/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1158 \| 1.02 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_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/yakumo_ran_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 \| 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, closed_mouth, long_sleeves, solo, tabard, white_dress, wide_sleeves, bangs, looking_at_viewer, white_headwear, blush, frills, large_breasts, simple_background, white_background \| \| 1 \| 16 \| ![](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, bangs, long_sleeves, solo, tabard, white_dress, wide_sleeves, looking_at_viewer, white_headwear, frills, closed_mouth, simple_background, smile, hands_in_opposite_sleeves, hair_between_eyes, white_background, upper_body, blush \| \| 2 \| 18 \| ![](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, long_sleeves, solo, tabard, looking_at_viewer, wide_sleeves, hands_in_opposite_sleeves, smile, white_dress, large_breasts, white_background \| \| 3 \| 18 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, hands_in_opposite_sleeves, solo, smile, wide_sleeves \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, looking_at_viewer, solo, blush, open_mouth, smile, tabard, large_breasts, no_headwear, upper_body, animal_ear_fluff \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, fox_mask, solo \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, closed_mouth, jeans, large_breasts, looking_at_viewer, simple_background, slit_pupils, solo, bangs, barefoot, blush, seiza, white_background, blue_pants, full_body, no_tail, short_sleeves, blue_shirt, long_sleeves, sweater \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| closed_mouth \| long_sleeves \| solo \| tabard \| white_dress \| wide_sleeves \| bangs \| looking_at_viewer \| white_headwear \| blush \| frills \| large_breasts \| simple_background \| white_background \| smile \| hands_in_opposite_sleeves \| hair_between_eyes \| upper_body \| open_mouth \| no_headwear \| animal_ear_fluff \| fox_mask \| jeans \| slit_pupils \| barefoot \| seiza \| blue_pants \| full_body \| no_tail \| short_sleeves \| blue_shirt \| sweater \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:---------------\|:-------\|:---------\|:--------------\|:---------------\|:--------\|:--------------------\|:-----------------\|:--------\|:---------\|:----------------\|:--------------------\|:-------------------\|:--------\|:----------------------------\|:--------------------\|:-------------\|:-------------\|:--------------\|:-------------------\|:-----------\|:--------\|:--------------\|:-----------\|:--------\|:-------------\|:------------\|:----------\|:----------------\|:-------------\|:----------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 16 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 18 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 18 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| X \| X \| \| \| \| X \| \| X \| \| X \| \| \| X \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| X \| X \| \| \| \| X \| X \| \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
sudy-super/piece-of-refined-oscar	--- license: apache-2.0 task_categories: - text-generation language: - ja size_categories: - 1M<n<10M --- # Descrption This dataset is part of the [OSCAR-2301](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301) cleaned. There are about 0.5b tokens counted by [calm2](https://huggingface.co/cyberagent/calm2-7b) tokenizer. # NOTE This dataset has not passed sentence end boundary determination or Perplexity Filtering, so there is room for improvement in quality.
nikchar/paper_test_set	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: claim dtype: string - name: evidence_wiki_url dtype: string - name: text dtype: string splits: - name: train num_bytes: 15920562 num_examples: 11073 download_size: 6320618 dataset_size: 15920562 --- # Dataset Card for "paper_test_set" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mnli_existential_you_have	--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 183415 num_examples: 823 - name: dev_mismatched num_bytes: 167912 num_examples: 686 - name: test_matched num_bytes: 181716 num_examples: 817 - name: test_mismatched num_bytes: 154207 num_examples: 688 - name: train num_bytes: 7401659 num_examples: 32434 download_size: 4938545 dataset_size: 8088909 --- # Dataset Card for "MULTI_VALUE_mnli_existential_you_have" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/OxfordPets_Multimodal_Fatima_opt_175b_LLM_Description_opt175b_downstream_tasks_ViT_L_14	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: text dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: test num_bytes: 3482068.0 num_examples: 100 download_size: 3458504 dataset_size: 3482068.0 --- # Dataset Card for "OxfordPets_Multimodal_Fatima_opt_175b_LLM_Description_opt175b_downstream_tasks_ViT_L_14" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-kand2-sdxl-wuerst-karlo/b6ea8c05	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 176 num_examples: 10 download_size: 1328 dataset_size: 176 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b6ea8c05" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
imageomics/KABR	--- license: cc0-1.0 task_categories: - video-classification tags: - zebra - giraffe - plains zebra - Grevy's zebra - video - animal behavior - behavior recognition - annotation - annotated video - conservation - drone - UAV - imbalanced - Kenya - Mpala Research Centre pretty_name: >- KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos size_categories: - 1M<n<10M --- # Dataset Card for KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos ## Dataset Description - Homepage: https://dirtmaxim.github.io/kabr/ - Repository: https://github.com/dirtmaxim/kabr-tools - Paper: https://openaccess.thecvf.com/content/WACV2024W/CV4Smalls/papers/Kholiavchenko_KABR_In-Situ_Dataset_for_Kenyan_Animal_Behavior_Recognition_From_Drone_WACVW_2024_paper.pdf - Leaderboard: - Point of Contact: ### Dataset Summary We present a novel high-quality dataset for animal behavior recognition from drone videos. The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors. The drone footage is captured at a resolution of 5472 x 3078 pixels, and the videos were recorded at a frame rate of 29.97 frames per second. <!--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 The results of our evaluation using I3D, SlowFast, and X3D architectures are given in the table below. For each one, the model was trained for 120 epochs with batch size of 5. For more information on these results, see our [paper](coming soon). \| Method \| All \| Giraffes \| Plains Zebras \| Grevy’s Zebras \| \| ---- \| ---- \| ---- \| ---- \| ---- \| \| I3D (16x5) \| 53.41 \| 61.82 \| 58.75 \| 46.73 \| \| SlowFast (16x5, 4x5) \| 52.92 \| 61.15 \| 60.60 \| 47.42 \| \| X3D (16x5) \| 61.9 \| 65.1 \| 63.11 \| 51.16 \| ### Languages English ## Dataset Structure Under `KABR/dataset/image/`, the data has been archived into `.zip` files, which are split into 2GB files. These must be recombined and extracted. After cloning and navigating into the repository, you can use the following commands to do the reconstruction: ```bash cd KABR/dataset/image/ cat giraffes_part_* > giraffes.zip md5sum giraffes.zip # Compare this to what's shown with `cat giraffes_md5.txt` unzip giraffes.zip rm -rf giraffes_part_* # Similarly for `zebras_grevys_part_` and `zebras_plains_part_` ``` Alternatively, there is a download script, `download.py`, which allows a download of the entire dataset in its established format without requiring one to clone the repository (cloning requires _at least_ double the size of the dataset to store). To proceed with this approach, download `download.py` to the system where you want to access the data. Then, in the same directory as the script, run the following to begin the download: ``` pip install requests python download.py ``` This script then downloads all the files present in the repository (without making a clone of the `.git` directory, etc.), concatenates the part files to their ZIP archives, verifies the MD5 checksums, extracts, and cleans up so that the folder structure, as described below, is present. Note that it will require approximately 116GB of free space to complete this process, though the final dataset will only take about 61GB of disk space (the script removes the extra files after checking the download was successful). The KABR dataset follows the Charades format: ``` KABR /dataset /image /video_1 /image_1.jpg /image_2.jpg ... /image_n.jpg /video_2 /image_1.jpg /image_2.jpg ... /image_n.jpg ... /video_n /image_1.jpg /image_2.jpg /image_3.jpg ... /image_n.jpg /annotation /classes.json /train.csv /val.csv ``` The dataset can be directly loaded and processed by the [SlowFast](https://github.com/facebookresearch/SlowFast) framework. Informational Files * `KABR/configs`: examples of SlowFast framework configs. * `KABR/annotation/distribution.xlsx`: distribution of classes for all videos. Scripts: * `image2video.py`: Encode image sequences into the original video. * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `video/G0067.mp4`. * `image2visual.py`: Encode image sequences into the original video with corresponding annotations. * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `visual/G0067.mp4`. ### Data Instances Naming: Within the image folder, the `video_n` folders are named as follows (X indicates a number): * G0XXX.X - Giraffes * ZP0XXX.X - Plains Zebras * ZG0XXX.X - Grevy's Zebras * Within each of these folders the images are simply `X.jpg`. Note: The dataset consists of a total of 1,139,893 frames captured from drone videos. There are 488,638 frames of Grevy's zebras, 492,507 frames of plains zebras, and 158,748 frames of giraffes. ### Data Fields There are 14,764 unique behavioral sequences in the dataset. These consist of eight distinct behaviors: - Walk - Trot - Run: animal is moving at a cantor or gallop - Graze: animal is eating grass or other vegetation - Browse: animal is eating trees or bushes - Head Up: animal is looking around or observe surroundings - Auto-Groom: animal is grooming itself (licking, scratching, or rubbing) - Occluded: animal is not fully visible ### Data Splits Training and validation sets are indicated by their respective CSV files (`train.csv` and `val.csv`), located within the `annotation` folder. ## Dataset Creation ### Curation Rationale We present a novel high-quality dataset for animal behavior recognition from drone videos. The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors. We believe that this dataset will be a valuable resource for researchers working on animal behavior recognition, as it provides a diverse and high-quality set of annotated videos that can be used for evaluating deep learning models. Additionally, the dataset can be used to study the behavior patterns of Kenyan animals and can help to inform conservation efforts and wildlife management strategies. <!-- [To be added:] --> We provide a detailed description of the dataset and its annotation process, along with some initial experiments on the dataset using conventional deep learning models. The results demonstrate the effectiveness of the dataset for animal behavior recognition and highlight the potential for further research in this area. ### Source Data #### Initial Data Collection and Normalization Data was collected from 6 January 2023 through 21 January 2023 at the [Mpala Research Centre](https://mpala.org/) in Kenya under a Nacosti research license. We used DJI Mavic 2S drones equipped with cameras to record 5.4K resolution videos (5472 x 3078 pixels) from varying altitudes and distances of 10 to 50 meters from the animals (distance was determined by circumstances and safety regulations). Mini-scenes were extracted from these videos to reduce the impact of drone movement and facilitate human annotation. Animals were detected in frame using YOLOv8, then the SORT tracking algorithm was applied to follow their movement. A 400 by 300 pixel window, centered on the animal, was then extracted; this is the mini-scene. <!-- #### Who are the source language producers? [More Information Needed] --> ### Annotations #### Annotation process In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. <!-- #### Who are the annotators? [More Information Needed] --> ### Personal and Sensitive Information Though there are endangered species included in this data, exact locations are not provided and their safety is assured by their location within the preserve. ## Considerations for Using the Data <!-- ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] --> ### Other Known Limitations This data exhibits a long-tailed distribution due to the natural variation in frequency of the observed behaviors. ## Additional Information ### Authors * Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957 * Jenna Kline (The Ohio State University) * Michelle Ramirez (The Ohio State University) * Sam Stevens (The Ohio State University) * Alec Sheets (The Ohio State University) - ORCID: 0000-0002-3737-1484 * Reshma Ramesh Babu (The Ohio State University) - ORCID: 0000-0002-2517-5347 * Namrata Banerji (The Ohio State University) - ORCID: 0000-0001-6813-0010 * Elizabeth Campolongo (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0846-2413 * Matthew Thompson (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0583-8585 * Nina Van Tiel (Eidgenössische Technische Hochschule Zürich) - ORCID: 0000-0001-6393-5629 * Jackson Miliko (Mpala Research Centre) * Eduardo Bessa (Universidade de Brasília) - ORCID: 0000-0003-0606-5860 * Tanya Berger-Wolf (The Ohio State University) - ORCID: 0000-0001-7610-1412 * Daniel Rubenstein (Princeton University) - ORCID: 0000-0001-9049-5219 * Charles Stewart (Rensselaer Polytechnic Institute) ### Licensing Information This dataset is dedicated to the public domain for the benefit of scientific pursuits. We ask that you cite the dataset and journal paper using the below citations if you make use of it in your research. ### Citation Information #### Dataset ``` @misc{KABR_Data, author = {Kholiavchenko, Maksim and Kline, Jenna and Ramirez, Michelle and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and Miliko, Jackson and Bessa, Eduardo and Duporge, Isla and Berger-Wolf, Tanya and Rubenstein, Daniel and Stewart, Charles}, title = {KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos}, year = {2023}, url = {https://huggingface.co/datasets/imageomics/KABR}, doi = {10.57967/hf/1010}, publisher = {Hugging Face} } ``` #### Paper ``` @inproceedings{kholiavchenko2024kabr, title={KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos}, author={Kholiavchenko, Maksim and Kline, Jenna and Ramirez, Michelle and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and Miliko, Jackson and Bessa, Eduardo and Duporge, Isla and Berger-Wolf, Tanya and Rubenstein, Daniel and Stewart, Charles}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={31-40}, year={2024} } ``` ### Contributions The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
reciprocate/tinygsm_mixtral_8M	--- dataset_info: features: - name: question dtype: string - name: program dtype: string - name: result dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 10716696014 num_examples: 8000000 download_size: 3197472673 dataset_size: 10716696014 configs: - config_name: default data_files: - split: train path: data/train-* ---
rainbow/Andy_Lau	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6985835.0 num_examples: 16 download_size: 6986820 dataset_size: 6985835.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Andy_Lau" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-moral_scenarios-dev	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string splits: - name: dev num_bytes: 4507 num_examples: 5 download_size: 9481 dataset_size: 4507 --- # Dataset Card for "mmlu-moral_scenarios-dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Soma8622/kokkai_speech	--- license: mit --- # 概要 [データ取得元](https://kokkai.ndl.go.jp/api.html)
Lfu001/image-text	--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: negative_prompt dtype: string splits: - name: train num_bytes: 387510452.0 num_examples: 210 download_size: 387472246 dataset_size: 387510452.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
scene_parse_150	--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - bsd-3-clause multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended\|ade20k task_categories: - image-segmentation task_ids: - instance-segmentation paperswithcode_id: ade20k pretty_name: MIT Scene Parsing Benchmark tags: - scene-parsing dataset_info: - config_name: scene_parsing features: - name: image dtype: image - name: annotation dtype: image - name: scene_category dtype: class_label: names: '0': airport_terminal '1': art_gallery '2': badlands '3': ball_pit '4': bathroom '5': beach '6': bedroom '7': booth_indoor '8': botanical_garden '9': bridge '10': bullring '11': bus_interior '12': butte '13': canyon '14': casino_outdoor '15': castle '16': church_outdoor '17': closet '18': coast '19': conference_room '20': construction_site '21': corral '22': corridor '23': crosswalk '24': day_care_center '25': sand '26': elevator_interior '27': escalator_indoor '28': forest_road '29': gangplank '30': gas_station '31': golf_course '32': gymnasium_indoor '33': harbor '34': hayfield '35': heath '36': hoodoo '37': house '38': hunting_lodge_outdoor '39': ice_shelf '40': joss_house '41': kiosk_indoor '42': kitchen '43': landfill '44': library_indoor '45': lido_deck_outdoor '46': living_room '47': locker_room '48': market_outdoor '49': mountain_snowy '50': office '51': orchard '52': arbor '53': bookshelf '54': mews '55': nook '56': preserve '57': traffic_island '58': palace '59': palace_hall '60': pantry '61': patio '62': phone_booth '63': establishment '64': poolroom_home '65': quonset_hut_outdoor '66': rice_paddy '67': sandbox '68': shopfront '69': skyscraper '70': stone_circle '71': subway_interior '72': platform '73': supermarket '74': swimming_pool_outdoor '75': television_studio '76': indoor_procenium '77': train_railway '78': coral_reef '79': viaduct '80': wave '81': wind_farm '82': bottle_storage '83': abbey '84': access_road '85': air_base '86': airfield '87': airlock '88': airplane_cabin '89': airport '90': entrance '91': airport_ticket_counter '92': alcove '93': alley '94': amphitheater '95': amusement_arcade '96': amusement_park '97': anechoic_chamber '98': apartment_building_outdoor '99': apse_indoor '100': apse_outdoor '101': aquarium '102': aquatic_theater '103': aqueduct '104': arcade '105': arch '106': archaelogical_excavation '107': archive '108': basketball '109': football '110': hockey '111': performance '112': rodeo '113': soccer '114': armory '115': army_base '116': arrival_gate_indoor '117': arrival_gate_outdoor '118': art_school '119': art_studio '120': artists_loft '121': assembly_line '122': athletic_field_indoor '123': athletic_field_outdoor '124': atrium_home '125': atrium_public '126': attic '127': auditorium '128': auto_factory '129': auto_mechanics_indoor '130': auto_mechanics_outdoor '131': auto_racing_paddock '132': auto_showroom '133': backstage '134': backstairs '135': badminton_court_indoor '136': badminton_court_outdoor '137': baggage_claim '138': shop '139': exterior '140': balcony_interior '141': ballroom '142': bamboo_forest '143': bank_indoor '144': bank_outdoor '145': bank_vault '146': banquet_hall '147': baptistry_indoor '148': baptistry_outdoor '149': bar '150': barbershop '151': barn '152': barndoor '153': barnyard '154': barrack '155': baseball_field '156': basement '157': basilica '158': basketball_court_indoor '159': basketball_court_outdoor '160': bathhouse '161': batters_box '162': batting_cage_indoor '163': batting_cage_outdoor '164': battlement '165': bayou '166': bazaar_indoor '167': bazaar_outdoor '168': beach_house '169': beauty_salon '170': bedchamber '171': beer_garden '172': beer_hall '173': belfry '174': bell_foundry '175': berth '176': berth_deck '177': betting_shop '178': bicycle_racks '179': bindery '180': biology_laboratory '181': bistro_indoor '182': bistro_outdoor '183': bleachers_indoor '184': bleachers_outdoor '185': boardwalk '186': boat_deck '187': boathouse '188': bog '189': bomb_shelter_indoor '190': bookbindery '191': bookstore '192': bow_window_indoor '193': bow_window_outdoor '194': bowling_alley '195': box_seat '196': boxing_ring '197': breakroom '198': brewery_indoor '199': brewery_outdoor '200': brickyard_indoor '201': brickyard_outdoor '202': building_complex '203': building_facade '204': bullpen '205': burial_chamber '206': bus_depot_indoor '207': bus_depot_outdoor '208': bus_shelter '209': bus_station_indoor '210': bus_station_outdoor '211': butchers_shop '212': cabana '213': cabin_indoor '214': cabin_outdoor '215': cafeteria '216': call_center '217': campsite '218': campus '219': natural '220': urban '221': candy_store '222': canteen '223': car_dealership '224': backseat '225': frontseat '226': caravansary '227': cardroom '228': cargo_container_interior '229': airplane '230': boat '231': freestanding '232': carport_indoor '233': carport_outdoor '234': carrousel '235': casino_indoor '236': catacomb '237': cathedral_indoor '238': cathedral_outdoor '239': catwalk '240': cavern_indoor '241': cavern_outdoor '242': cemetery '243': chalet '244': chaparral '245': chapel '246': checkout_counter '247': cheese_factory '248': chemical_plant '249': chemistry_lab '250': chicken_coop_indoor '251': chicken_coop_outdoor '252': chicken_farm_indoor '253': chicken_farm_outdoor '254': childs_room '255': choir_loft_interior '256': church_indoor '257': circus_tent_indoor '258': circus_tent_outdoor '259': city '260': classroom '261': clean_room '262': cliff '263': booth '264': room '265': clock_tower_indoor '266': cloister_indoor '267': cloister_outdoor '268': clothing_store '269': coast_road '270': cockpit '271': coffee_shop '272': computer_room '273': conference_center '274': conference_hall '275': confessional '276': control_room '277': control_tower_indoor '278': control_tower_outdoor '279': convenience_store_indoor '280': convenience_store_outdoor '281': corn_field '282': cottage '283': cottage_garden '284': courthouse '285': courtroom '286': courtyard '287': covered_bridge_interior '288': crawl_space '289': creek '290': crevasse '291': library '292': cybercafe '293': dacha '294': dairy_indoor '295': dairy_outdoor '296': dam '297': dance_school '298': darkroom '299': delicatessen '300': dentists_office '301': department_store '302': departure_lounge '303': vegetation '304': desert_road '305': diner_indoor '306': diner_outdoor '307': dinette_home '308': vehicle '309': dining_car '310': dining_hall '311': dining_room '312': dirt_track '313': discotheque '314': distillery '315': ditch '316': dock '317': dolmen '318': donjon '319': doorway_indoor '320': doorway_outdoor '321': dorm_room '322': downtown '323': drainage_ditch '324': dress_shop '325': dressing_room '326': drill_rig '327': driveway '328': driving_range_indoor '329': driving_range_outdoor '330': drugstore '331': dry_dock '332': dugout '333': earth_fissure '334': editing_room '335': electrical_substation '336': elevated_catwalk '337': door '338': freight_elevator '339': elevator_lobby '340': elevator_shaft '341': embankment '342': embassy '343': engine_room '344': entrance_hall '345': escalator_outdoor '346': escarpment '347': estuary '348': excavation '349': exhibition_hall '350': fabric_store '351': factory_indoor '352': factory_outdoor '353': fairway '354': farm '355': fastfood_restaurant '356': fence '357': cargo_deck '358': ferryboat_indoor '359': passenger_deck '360': cultivated '361': wild '362': field_road '363': fire_escape '364': fire_station '365': firing_range_indoor '366': firing_range_outdoor '367': fish_farm '368': fishmarket '369': fishpond '370': fitting_room_interior '371': fjord '372': flea_market_indoor '373': flea_market_outdoor '374': floating_dry_dock '375': flood '376': florist_shop_indoor '377': florist_shop_outdoor '378': fly_bridge '379': food_court '380': football_field '381': broadleaf '382': needleleaf '383': forest_fire '384': forest_path '385': formal_garden '386': fort '387': fortress '388': foundry_indoor '389': foundry_outdoor '390': fountain '391': freeway '392': funeral_chapel '393': funeral_home '394': furnace_room '395': galley '396': game_room '397': garage_indoor '398': garage_outdoor '399': garbage_dump '400': gasworks '401': gate '402': gatehouse '403': gazebo_interior '404': general_store_indoor '405': general_store_outdoor '406': geodesic_dome_indoor '407': geodesic_dome_outdoor '408': ghost_town '409': gift_shop '410': glacier '411': glade '412': gorge '413': granary '414': great_hall '415': greengrocery '416': greenhouse_indoor '417': greenhouse_outdoor '418': grotto '419': guardhouse '420': gulch '421': gun_deck_indoor '422': gun_deck_outdoor '423': gun_store '424': hacienda '425': hallway '426': handball_court '427': hangar_indoor '428': hangar_outdoor '429': hardware_store '430': hat_shop '431': hatchery '432': hayloft '433': hearth '434': hedge_maze '435': hedgerow '436': heliport '437': herb_garden '438': highway '439': hill '440': home_office '441': home_theater '442': hospital '443': hospital_room '444': hot_spring '445': hot_tub_indoor '446': hot_tub_outdoor '447': hotel_outdoor '448': hotel_breakfast_area '449': hotel_room '450': hunting_lodge_indoor '451': hut '452': ice_cream_parlor '453': ice_floe '454': ice_skating_rink_indoor '455': ice_skating_rink_outdoor '456': iceberg '457': igloo '458': imaret '459': incinerator_indoor '460': incinerator_outdoor '461': industrial_area '462': industrial_park '463': inn_indoor '464': inn_outdoor '465': irrigation_ditch '466': islet '467': jacuzzi_indoor '468': jacuzzi_outdoor '469': jail_indoor '470': jail_outdoor '471': jail_cell '472': japanese_garden '473': jetty '474': jewelry_shop '475': junk_pile '476': junkyard '477': jury_box '478': kasbah '479': kennel_indoor '480': kennel_outdoor '481': kindergarden_classroom '482': kiosk_outdoor '483': kitchenette '484': lab_classroom '485': labyrinth_indoor '486': labyrinth_outdoor '487': lagoon '488': artificial '489': landing '490': landing_deck '491': laundromat '492': lava_flow '493': lavatory '494': lawn '495': lean-to '496': lecture_room '497': legislative_chamber '498': levee '499': library_outdoor '500': lido_deck_indoor '501': lift_bridge '502': lighthouse '503': limousine_interior '504': liquor_store_indoor '505': liquor_store_outdoor '506': loading_dock '507': lobby '508': lock_chamber '509': loft '510': lookout_station_indoor '511': lookout_station_outdoor '512': lumberyard_indoor '513': lumberyard_outdoor '514': machine_shop '515': manhole '516': mansion '517': manufactured_home '518': market_indoor '519': marsh '520': martial_arts_gym '521': mastaba '522': maternity_ward '523': mausoleum '524': medina '525': menhir '526': mesa '527': mess_hall '528': mezzanine '529': military_hospital '530': military_hut '531': military_tent '532': mine '533': mineshaft '534': mini_golf_course_indoor '535': mini_golf_course_outdoor '536': mission '537': dry '538': water '539': mobile_home '540': monastery_indoor '541': monastery_outdoor '542': moon_bounce '543': moor '544': morgue '545': mosque_indoor '546': mosque_outdoor '547': motel '548': mountain '549': mountain_path '550': mountain_road '551': movie_theater_indoor '552': movie_theater_outdoor '553': mudflat '554': museum_indoor '555': museum_outdoor '556': music_store '557': music_studio '558': misc '559': natural_history_museum '560': naval_base '561': newsroom '562': newsstand_indoor '563': newsstand_outdoor '564': nightclub '565': nuclear_power_plant_indoor '566': nuclear_power_plant_outdoor '567': nunnery '568': nursery '569': nursing_home '570': oasis '571': oast_house '572': observatory_indoor '573': observatory_outdoor '574': observatory_post '575': ocean '576': office_building '577': office_cubicles '578': oil_refinery_indoor '579': oil_refinery_outdoor '580': oilrig '581': operating_room '582': optician '583': organ_loft_interior '584': orlop_deck '585': ossuary '586': outcropping '587': outhouse_indoor '588': outhouse_outdoor '589': overpass '590': oyster_bar '591': oyster_farm '592': acropolis '593': aircraft_carrier_object '594': amphitheater_indoor '595': archipelago '596': questionable '597': assembly_hall '598': assembly_plant '599': awning_deck '600': back_porch '601': backdrop '602': backroom '603': backstage_outdoor '604': backstairs_indoor '605': backwoods '606': ballet '607': balustrade '608': barbeque '609': basin_outdoor '610': bath_indoor '611': bath_outdoor '612': bathhouse_outdoor '613': battlefield '614': bay '615': booth_outdoor '616': bottomland '617': breakfast_table '618': bric-a-brac '619': brooklet '620': bubble_chamber '621': buffet '622': bulkhead '623': bunk_bed '624': bypass '625': byroad '626': cabin_cruiser '627': cargo_helicopter '628': cellar '629': chair_lift '630': cocktail_lounge '631': corner '632': country_house '633': country_road '634': customhouse '635': dance_floor '636': deck-house_boat_deck_house '637': deck-house_deck_house '638': dining_area '639': diving_board '640': embrasure '641': entranceway_indoor '642': entranceway_outdoor '643': entryway_outdoor '644': estaminet '645': farm_building '646': farmhouse '647': feed_bunk '648': field_house '649': field_tent_indoor '650': field_tent_outdoor '651': fire_trench '652': fireplace '653': flashflood '654': flatlet '655': floating_dock '656': flood_plain '657': flowerbed '658': flume_indoor '659': flying_buttress '660': foothill '661': forecourt '662': foreshore '663': front_porch '664': garden '665': gas_well '666': glen '667': grape_arbor '668': grove '669': guardroom '670': guesthouse '671': gymnasium_outdoor '672': head_shop '673': hen_yard '674': hillock '675': housing_estate '676': housing_project '677': howdah '678': inlet '679': insane_asylum '680': outside '681': juke_joint '682': jungle '683': kraal '684': laboratorywet '685': landing_strip '686': layby '687': lean-to_tent '688': loge '689': loggia_outdoor '690': lower_deck '691': luggage_van '692': mansard '693': meadow '694': meat_house '695': megalith '696': mens_store_outdoor '697': mental_institution_indoor '698': mental_institution_outdoor '699': military_headquarters '700': millpond '701': millrace '702': natural_spring '703': nursing_home_outdoor '704': observation_station '705': open-hearth_furnace '706': operating_table '707': outbuilding '708': palestra '709': parkway '710': patio_indoor '711': pavement '712': pawnshop_outdoor '713': pinetum '714': piste_road '715': pizzeria_outdoor '716': powder_room '717': pumping_station '718': reception_room '719': rest_stop '720': retaining_wall '721': rift_valley '722': road '723': rock_garden '724': rotisserie '725': safari_park '726': salon '727': saloon '728': sanatorium '729': science_laboratory '730': scrubland '731': scullery '732': seaside '733': semidesert '734': shelter '735': shelter_deck '736': shelter_tent '737': shore '738': shrubbery '739': sidewalk '740': snack_bar '741': snowbank '742': stage_set '743': stall '744': stateroom '745': store '746': streetcar_track '747': student_center '748': study_hall '749': sugar_refinery '750': sunroom '751': supply_chamber '752': t-bar_lift '753': tannery '754': teahouse '755': threshing_floor '756': ticket_window_indoor '757': tidal_basin '758': tidal_river '759': tiltyard '760': tollgate '761': tomb '762': tract_housing '763': trellis '764': truck_stop '765': upper_balcony '766': vestibule '767': vinery '768': walkway '769': war_room '770': washroom '771': water_fountain '772': water_gate '773': waterscape '774': waterway '775': wetland '776': widows_walk_indoor '777': windstorm '778': packaging_plant '779': pagoda '780': paper_mill '781': park '782': parking_garage_indoor '783': parking_garage_outdoor '784': parking_lot '785': parlor '786': particle_accelerator '787': party_tent_indoor '788': party_tent_outdoor '789': pasture '790': pavilion '791': pawnshop '792': pedestrian_overpass_indoor '793': penalty_box '794': pet_shop '795': pharmacy '796': physics_laboratory '797': piano_store '798': picnic_area '799': pier '800': pig_farm '801': pilothouse_indoor '802': pilothouse_outdoor '803': pitchers_mound '804': pizzeria '805': planetarium_indoor '806': planetarium_outdoor '807': plantation_house '808': playground '809': playroom '810': plaza '811': podium_indoor '812': podium_outdoor '813': police_station '814': pond '815': pontoon_bridge '816': poop_deck '817': porch '818': portico '819': portrait_studio '820': postern '821': power_plant_outdoor '822': print_shop '823': priory '824': promenade '825': promenade_deck '826': pub_indoor '827': pub_outdoor '828': pulpit '829': putting_green '830': quadrangle '831': quicksand '832': quonset_hut_indoor '833': racecourse '834': raceway '835': raft '836': railroad_track '837': railway_yard '838': rainforest '839': ramp '840': ranch '841': ranch_house '842': reading_room '843': reception '844': recreation_room '845': rectory '846': recycling_plant_indoor '847': refectory '848': repair_shop '849': residential_neighborhood '850': resort '851': rest_area '852': restaurant '853': restaurant_kitchen '854': restaurant_patio '855': restroom_indoor '856': restroom_outdoor '857': revolving_door '858': riding_arena '859': river '860': road_cut '861': rock_arch '862': roller_skating_rink_indoor '863': roller_skating_rink_outdoor '864': rolling_mill '865': roof '866': roof_garden '867': root_cellar '868': rope_bridge '869': roundabout '870': roundhouse '871': rubble '872': ruin '873': runway '874': sacristy '875': salt_plain '876': sand_trap '877': sandbar '878': sauna '879': savanna '880': sawmill '881': schoolhouse '882': schoolyard '883': science_museum '884': scriptorium '885': sea_cliff '886': seawall '887': security_check_point '888': server_room '889': sewer '890': sewing_room '891': shed '892': shipping_room '893': shipyard_outdoor '894': shoe_shop '895': shopping_mall_indoor '896': shopping_mall_outdoor '897': shower '898': shower_room '899': shrine '900': signal_box '901': sinkhole '902': ski_jump '903': ski_lodge '904': ski_resort '905': ski_slope '906': sky '907': skywalk_indoor '908': skywalk_outdoor '909': slum '910': snowfield '911': massage_room '912': mineral_bath '913': spillway '914': sporting_goods_store '915': squash_court '916': stable '917': baseball '918': stadium_outdoor '919': stage_indoor '920': stage_outdoor '921': staircase '922': starting_gate '923': steam_plant_outdoor '924': steel_mill_indoor '925': storage_room '926': storm_cellar '927': street '928': strip_mall '929': strip_mine '930': student_residence '931': submarine_interior '932': sun_deck '933': sushi_bar '934': swamp '935': swimming_hole '936': swimming_pool_indoor '937': synagogue_indoor '938': synagogue_outdoor '939': taxistand '940': taxiway '941': tea_garden '942': tearoom '943': teashop '944': television_room '945': east_asia '946': mesoamerican '947': south_asia '948': western '949': tennis_court_indoor '950': tennis_court_outdoor '951': tent_outdoor '952': terrace_farm '953': indoor_round '954': indoor_seats '955': theater_outdoor '956': thriftshop '957': throne_room '958': ticket_booth '959': tobacco_shop_indoor '960': toll_plaza '961': tollbooth '962': topiary_garden '963': tower '964': town_house '965': toyshop '966': track_outdoor '967': trading_floor '968': trailer_park '969': train_interior '970': train_station_outdoor '971': station '972': tree_farm '973': tree_house '974': trench '975': trestle_bridge '976': tundra '977': rail_indoor '978': rail_outdoor '979': road_indoor '980': road_outdoor '981': turkish_bath '982': ocean_deep '983': ocean_shallow '984': utility_room '985': valley '986': van_interior '987': vegetable_garden '988': velodrome_indoor '989': velodrome_outdoor '990': ventilation_shaft '991': veranda '992': vestry '993': veterinarians_office '994': videostore '995': village '996': vineyard '997': volcano '998': volleyball_court_indoor '999': volleyball_court_outdoor '1000': voting_booth '1001': waiting_room '1002': walk_in_freezer '1003': warehouse_indoor '1004': warehouse_outdoor '1005': washhouse_indoor '1006': washhouse_outdoor '1007': watchtower '1008': water_mill '1009': water_park '1010': water_tower '1011': water_treatment_plant_indoor '1012': water_treatment_plant_outdoor '1013': block '1014': cascade '1015': cataract '1016': fan '1017': plunge '1018': watering_hole '1019': weighbridge '1020': wet_bar '1021': wharf '1022': wheat_field '1023': whispering_gallery '1024': widows_walk_interior '1025': windmill '1026': window_seat '1027': barrel_storage '1028': winery '1029': witness_stand '1030': woodland '1031': workroom '1032': workshop '1033': wrestling_ring_indoor '1034': wrestling_ring_outdoor '1035': yard '1036': youth_hostel '1037': zen_garden '1038': ziggurat '1039': zoo '1040': forklift '1041': hollow '1042': hutment '1043': pueblo '1044': vat '1045': perfume_shop '1046': steel_mill_outdoor '1047': orchestra_pit '1048': bridle_path '1049': lyceum '1050': one-way_street '1051': parade_ground '1052': pump_room '1053': recycling_plant_outdoor '1054': chuck_wagon splits: - name: train num_bytes: 8468086 num_examples: 20210 - name: test num_bytes: 744607 num_examples: 3352 - name: validation num_bytes: 838032 num_examples: 2000 download_size: 1179202534 dataset_size: 10050725 - config_name: instance_segmentation features: - name: image dtype: image - name: annotation dtype: image splits: - name: train num_bytes: 862611544 num_examples: 20210 - name: test num_bytes: 212493928 num_examples: 3352 - name: validation num_bytes: 87502294 num_examples: 2000 download_size: 1197393920 dataset_size: 1162607766 --- # Dataset Card for MIT Scene Parsing Benchmark ## 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: [MIT Scene Parsing Benchmark homepage](http://sceneparsing.csail.mit.edu/) - Repository: [Scene Parsing repository (Caffe/Torch7)](https://github.com/CSAILVision/sceneparsing),[Scene Parsing repository (PyTorch)](https://github.com/CSAILVision/semantic-segmentation-pytorch) and [Instance Segmentation repository](https://github.com/CSAILVision/placeschallenge/tree/master/instancesegmentation) - Paper: [Scene Parsing through ADE20K Dataset](http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf) and [Semantic Understanding of Scenes through ADE20K Dataset](https://arxiv.org/abs/1608.05442) - Leaderboard: [MIT Scene Parsing Benchmark leaderboard](http://sceneparsing.csail.mit.edu/#:~:text=twice%20per%20week.-,leaderboard,-Organizers) - Point of Contact: [Bolei Zhou](mailto:bzhou@ie.cuhk.edu.hk) ### Dataset Summary Scene parsing is the task of segmenting and parsing an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing. The data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts. Specifically, the benchmark is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing. There are in total 150 semantic categories included for evaluation, which include e.g. sky, road, grass, and discrete objects like person, car, bed. Note that there are non-uniform distribution of objects occuring in the images, mimicking a more natural object occurrence in daily scene. The goal of this benchmark is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bedThis benchamark is similar to semantic segmentation tasks in COCO and Pascal Dataset, but the data is more scene-centric and with a diverse range of object categories. The data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts. ### Supported Tasks and Leaderboards - `scene-parsing`: The goal of this task is to segment the whole image densely into semantic classes (image regions), where each pixel is assigned a class label such as the region of tree and the region of building. [The leaderboard](http://sceneparsing.csail.mit.edu/#:~:text=twice%20per%20week.-,leaderboard,-Organizers) for this task ranks the models by considering the mean of the pixel-wise accuracy and class-wise IoU as the final score. Pixel-wise accuracy indicates the ratio of pixels which are correctly predicted, while class-wise IoU indicates the Intersection of Union of pixels averaged over all the 150 semantic categories. Refer to the [Development Kit](https://github.com/CSAILVision/sceneparsing) for the detail. - `instance-segmentation`: The goal of this task is to detect the object instances inside an image and further generate the precise segmentation masks of the objects. Its difference compared to the task of scene parsing is that in scene parsing there is no instance concept for the segmented regions, instead in instance segmentation if there are three persons in the scene, the network is required to segment each one of the person regions. This task doesn't have an active leaderboard. The performance of the instance segmentation algorithms is evaluated by Average Precision (AP, or mAP), following COCO evaluation metrics. For each image, at most 255 top-scoring instance masks are taken across all categories. Each instance mask prediction is only considered if its IoU with ground truth is above a certain threshold. There are 10 IoU thresholds of 0.50:0.05:0.95 for evaluation. The final AP is averaged across 10 IoU thresholds and 100 categories. You can refer to COCO evaluation page for more explanation: http://mscoco.org/dataset/#detections-eval ### Languages English. ## Dataset Structure ### Data Instances A data point comprises an image and its annotation mask, which is `None` in the testing set. The `scene_parsing` configuration has an additional `scene_category` field. #### `scene_parsing` ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=683x512 at 0x1FF32A3EDA0>, 'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=683x512 at 0x1FF32E5B978>, 'scene_category': 0 } ``` #### `instance_segmentation` ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256 at 0x20B51B5C400>, 'annotation': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256 at 0x20B57051B38> } ``` ### Data Fields #### `scene_parsing` - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`. - `annotation`: A `PIL.Image.Image` object containing the annotation mask. - `scene_category`: A scene category for the image (e.g. `airport_terminal`, `canyon`, `mobile_home`). > Note: annotation masks contain labels ranging from 0 to 150, where 0 refers to "other objects". Those pixels are not considered in the official evaluation. Refer to [this file](https://github.com/CSAILVision/sceneparsing/blob/master/objectInfo150.csv) for the information about the labels of the 150 semantic categories, including indices, pixel ratios and names. #### `instance_segmentation` - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, i.e. `dataset[0]["image"]` should always be preferred over `dataset["image"][0]`. - `annotation`: A `PIL.Image.Image` object containing the annotation mask. > Note: in the instance annotation masks, the R(ed) channel encodes category ID, and the G(reen) channel encodes instance ID. Each object instance has a unique instance ID regardless of its category ID. In the dataset, all images have <256 object instances. Refer to [this file (train split)](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/instanceInfo100_train.txt) and to [this file (validation split)](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/instanceInfo100_val.txt) for the information about the labels of the 100 semantic categories. To find the mapping between the semantic categories for `instance_segmentation` and `scene_parsing`, refer to [this file](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/categoryMapping.txt). ### Data Splits The data is split into training, test and validation set. The training data contains 20210 images, the testing data contains 3352 images and the validation data contains 2000 images. ## Dataset Creation ### Curation Rationale The rationale from the paper for the ADE20K dataset from which this benchmark originates: > Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. > The motivation of this work is to collect a dataset that has densely annotated images (every pixel has a semantic label) with a large and an unrestricted open vocabulary. The images in our dataset are manually segmented in great detail, covering a diverse set of scenes, object and object part categories. The challenge for collecting such annotations is finding reliable annotators, as well as the fact that labeling is difficult if the class list is not defined in advance. On the other hand, open vocabulary naming also suffers from naming inconsistencies across different annotators. In contrast, our dataset was annotated by a single expert annotator, providing extremely detailed and exhaustive image annotations. On average, our annotator labeled 29 annotation segments per image, compared to the 16 segments per image labeled by external annotators (like workers from Amazon Mechanical Turk). Furthermore, the data consistency and quality are much higher than that of external annotators. ### Source Data #### Initial Data Collection and Normalization Images come from the LabelMe, SUN datasets, and Places and were selected to cover the 900 scene categories defined in the SUN database. This benchmark was built by selecting the top 150 objects ranked by their total pixel ratios from the ADE20K dataset. As the original images in the ADE20K dataset have various sizes, for simplicity those large-sized images were rescaled to make their minimum heights or widths as 512. Among the 150 objects, there are 35 stuff classes (i.e., wall, sky, road) and 115 discrete objects (i.e., car, person, table). The annotated pixels of the 150 objects occupy 92.75% of all the pixels in the dataset, where the stuff classes occupy 60.92%, and discrete objects occupy 31.83%. #### Who are the source language producers? The same as in the LabelMe, SUN datasets, and Places datasets. ### Annotations #### Annotation process Annotation process for the ADE20K dataset: > Image Annotation. For our dataset, we are interested in having a diverse set of scenes with dense annotations of all the objects present. Images come from the LabelMe, SUN datasets, and Places and were selected to cover the 900 scene categories defined in the SUN database. Images were annotated by a single expert worker using the LabelMe interface. Fig. 2 shows a snapshot of the annotation interface and one fully segmented image. The worker provided three types of annotations: object segments with names, object parts, and attributes. All object instances are segmented independently so that the dataset could be used to train and evaluate detection or segmentation algorithms. Datasets such as COCO, Pascal or Cityscape start by defining a set of object categories of interest. However, when labeling all the objects in a scene, working with a predefined list of objects is not possible as new categories appear frequently (see fig. 5.d). Here, the annotator created a dictionary of visual concepts where new classes were added constantly to ensure consistency in object naming. Object parts are associated with object instances. Note that parts can have parts too, and we label these associations as well. For example, the ‘rim’ is a part of a ‘wheel’, which in turn is part of a ‘car’. A ‘knob’ is a part of a ‘door’ that can be part of a ‘cabinet’. The total part hierarchy has a depth of 3. The object and part hierarchy is in the supplementary materials. > Annotation Consistency. Defining a labeling protocol is relatively easy when the labeling task is restricted to a fixed list of object classes, however it becomes challenging when the class list is openended. As the goal is to label all the objects within each image, the list of classes grows unbounded. >Many object classes appear only a few times across the entire collection of images. However, those rare >object classes cannot be ignored as they might be important elements for the interpretation of the scene. >Labeling in these conditions becomes difficult because we need to keep a growing list of all the object >classes in order to have a consistent naming across the entire dataset. Despite the annotator’s best effort, >the process is not free of noise. To analyze the annotation consistency we took a subset of 61 randomly >chosen images from the validation set, then asked our annotator to annotate them again (there is a time difference of six months). One expects that there are some differences between the two annotations. A few examples are shown in Fig 3. On average, 82.4% of the pixels got the same label. The remaining 17.6% of pixels had some errors for which we grouped into three error types as follows: > > • Segmentation quality: Variations in the quality of segmentation and outlining of the object boundary. One typical source of error arises when segmenting complex objects such as buildings and trees, which can be segmented with different degrees of precision. 5.7% of the pixels had this type of error. > > • Object naming: Differences in object naming (due to ambiguity or similarity between concepts, for instance calling a big car a ‘car’ in one segmentation and a ‘truck’ in the another one, or a ‘palm tree’ a‘tree’. 6.0% of the pixels had naming issues. These errors can be reduced by defining a very precise terminology, but this becomes much harder with a large growing vocabulary. > > • Segmentation quantity: Missing objects in one of the two segmentations. There is a very large number of objects in each image and some images might be annotated more thoroughly than others. For example, in the third column of Fig 3 the annotator missed some small objects in different annotations. 5.9% of the pixels are due to missing labels. A similar issue existed in segmentation datasets such as the Berkeley Image segmentation dataset. > > The median error values for the three error types are: 4.8%, 0.3% and 2.6% showing that the mean value is dominated by a few images, and that the most common type of error is segmentation quality. To further compare the annotation done by our single expert annotator and the AMT-like annotators, 20 images from the validation set are annotated by two invited external annotators, both with prior experience in image labeling. The first external annotator had 58.5% of inconsistent pixels compared to the segmentation provided by our annotator, and the second external annotator had 75% of the inconsistent pixels. Many of these inconsistencies are due to the poor quality of the segmentations provided by external annotators (as it has been observed with AMT which requires multiple verification steps for quality control). For the best external annotator (the first one), 7.9% of pixels have inconsistent segmentations (just slightly worse than our annotator), 14.9% have inconsistent object naming and 35.8% of the pixels correspond to missing objects, which is due to the much smaller number of objects annotated by the external annotator in comparison with the ones annotated by our expert annotator. The external annotators labeled on average 16 segments per image while our annotator provided 29 segments per image. #### Who are the annotators? Three expert annotators and the AMT-like annotators. ### 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 Refer to the `Annotation Consistency` subsection of `Annotation Process`. ## Additional Information ### Dataset Curators Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso and Antonio Torralba. ### Licensing Information The MIT Scene Parsing Benchmark dataset is licensed under a [BSD 3-Clause License](https://github.com/CSAILVision/sceneparsing/blob/master/LICENSE). ### Citation Information ```bibtex @inproceedings{zhou2017scene, title={Scene Parsing through ADE20K Dataset}, author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2017} } @article{zhou2016semantic, title={Semantic understanding of scenes through the ade20k dataset}, author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio}, journal={arXiv preprint arXiv:1608.05442}, year={2016} } ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
KentoTsu/Bets	--- license: openrail ---
Christoph911/German-legal-SQuAD	--- license: mit ---
EleutherAI/quirky_addition_raw	--- dataset_info: features: - name: id dtype: string - name: template_args struct: - name: character dtype: string - name: op1 dtype: int64 - name: op2 dtype: int64 - name: result dtype: int64 - name: character dtype: string - name: label dtype: bool - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: difficulty_quantile dtype: float64 splits: - name: train num_bytes: 26256000 num_examples: 384000 - name: validation num_bytes: 547000 num_examples: 8000 - name: test num_bytes: 547000 num_examples: 8000 download_size: 13465330 dataset_size: 27350000 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
AdapterOcean/code_instructions_standardized_cluster_10_std	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 8041456 num_examples: 11514 download_size: 3654425 dataset_size: 8041456 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_10_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Cheetor1996/Natsu_Hyuga	--- license: cc-by-2.0 language: - en tags: - art --- Natsu Hyuga from Kansen 5 - Trained with Anime (full-final-pruned) model - Works best with ALL, MIDD, OUTD, and OUTALL LoRA weight blocks, and with 0.4-0.9 weights.
Intuit-GenSRF/es_mental_health_counseling	--- dataset_info: features: - name: Context dtype: string - name: Response dtype: string - name: split dtype: string - name: text dtype: string - name: text_spanish dtype: string splits: - name: train num_bytes: 13763461 num_examples: 3512 download_size: 7425319 dataset_size: 13763461 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "es_mental_health_counseling" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_ConvexAI__BurningBruce-005	--- pretty_name: Evaluation run of ConvexAI/BurningBruce-005 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ConvexAI/BurningBruce-005](https://huggingface.co/ConvexAI/BurningBruce-005)\ \ 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-005\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-02T18:58:34.137305](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__BurningBruce-005/blob/main/results_2024-02-02T18-58-34.137305.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\": 0.6536170901380703,\n\ \ \"acc_stderr\": 0.032028038336707275,\n \"acc_norm\": 0.6528681277337212,\n\ \ \"acc_norm_stderr\": 0.032697450933548394,\n \"mc1\": 0.543451652386781,\n\ \ \"mc1_stderr\": 0.017437280953183688,\n \"mc2\": 0.6726501530582988,\n\ \ \"mc2_stderr\": 0.015249067039770463\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6945392491467577,\n \"acc_stderr\": 0.013460080478002507,\n\ \ \"acc_norm\": 0.7201365187713311,\n \"acc_norm_stderr\": 0.013119040897725922\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7117108145787692,\n\ \ \"acc_stderr\": 0.004520406331084042,\n \"acc_norm\": 0.8830910177255527,\n\ \ \"acc_norm_stderr\": 0.003206551283257396\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.66,\n\ \ \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \ \ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7283018867924528,\n \"acc_stderr\": 0.027377706624670713,\n\ \ \"acc_norm\": 0.7283018867924528,\n \"acc_norm_stderr\": 0.027377706624670713\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness\|hendrycksTest-college_chemistry\|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_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\": 0.29,\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\": 0.6878612716763006,\n\ \ \"acc_stderr\": 0.035331333893236574,\n \"acc_norm\": 0.6878612716763006,\n\ \ \"acc_norm_stderr\": 0.035331333893236574\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\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\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\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\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7903225806451613,\n\ \ \"acc_stderr\": 0.023157879349083522,\n \"acc_norm\": 0.7903225806451613,\n\ \ \"acc_norm_stderr\": 0.023157879349083522\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652456,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652456\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886786,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886786\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.034076320938540516,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.034076320938540516\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\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\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8846153846153846,\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.43798882681564244,\n\ \ \"acc_stderr\": 0.016593394227564843,\n \"acc_norm\": 0.43798882681564244,\n\ \ \"acc_norm_stderr\": 0.016593394227564843\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.025670259242188936,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.025670259242188936\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\"\ : 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"\ acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4680573663624511,\n\ \ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\ \ \"acc_norm_stderr\": 0.012744149704869649\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6666666666666666,\n \"acc_stderr\": 0.019070985589687495,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.019070985589687495\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\": 0.028123429335142773,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169146,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169146\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.543451652386781,\n\ \ \"mc1_stderr\": 0.017437280953183688,\n \"mc2\": 0.6726501530582988,\n\ \ \"mc2_stderr\": 0.015249067039770463\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.010470796496781096\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7149355572403336,\n \ \ \"acc_stderr\": 0.012435042334904004\n }\n}\n```" repo_url: https://huggingface.co/ConvexAI/BurningBruce-005 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_58_34.137305 path: - '/details_harness\|arc:challenge\|25_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-02T18-58-34.137305.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|gsm8k\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hellaswag\|10_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T18-58-34.137305.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-02T18-58-34.137305.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '/details_harness\|winogrande\|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-02T18-58-34.137305.parquet' - config_name: results data_files: - split: 2024_02_02T18_58_34.137305 path: - results_2024-02-02T18-58-34.137305.parquet - split: latest path: - results_2024-02-02T18-58-34.137305.parquet --- # Dataset Card for Evaluation run of ConvexAI/BurningBruce-005 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ConvexAI/BurningBruce-005](https://huggingface.co/ConvexAI/BurningBruce-005) 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-005", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-02T18:58:34.137305](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__BurningBruce-005/blob/main/results_2024-02-02T18-58-34.137305.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": 0.6536170901380703, "acc_stderr": 0.032028038336707275, "acc_norm": 0.6528681277337212, "acc_norm_stderr": 0.032697450933548394, "mc1": 0.543451652386781, "mc1_stderr": 0.017437280953183688, "mc2": 0.6726501530582988, "mc2_stderr": 0.015249067039770463 }, "harness\|arc:challenge\|25": { "acc": 0.6945392491467577, "acc_stderr": 0.013460080478002507, "acc_norm": 0.7201365187713311, "acc_norm_stderr": 0.013119040897725922 }, "harness\|hellaswag\|10": { "acc": 0.7117108145787692, "acc_stderr": 0.004520406331084042, "acc_norm": 0.8830910177255527, "acc_norm_stderr": 0.003206551283257396 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7283018867924528, "acc_stderr": 0.027377706624670713, "acc_norm": 0.7283018867924528, "acc_norm_stderr": 0.027377706624670713 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.0358687928008034, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.0358687928008034 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "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": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6878612716763006, "acc_stderr": 0.035331333893236574, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.035331333893236574 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "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": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "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": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083522, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083522 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267042, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267042 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3296296296296296, "acc_stderr": 0.02866120111652456, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.02866120111652456 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886786, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886786 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8440366972477065, "acc_stderr": 0.01555580271359017, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.01555580271359017 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.034076320938540516, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.034076320938540516 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8431372549019608, "acc_stderr": 0.02552472232455335, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455335 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.02595502084162113, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.02595502084162113 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7557251908396947, "acc_stderr": 0.037683359597287434, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.037683359597287434 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "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": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371803, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371803 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.02378620325550829, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.02378620325550829 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.43798882681564244, "acc_stderr": 0.016593394227564843, "acc_norm": 0.43798882681564244, "acc_norm_stderr": 0.016593394227564843 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7138263665594855, "acc_stderr": 0.025670259242188936, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.025670259242188936 }, "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": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4680573663624511, "acc_stderr": 0.012744149704869649, "acc_norm": 0.4680573663624511, "acc_norm_stderr": 0.012744149704869649 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.019070985589687495, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.019070985589687495 }, "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": 0.028123429335142773, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142773 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169146, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169146 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.543451652386781, "mc1_stderr": 0.017437280953183688, "mc2": 0.6726501530582988, "mc2_stderr": 0.015249067039770463 }, "harness\|winogrande\|5": { "acc": 0.8334648776637726, "acc_stderr": 0.010470796496781096 }, "harness\|gsm8k\|5": { "acc": 0.7149355572403336, "acc_stderr": 0.012435042334904004 } } ``` ## 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]
pradeep239/philp_plain_5k	--- license: mit dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 882548358.845 num_examples: 1873 - name: validation num_bytes: 107622995.0 num_examples: 220 - name: test num_bytes: 53224252.0 num_examples: 111 download_size: 771789438 dataset_size: 1043395605.845 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
fathyshalab/reklamation24_schoenheit-wellness-intent	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 178584 num_examples: 397 - name: test num_bytes: 47435 num_examples: 100 download_size: 127871 dataset_size: 226019 --- # Dataset Card for "reklamation24_schoenheit-wellness-intent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-machine_learning-rule-neg	--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 34262 num_examples: 112 download_size: 19343 dataset_size: 34262 --- # Dataset Card for "mmlu-machine_learning-rule-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
denizzhansahin/Turkish_News-2024	--- dataset_info: features: - name: 'Unnamed: 0.2' dtype: int64 - name: Baslik dtype: string - name: Ozet dtype: string - name: Kategori dtype: string - name: Link dtype: string - name: Icerik dtype: string - name: 'Unnamed: 0' dtype: float64 splits: - name: train num_bytes: 49152035.39270457 num_examples: 19170 - name: validation num_bytes: 21068454.60729543 num_examples: 8217 download_size: 40617048 dataset_size: 70220490.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
0x-YuAN/source	--- dataset_info: features: - name: reason dtype: string - name: self_comment dtype: string - name: other_comment dtype: string - name: relatedIssues list: - name: issueRef dtype: string - name: lawName dtype: string splits: - name: train num_bytes: 1975024677 num_examples: 234054 download_size: 553769254 dataset_size: 1975024677 --- # Dataset Card for "source" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Shularp/350k_dataset_health_ar_en_th	--- dataset_info: features: - name: ar dtype: string - name: en dtype: string - name: th dtype: string splits: - name: validation num_bytes: 4370651 num_examples: 10078 - name: test num_bytes: 4378778 num_examples: 10108 - name: train num_bytes: 122924727 num_examples: 268888 download_size: 70750385 dataset_size: 131674156 --- # Dataset Card for "350k_dataset_health_ar_en_th" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
r-three/Phatgoose_flanv2_offline	--- license: mit ---
Codec-SUPERB/quesst14_all_synth	--- configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 8000 - name: id dtype: string splits: - name: original num_bytes: 1368882918.0 num_examples: 13607 - name: academicodec_hifi_16k_320d num_bytes: 2733824255.0 num_examples: 13607 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 2733824255.0 num_examples: 13607 - name: academicodec_hifi_24k_320d num_bytes: 4100996735.0 num_examples: 13607 - name: audiodec_24k_320d num_bytes: 4107921615.0 num_examples: 13607 - name: dac_16k num_bytes: 2736769119.0 num_examples: 13607 - name: dac_24k num_bytes: 4104632271.0 num_examples: 13607 - name: dac_44k num_bytes: 7541396965.0 num_examples: 13607 - name: encodec_24k_12bps num_bytes: 4104632271.0 num_examples: 13607 - name: encodec_24k_1_5bps num_bytes: 4104632271.0 num_examples: 13607 - name: encodec_24k_24bps num_bytes: 4104632271.0 num_examples: 13607 - name: encodec_24k_3bps num_bytes: 4104632271.0 num_examples: 13607 - name: encodec_24k_6bps num_bytes: 4104632271.0 num_examples: 13607 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 2736757881.0 num_examples: 13607 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 2736757881.0 num_examples: 13607 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2737231757.0 num_examples: 13607 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 2737231757.0 num_examples: 13607 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2737231757.0 num_examples: 13607 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2737231757.0 num_examples: 13607 - name: speech_tokenizer_16k num_bytes: 2740983853.0 num_examples: 13607 download_size: 18905736290 dataset_size: 69114836131.0 --- # Dataset Card for "quesst14_all_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FaalSa/dataE	--- dataset_info: features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: item_id dtype: string - name: feat_static_cat sequence: uint64 splits: - name: train num_bytes: 57629 num_examples: 1 - name: validation num_bytes: 58109 num_examples: 1 - name: test num_bytes: 58589 num_examples: 1 download_size: 12910 dataset_size: 174327 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
Maikin023/piuvoz	--- license: openrail ---
tollefj/sickr-sts-NOB	--- license: cc-by-4.0 --- # Translated STS dataset to Norwegian Bokmål Machine translated using the No language left behind model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B
davanstrien/model_cards_with_readmes_sections	--- dataset_info: features: - name: license dtype: string - name: tags dtype: string - name: is_nc dtype: bool - name: readme_section dtype: string - name: hash dtype: string splits: - name: train num_bytes: 28801782.8572217 num_examples: 32124 download_size: 13668782 dataset_size: 28801782.8572217 --- # Dataset Card for "model_cards_with_readmes_sections" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
huggingartists/scriptonite	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/scriptonite" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 1.251394 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/411d50392aef867fe0e9dd55a074ecfb.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/scriptonite"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Скриптонит (Scriptonite)</div> <a href="https://genius.com/artists/scriptonite"> <div style="text-align: center; font-size: 14px;">@scriptonite</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/scriptonite). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/scriptonite") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|367\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/scriptonite") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
HuggingFaceH4/ultrachat_200k	--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: \| train_sft \| test_sft \| train_gen \| test_gen \| \|:-------:\|:-----------:\|:-----:\| :-----:\| \| 207865 \| 23110 \| 256032 \| 28304 \| The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` You may also wish to cite the Zephyr 7B technical report: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
liuyanchen1015/MULTI_VALUE_sst2_comparative_more_and	--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 3080 num_examples: 19 - name: test num_bytes: 6036 num_examples: 38 - name: train num_bytes: 73392 num_examples: 631 download_size: 35653 dataset_size: 82508 --- # Dataset Card for "MULTI_VALUE_sst2_comparative_more_and" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FINNUMBER/FINCH_TRAIN_QA_1200_per400_NEWFORMAT	--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5478684 num_examples: 1200 download_size: 2970119 dataset_size: 5478684 configs: - config_name: default data_files: - split: train path: data/train-* ---
Pinhamusic/candicegomes3	--- license: openrail ---

kozistr/mqa-ko

--- language: - ko license: cc0-1.0 task_categories: - question-answering tags: - mqa dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 541067862 num_examples: 1382378 download_size: 162865210 dataset_size: 541067862 configs: - config_name: default data_files: - split: train path: data/train-* --- * https://huggingface.co/datasets/clips/mqa

open-llm-leaderboard/details_Charlie911__MultiLoRA-llama2-mmlu

--- pretty_name: Evaluation run of Charlie911/MultiLoRA-llama2-mmlu dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Charlie911/MultiLoRA-llama2-mmlu](https://huggingface.co/Charlie911/MultiLoRA-llama2-mmlu)\ \ 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_Charlie911__MultiLoRA-llama2-mmlu\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T20:19:51.603035](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__MultiLoRA-llama2-mmlu/blob/main/results_2024-02-09T20-19-51.603035.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\": 0.42939450714393407,\n\ \ \"acc_stderr\": 0.03450029235435365,\n \"acc_norm\": 0.4336173195651683,\n\ \ \"acc_norm_stderr\": 0.03529727761229674,\n \"mc1\": 0.2558139534883721,\n\ \ \"mc1_stderr\": 0.01527417621928336,\n \"mc2\": 0.40926286124406613,\n\ \ \"mc2_stderr\": 0.01393003126171617\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.47013651877133106,\n \"acc_stderr\": 0.0145853058400071,\n\ \ \"acc_norm\": 0.5221843003412969,\n \"acc_norm_stderr\": 0.01459700192707614\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5821549492133041,\n\ \ \"acc_stderr\": 0.00492196413387402,\n \"acc_norm\": 0.7759410476000796,\n\ \ \"acc_norm_stderr\": 0.004161089244867776\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\ \ \"acc_stderr\": 0.04304979692464243,\n \"acc_norm\": 0.45925925925925926,\n\ \ \"acc_norm_stderr\": 0.04304979692464243\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.45394736842105265,\n \"acc_stderr\": 0.04051646342874143,\n\ \ \"acc_norm\": 0.45394736842105265,\n \"acc_norm_stderr\": 0.04051646342874143\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.41,\n\ \ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.4981132075471698,\n \"acc_stderr\": 0.030772653642075657,\n\ \ \"acc_norm\": 0.4981132075471698,\n \"acc_norm_stderr\": 0.030772653642075657\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4583333333333333,\n\ \ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.4583333333333333,\n\ \ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3699421965317919,\n\ \ \"acc_stderr\": 0.03681229633394319,\n \"acc_norm\": 0.3699421965317919,\n\ \ \"acc_norm_stderr\": 0.03681229633394319\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n\ \ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.39148936170212767,\n \"acc_stderr\": 0.03190701242326812,\n\ \ \"acc_norm\": 0.39148936170212767,\n \"acc_norm_stderr\": 0.03190701242326812\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21052631578947367,\n\ \ \"acc_stderr\": 0.03835153954399421,\n \"acc_norm\": 0.21052631578947367,\n\ \ \"acc_norm_stderr\": 0.03835153954399421\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2751322751322751,\n \"acc_stderr\": 0.023000086859068642,\n \"\ acc_norm\": 0.2751322751322751,\n \"acc_norm_stderr\": 0.023000086859068642\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\ \ \"acc_stderr\": 0.04073524322147126,\n \"acc_norm\": 0.29365079365079366,\n\ \ \"acc_norm_stderr\": 0.04073524322147126\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.43548387096774194,\n\ \ \"acc_stderr\": 0.02820622559150274,\n \"acc_norm\": 0.43548387096774194,\n\ \ \"acc_norm_stderr\": 0.02820622559150274\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.35467980295566504,\n \"acc_stderr\": 0.0336612448905145,\n\ \ \"acc_norm\": 0.35467980295566504,\n \"acc_norm_stderr\": 0.0336612448905145\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.46060606060606063,\n \"acc_stderr\": 0.03892207016552013,\n\ \ \"acc_norm\": 0.46060606060606063,\n \"acc_norm_stderr\": 0.03892207016552013\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.4898989898989899,\n \"acc_stderr\": 0.035616254886737454,\n \"\ acc_norm\": 0.4898989898989899,\n \"acc_norm_stderr\": 0.035616254886737454\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.6062176165803109,\n \"acc_stderr\": 0.035260770955482405,\n\ \ \"acc_norm\": 0.6062176165803109,\n \"acc_norm_stderr\": 0.035260770955482405\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.4282051282051282,\n \"acc_stderr\": 0.025088301454694834,\n\ \ \"acc_norm\": 0.4282051282051282,\n \"acc_norm_stderr\": 0.025088301454694834\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.33613445378151263,\n \"acc_stderr\": 0.030684737115135377,\n\ \ \"acc_norm\": 0.33613445378151263,\n \"acc_norm_stderr\": 0.030684737115135377\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.5504587155963303,\n \"acc_stderr\": 0.021327881417823363,\n \"\ acc_norm\": 0.5504587155963303,\n \"acc_norm_stderr\": 0.021327881417823363\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.3148148148148148,\n \"acc_stderr\": 0.0316746870682898,\n \"acc_norm\"\ : 0.3148148148148148,\n \"acc_norm_stderr\": 0.0316746870682898\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5245098039215687,\n\ \ \"acc_stderr\": 0.03505093194348798,\n \"acc_norm\": 0.5245098039215687,\n\ \ \"acc_norm_stderr\": 0.03505093194348798\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.569620253164557,\n \"acc_stderr\": 0.032230171959375976,\n\ \ \"acc_norm\": 0.569620253164557,\n \"acc_norm_stderr\": 0.032230171959375976\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.4798206278026906,\n\ \ \"acc_stderr\": 0.033530461674123,\n \"acc_norm\": 0.4798206278026906,\n\ \ \"acc_norm_stderr\": 0.033530461674123\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.46564885496183206,\n \"acc_stderr\": 0.04374928560599738,\n\ \ \"acc_norm\": 0.46564885496183206,\n \"acc_norm_stderr\": 0.04374928560599738\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.5785123966942148,\n \"acc_stderr\": 0.04507732278775088,\n \"\ acc_norm\": 0.5785123966942148,\n \"acc_norm_stderr\": 0.04507732278775088\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4537037037037037,\n\ \ \"acc_stderr\": 0.048129173245368216,\n \"acc_norm\": 0.4537037037037037,\n\ \ \"acc_norm_stderr\": 0.048129173245368216\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.36809815950920244,\n \"acc_stderr\": 0.03789213935838396,\n\ \ \"acc_norm\": 0.36809815950920244,\n \"acc_norm_stderr\": 0.03789213935838396\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.04059867246952687,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.04059867246952687\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.49514563106796117,\n \"acc_stderr\": 0.049505043821289195,\n\ \ \"acc_norm\": 0.49514563106796117,\n \"acc_norm_stderr\": 0.049505043821289195\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.5299145299145299,\n\ \ \"acc_stderr\": 0.032697411068124425,\n \"acc_norm\": 0.5299145299145299,\n\ \ \"acc_norm_stderr\": 0.032697411068124425\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.598978288633461,\n\ \ \"acc_stderr\": 0.017526133150124572,\n \"acc_norm\": 0.598978288633461,\n\ \ \"acc_norm_stderr\": 0.017526133150124572\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.4508670520231214,\n \"acc_stderr\": 0.026788811931562764,\n\ \ \"acc_norm\": 0.4508670520231214,\n \"acc_norm_stderr\": 0.026788811931562764\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.01435591196476786,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.01435591196476786\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.028431095444176647,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.028431095444176647\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.48231511254019294,\n\ \ \"acc_stderr\": 0.02838032284907713,\n \"acc_norm\": 0.48231511254019294,\n\ \ \"acc_norm_stderr\": 0.02838032284907713\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.4660493827160494,\n \"acc_stderr\": 0.02775653525734767,\n\ \ \"acc_norm\": 0.4660493827160494,\n \"acc_norm_stderr\": 0.02775653525734767\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.29432624113475175,\n \"acc_stderr\": 0.027187127011503814,\n \ \ \"acc_norm\": 0.29432624113475175,\n \"acc_norm_stderr\": 0.027187127011503814\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3376792698826597,\n\ \ \"acc_stderr\": 0.012078563777145564,\n \"acc_norm\": 0.3376792698826597,\n\ \ \"acc_norm_stderr\": 0.012078563777145564\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.46691176470588236,\n \"acc_stderr\": 0.030306257722468314,\n\ \ \"acc_norm\": 0.46691176470588236,\n \"acc_norm_stderr\": 0.030306257722468314\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.39869281045751637,\n \"acc_stderr\": 0.019808281317449848,\n \ \ \"acc_norm\": 0.39869281045751637,\n \"acc_norm_stderr\": 0.019808281317449848\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.4636363636363636,\n\ \ \"acc_stderr\": 0.047764491623961985,\n \"acc_norm\": 0.4636363636363636,\n\ \ \"acc_norm_stderr\": 0.047764491623961985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.4530612244897959,\n \"acc_stderr\": 0.03186785930004129,\n\ \ \"acc_norm\": 0.4530612244897959,\n \"acc_norm_stderr\": 0.03186785930004129\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.48756218905472637,\n\ \ \"acc_stderr\": 0.03534439848539579,\n \"acc_norm\": 0.48756218905472637,\n\ \ \"acc_norm_stderr\": 0.03534439848539579\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.3795180722891566,\n\ \ \"acc_stderr\": 0.03777798822748018,\n \"acc_norm\": 0.3795180722891566,\n\ \ \"acc_norm_stderr\": 0.03777798822748018\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.6140350877192983,\n \"acc_stderr\": 0.03733756969066164,\n\ \ \"acc_norm\": 0.6140350877192983,\n \"acc_norm_stderr\": 0.03733756969066164\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2558139534883721,\n\ \ \"mc1_stderr\": 0.01527417621928336,\n \"mc2\": 0.40926286124406613,\n\ \ \"mc2_stderr\": 0.01393003126171617\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7379636937647988,\n \"acc_stderr\": 0.01235894443163756\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11751326762699014,\n \ \ \"acc_stderr\": 0.008870331256489986\n }\n}\n```" repo_url: https://huggingface.co/Charlie911/MultiLoRA-llama2-mmlu 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_09T20_19_51.603035 path: - '**/details_harness|arc:challenge|25_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-09T20-19-51.603035.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|gsm8k|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hellaswag|10_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T20-19-51.603035.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T20-19-51.603035.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T20-19-51.603035.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T20_19_51.603035 path: - '**/details_harness|winogrande|5_2024-02-09T20-19-51.603035.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-09T20-19-51.603035.parquet' - config_name: results data_files: - split: 2024_02_09T20_19_51.603035 path: - results_2024-02-09T20-19-51.603035.parquet - split: latest path: - results_2024-02-09T20-19-51.603035.parquet --- # Dataset Card for Evaluation run of Charlie911/MultiLoRA-llama2-mmlu  Dataset automatically created during the evaluation run of model [Charlie911/MultiLoRA-llama2-mmlu](https://huggingface.co/Charlie911/MultiLoRA-llama2-mmlu) 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_Charlie911__MultiLoRA-llama2-mmlu", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T20:19:51.603035](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__MultiLoRA-llama2-mmlu/blob/main/results_2024-02-09T20-19-51.603035.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": 0.42939450714393407, "acc_stderr": 0.03450029235435365, "acc_norm": 0.4336173195651683, "acc_norm_stderr": 0.03529727761229674, "mc1": 0.2558139534883721, "mc1_stderr": 0.01527417621928336, "mc2": 0.40926286124406613, "mc2_stderr": 0.01393003126171617 }, "harness|arc:challenge|25": { "acc": 0.47013651877133106, "acc_stderr": 0.0145853058400071, "acc_norm": 0.5221843003412969, "acc_norm_stderr": 0.01459700192707614 }, "harness|hellaswag|10": { "acc": 0.5821549492133041, "acc_stderr": 0.00492196413387402, "acc_norm": 0.7759410476000796, "acc_norm_stderr": 0.004161089244867776 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464243, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464243 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.45394736842105265, "acc_stderr": 0.04051646342874143, "acc_norm": 0.45394736842105265, "acc_norm_stderr": 0.04051646342874143 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4981132075471698, "acc_stderr": 0.030772653642075657, "acc_norm": 0.4981132075471698, "acc_norm_stderr": 0.030772653642075657 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4583333333333333, "acc_stderr": 0.04166666666666665, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.04166666666666665 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3699421965317919, "acc_stderr": 0.03681229633394319, "acc_norm": 0.3699421965317919, "acc_norm_stderr": 0.03681229633394319 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364395, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364395 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.39148936170212767, "acc_stderr": 0.03190701242326812, "acc_norm": 0.39148936170212767, "acc_norm_stderr": 0.03190701242326812 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03835153954399421, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03835153954399421 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4689655172413793, "acc_stderr": 0.04158632762097828, "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2751322751322751, "acc_stderr": 0.023000086859068642, "acc_norm": 0.2751322751322751, "acc_norm_stderr": 0.023000086859068642 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.29365079365079366, "acc_stderr": 0.04073524322147126, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.04073524322147126 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.43548387096774194, "acc_stderr": 0.02820622559150274, "acc_norm": 0.43548387096774194, "acc_norm_stderr": 0.02820622559150274 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.35467980295566504, "acc_stderr": 0.0336612448905145, "acc_norm": 0.35467980295566504, "acc_norm_stderr": 0.0336612448905145 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.46060606060606063, "acc_stderr": 0.03892207016552013, "acc_norm": 0.46060606060606063, "acc_norm_stderr": 0.03892207016552013 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.4898989898989899, "acc_stderr": 0.035616254886737454, "acc_norm": 0.4898989898989899, "acc_norm_stderr": 0.035616254886737454 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6062176165803109, "acc_stderr": 0.035260770955482405, "acc_norm": 0.6062176165803109, "acc_norm_stderr": 0.035260770955482405 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4282051282051282, "acc_stderr": 0.025088301454694834, "acc_norm": 0.4282051282051282, "acc_norm_stderr": 0.025088301454694834 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.33613445378151263, "acc_stderr": 0.030684737115135377, "acc_norm": 0.33613445378151263, "acc_norm_stderr": 0.030684737115135377 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5504587155963303, "acc_stderr": 0.021327881417823363, "acc_norm": 0.5504587155963303, "acc_norm_stderr": 0.021327881417823363 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.0316746870682898, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.0316746870682898 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5245098039215687, "acc_stderr": 0.03505093194348798, "acc_norm": 0.5245098039215687, "acc_norm_stderr": 0.03505093194348798 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.569620253164557, "acc_stderr": 0.032230171959375976, "acc_norm": 0.569620253164557, "acc_norm_stderr": 0.032230171959375976 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.4798206278026906, "acc_stderr": 0.033530461674123, "acc_norm": 0.4798206278026906, "acc_norm_stderr": 0.033530461674123 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.46564885496183206, "acc_stderr": 0.04374928560599738, "acc_norm": 0.46564885496183206, "acc_norm_stderr": 0.04374928560599738 }, "harness|hendrycksTest-international_law|5": { "acc": 0.5785123966942148, "acc_stderr": 0.04507732278775088, "acc_norm": 0.5785123966942148, "acc_norm_stderr": 0.04507732278775088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.4537037037037037, "acc_stderr": 0.048129173245368216, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.048129173245368216 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.36809815950920244, "acc_stderr": 0.03789213935838396, "acc_norm": 0.36809815950920244, "acc_norm_stderr": 0.03789213935838396 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.24107142857142858, "acc_stderr": 0.04059867246952687, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.04059867246952687 }, "harness|hendrycksTest-management|5": { "acc": 0.49514563106796117, "acc_stderr": 0.049505043821289195, "acc_norm": 0.49514563106796117, "acc_norm_stderr": 0.049505043821289195 }, "harness|hendrycksTest-marketing|5": { "acc": 0.5299145299145299, "acc_stderr": 0.032697411068124425, "acc_norm": 0.5299145299145299, "acc_norm_stderr": 0.032697411068124425 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.598978288633461, "acc_stderr": 0.017526133150124572, "acc_norm": 0.598978288633461, "acc_norm_stderr": 0.017526133150124572 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.4508670520231214, "acc_stderr": 0.026788811931562764, "acc_norm": 0.4508670520231214, "acc_norm_stderr": 0.026788811931562764 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2435754189944134, "acc_stderr": 0.01435591196476786, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.01435591196476786 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.4411764705882353, "acc_stderr": 0.028431095444176647, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.028431095444176647 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.48231511254019294, "acc_stderr": 0.02838032284907713, "acc_norm": 0.48231511254019294, "acc_norm_stderr": 0.02838032284907713 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4660493827160494, "acc_stderr": 0.02775653525734767, "acc_norm": 0.4660493827160494, "acc_norm_stderr": 0.02775653525734767 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.29432624113475175, "acc_stderr": 0.027187127011503814, "acc_norm": 0.29432624113475175, "acc_norm_stderr": 0.027187127011503814 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3376792698826597, "acc_stderr": 0.012078563777145564, "acc_norm": 0.3376792698826597, "acc_norm_stderr": 0.012078563777145564 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.46691176470588236, "acc_stderr": 0.030306257722468314, "acc_norm": 0.46691176470588236, "acc_norm_stderr": 0.030306257722468314 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.39869281045751637, "acc_stderr": 0.019808281317449848, "acc_norm": 0.39869281045751637, "acc_norm_stderr": 0.019808281317449848 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.4636363636363636, "acc_stderr": 0.047764491623961985, "acc_norm": 0.4636363636363636, "acc_norm_stderr": 0.047764491623961985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4530612244897959, "acc_stderr": 0.03186785930004129, "acc_norm": 0.4530612244897959, "acc_norm_stderr": 0.03186785930004129 }, "harness|hendrycksTest-sociology|5": { "acc": 0.48756218905472637, "acc_stderr": 0.03534439848539579, "acc_norm": 0.48756218905472637, "acc_norm_stderr": 0.03534439848539579 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-virology|5": { "acc": 0.3795180722891566, "acc_stderr": 0.03777798822748018, "acc_norm": 0.3795180722891566, "acc_norm_stderr": 0.03777798822748018 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6140350877192983, "acc_stderr": 0.03733756969066164, "acc_norm": 0.6140350877192983, "acc_norm_stderr": 0.03733756969066164 }, "harness|truthfulqa:mc|0": { "mc1": 0.2558139534883721, "mc1_stderr": 0.01527417621928336, "mc2": 0.40926286124406613, "mc2_stderr": 0.01393003126171617 }, "harness|winogrande|5": { "acc": 0.7379636937647988, "acc_stderr": 0.01235894443163756 }, "harness|gsm8k|5": { "acc": 0.11751326762699014, "acc_stderr": 0.008870331256489986 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

gagan3012/arabic-xnli-pairwise

--- dataset_info: features: - name: labels sequence: int64 - name: sent1 sequence: string - name: sent2 sequence: string splits: - name: train num_bytes: 70811123 num_examples: 1 - name: test num_bytes: 850605 num_examples: 1 - name: validation num_bytes: 415074 num_examples: 1 download_size: 37859272 dataset_size: 72076802 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

BangumiBase/lycorisrecoil

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Lycoris Recoil This is the image base of bangumi Lycoris Recoil, we detected 31 characters, 2149 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 22 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 67 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 17 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 117 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 120 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 21 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 79 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 36 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 16 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 24 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 11 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 21 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 11 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 10 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 10 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 118 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 10 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 54 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 50 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 23 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 10 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 9 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 407 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 13 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 102 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 9 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 27 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 510 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 33 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 27 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | noise | 165 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

Ti-Ma/TiMaGPT2-2019

--- license: other license_name: paracrawl-license license_link: LICENSE ---

DrNicefellow/Quality_WorryFree_GeneralQA_Chat_Dataset-v1

--- license: apache-2.0 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 37678642 num_examples: 21982 download_size: 16914098 dataset_size: 37678642 --- # Dr. Nicefollows's Worry Free General Chat Dataset v1 ## Overview This dataset contains high-quality general chat samples questions and answers. It is designed following the LIMA: Less Is More for Alignment principle from MetaAI: emphasizing the importance of quality over quantity in training data. Despite its modest size, the dataset's quality ensures its effectiveness in training and fine-tuning conversational AI models. In this version, each chat has one user query and assistant answer. In the next version, it will become a conversation of multiple rounds. ## Dataset Format The dataset is structured in the Vicuna 1.1 format, featuring one-round chats. This format is chosen for its compatibility with various conversational AI training paradigms and its efficiency in representing dialogues. ## Volume The dataset comprises a few thousand chat samples. Each sample has been carefully curated to ensure the highest quality, aligning with the LIMA principle. ## Licensing Our dataset is worry-free regarding proprietary issues, as it is not automatically generated by a proprietary chatbot. This dataset is released under the Apache License 2.0. This license allows for broad freedom in usage and modification, provided that proper credit is given and changes are documented. For full license terms, please refer to the LICENSE file. ## Use Case This dataset is ideal for training conversational AI models. It can help in developing chatbots or virtual assistants capable of handling a wide range of queries with high accuracy. To use the dataset for finetuning a model with Axolotl, simply add the following to the .yml file: datasets: - path: DrNicefellow/Quality_WorryFree_GeneralQA_Chat_Dataset-v1 - type: completion ## Feeling Generous? 😊 Eager to buy me a cup of 2$ coffe or iced tea?🍵☕ Sure, here is the link: [https://ko-fi.com/drnicefellow](https://ko-fi.com/drnicefellow). Please add a note on which one you want me to drink?

xiaxiaoqian/model

--- license: mit ---

jsqihui/detective-dataset-en

--- dataset_info: features: - name: prompt dtype: string - name: text dtype: string splits: - name: train num_bytes: 746174 num_examples: 74 download_size: 409863 dataset_size: 746174 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvations/c4p0-v2-en-de

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 41900834 num_examples: 34234 download_size: 19737322 dataset_size: 41900834 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4

--- pretty_name: Evaluation run of Mikivis/gpt2-large-lora-stf4 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Mikivis/gpt2-large-lora-stf4](https://huggingface.co/Mikivis/gpt2-large-lora-stf4)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T23:48:52.785657](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4/blob/main/results_2023-10-27T23-48-52.785657.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\": 0.003460570469798658,\n\ \ \"em_stderr\": 0.0006013962884271089,\n \"f1\": 0.07443372483221503,\n\ \ \"f1_stderr\": 0.0016782330994195233,\n \"acc\": 0.26795580110497236,\n\ \ \"acc_stderr\": 0.007008096716979156\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.003460570469798658,\n \"em_stderr\": 0.0006013962884271089,\n\ \ \"f1\": 0.07443372483221503,\n \"f1_stderr\": 0.0016782330994195233\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5359116022099447,\n\ \ \"acc_stderr\": 0.014016193433958312\n }\n}\n```" repo_url: https://huggingface.co/Mikivis/gpt2-large-lora-stf4 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_12T03_05_07.244584 path: - '**/details_harness|arc:challenge|25_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-12T03-05-07.244584.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T23_48_52.785657 path: - '**/details_harness|drop|3_2023-10-27T23-48-52.785657.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-27T23-48-52.785657.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T23_48_52.785657 path: - '**/details_harness|gsm8k|5_2023-10-27T23-48-52.785657.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-27T23-48-52.785657.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hellaswag|10_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-12T03-05-07.244584.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T03-05-07.244584.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_12T03_05_07.244584 path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T03-05-07.244584.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-12T03-05-07.244584.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T23_48_52.785657 path: - '**/details_harness|winogrande|5_2023-10-27T23-48-52.785657.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-27T23-48-52.785657.parquet' - config_name: results data_files: - split: 2023_09_12T03_05_07.244584 path: - results_2023-09-12T03-05-07.244584.parquet - split: 2023_10_27T23_48_52.785657 path: - results_2023-10-27T23-48-52.785657.parquet - split: latest path: - results_2023-10-27T23-48-52.785657.parquet --- # Dataset Card for Evaluation run of Mikivis/gpt2-large-lora-stf4 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Mikivis/gpt2-large-lora-stf4 - **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 [Mikivis/gpt2-large-lora-stf4](https://huggingface.co/Mikivis/gpt2-large-lora-stf4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T23:48:52.785657](https://huggingface.co/datasets/open-llm-leaderboard/details_Mikivis__gpt2-large-lora-stf4/blob/main/results_2023-10-27T23-48-52.785657.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": 0.003460570469798658, "em_stderr": 0.0006013962884271089, "f1": 0.07443372483221503, "f1_stderr": 0.0016782330994195233, "acc": 0.26795580110497236, "acc_stderr": 0.007008096716979156 }, "harness|drop|3": { "em": 0.003460570469798658, "em_stderr": 0.0006013962884271089, "f1": 0.07443372483221503, "f1_stderr": 0.0016782330994195233 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.5359116022099447, "acc_stderr": 0.014016193433958312 } } ``` ### 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]

MatsuoDochiai/Mae

--- license: openrail ---

deepharborAI/Hindi-Niband

--- license: mit task_categories: - text-generation - table-question-answering - summarization language: - hi pretty_name: 'Niband ' size_categories: - 100M<n<1B --- ### Dataset Name: Hindi- Niband (Massive Hindi language Text Dataset) #### Dataset Overview This dataset is a comprehensive collection of text data consisting of more than 10 billion tokens. It encompasses a wide range of sources, including Wikipedia articles, news articles, email transcripts, and generated prompt text. Specific Hindi language data columns have been extracted from the CulturaX dataset, which is a large, cleaned, and multilingual dataset for large language models. We acknowledge and cite the CulturaX dataset using the provided citation. #### Data Sources 1. **Wikipedia Articles:** A large corpus of text extracted from Wikipedia articles covering various topics and domains. 2. **News Articles:** Textual data sourced from news articles from diverse sources and regions. 3. **Email Transcripts:** Transcripts of email communications, providing insights into natural language usage in electronic correspondence. 4. **Prompt Text Generation:** Text generated from prompts or prompts used to generate text, contributing to the dataset's diversity and complexity. 5. **Hindi Data from CulturaX Dataset:** Specific Hindi language data columns have been extracted from the CulturaX dataset, which is a large, cleaned, and multilingual dataset for large language models. #### Potential Uses - Training and evaluating natural language generation models in the Hindi language domain. - Exploring the capabilities of models in narrative generation tasks. - Conducting research on narrative understanding and generation in Hindi. - Analyzing sentiment and opinion mining in Hindi text data. - Building chatbots or virtual assistants capable of interacting in Hindi. - Creating educational resources for teaching Hindi language and literature. - Developing machine translation systems for translating between Hindi and other languages. - Studying cross-lingual transfer learning techniques for improving natural language processing tasks in Hindi. #### Importance for Indian Native Languages :- This dataset can be crucial for the training of LLM (Large Language Model) models and aiming to explore the capabilities of those natural language generation models in Hindi. It serves as a foundation for training and evaluating models capable of producing coherent and contextually relevant narratives or explanations. Additionally, this dataset aligns with our commitment to promoting Indian native languages on a global scale. We recognize the limited availability of such datasets as a major challenge for innovation within the local Indian community. As part of our contribution to the Indian open-source community, we are planning to release a very large database covering various Indian native languages. This initiative aims to empower researchers, practitioners, and developers to explore and innovate in Indian language processing and generation tasks. #### Citation If you use this dataset in your research or applications, please consider citing the CulturaX dataset using the provided citation. We acknowledge and cite the CulturaX dataset using the following citation: ``` @misc{nguyen2023culturax, title={CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages}, author={Thuat Nguyen and Chien Van Nguyen and Viet Dac Lai and Hieu Man and Nghia Trung Ngo and Franck Dernoncourt and Ryan A. Rossi and Thien Huu Nguyen}, year={2023}, eprint={2309.09400}, archivePrefix={arXiv}, primaryClass={cs.CL}. ``` Additionally, the dataset includes news article data, and we acknowledge and cite the source of this data using the following citations: ``` @inproceedings{see-etal-2017-get, title = "Get To The Point: Summarization with Pointer-Generator Networks", author = "See, Abigail and Liu, Peter J. and Manning, Christopher D.", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P17-1099", doi = "10.18653/v1/P17-1099", pages = "1073--1083", abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",} @inproceedings{DBLP:conf/nips/HermannKGEKSB15, author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom}, title={Teaching Machines to Read and Comprehend}, year={2015}, cdate={1420070400000}, pages={1693-1701}, url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend}, booktitle={NIPS}, crossref={conf/nips/2015} } ``` #### License Please refer to the licensing terms specified by the dataset creators. #### Disclaimer The views expressed in the dataset do not necessarily reflect the views of the dataset creators or contributors. Users are advised to use the data responsibly and in accordance with ethical guidelines. This dataset card provides an overview of the massive multilingual text dataset, highlighting its sources, potential uses, citation, and disclaimer.

ravithejads/ms_marco_hi

--- dataset_info: features: - name: answers sequence: string - name: passages sequence: - name: is_selected dtype: int32 - name: passage_text dtype: string - name: url dtype: string - name: query dtype: string - name: query_id dtype: int32 - name: query_type dtype: string - name: wellFormedAnswers sequence: string - name: query_hi dtype: string - name: answers_hi dtype: string - name: passage_text_hi sequence: string splits: - name: test num_bytes: 129079041 num_examples: 9650 download_size: 49278186 dataset_size: 129079041 configs: - config_name: default data_files: - split: test path: data/test-* ---

FranzderPapst/squad_x_boolq

--- license: mit language: - en task_categories: - text-classification pretty_name: warrgalbhalble size_categories: - 1K<n<10K --- # ABOUT Wanted to train a model to classify question, if they are open ore boolean. So I merged SQuAD with BoolQ, the dataset contains 5000 question of each dataset, labeled with "true" (the boolean question) and with "false" (the open questions). Didn't add questions that don't fall into these categories. May be a flaw, we'll see:). For some reason the dataset viewer isn't working, sorry for that one, but here's a snippet of the json structure: { "question": "are there fiber optic cables under the ocean", "type": "true" }, { "question": "are dollar general and dollar tree owned by the same company", "type": "true" },

Mr-aio/All-Isa-AF

--- language: - en size_categories: - 1K<n<10K ---

NeuralNovel/Unsloth-DPO

--- language: - en license: apache-2.0 --- <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Data Card</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> <style> body { font-family: 'Quicksand', sans-serif; background-color: #1A202C; color: #D8DEE9; margin: 0; padding: 0; /* Remove default padding */ font-size: 26px; background: linear-gradient(135deg, #2E3440 0%, #1A202C 100%); } p { padding-left: 10px } .container { width: 100%; margin: auto; background-color: rgb(255 255 255 / 1%); padding: 20px 30px 40px; /* Add padding below the image only */ padding-right: 32px; border-radius: 12px; box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2); backdrop-filter: blur(10px); border: 1px solid rgba(255, 255, 255, 0.05); background-color: rgb(0 0 0 / 75%) !important; } .header h1 { font-size: 28px; color: #fff; /* White text color */ margin: 0; text-shadow: -1px -1px 0 #000, 1px -1px 0 #000, -1px 1px 0 #000, 1px 1px 0 #000; /* Black outline */ } .header { display: flex; align-items: center; justify-content: space-between; gap: 20px; } img { border-radius: 10px 10px 0 0!important; padding-left: 0px !important; } .header h1 { font-size: 28px; color: #ECEFF4; margin: 0; text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3); } .info { background-color: rgba(255, 255, 255, 0.05); color: #AEBAC7; border-radius: 12px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.2); font-size: 14px; line-height: 1.6; margin-left: 5px; overflow-x: auto; margin-top: 20px; /* Adjusted margin */ border: 1px solid rgba(255, 255, 255, 0.05); transition: background-color 0.6s ease; /* Smooth transition over 0.5 seconds */ } .info:hover { } .info img { width: 100%; border-radius: 10px 10px 0 0; margin-top: -20px; /* Negative margin to overlap container margin */ } a { color: #88C0D0; text-decoration: none; transition: color 0.3s ease; position: relative; } a:hover { color: #A3BE8C; text-decoration: none; } a::before { content: ''; position: absolute; width: 100%; height: 2px; bottom: 0; left: 0; background-color: #A3BE8C; visibility: hidden; transform: scaleX(0); transition: all 0.3s ease-in-out; } a:hover::before { visibility: visible; transform: scaleX(1); } .button { display: inline-block; background-color: #5E81AC; color: #E5E9F0; padding: 10px 20px; border-radius: 5px; cursor: pointer; text-decoration: none; transition: background-color 0.3s ease; } .button:hover { background-color: #81A1C1; } .hf-sanitized.hf-sanitized-oJB5trHYB93-j8lDfGQn3 .container { } </style> </head> <body> <div class="container"> <div class="header"> <h1>Unsloth-DPO</h1> </div> <div class="info"> <img src="https://i.ibb.co/hY42ZY7/OIG4-8.jpg" style="border-radius: 10px;"> <p><strong>Creator:</strong> <a href="https://huggingface.co/NeuralNovel" target="_blank">NeuralNovel</a></p> <p><strong>Community Organization:</strong> <a href="https://huggingface.co/ConvexAI" target="_blank">ConvexAI</a></p> <p><strong>Discord:</strong> <a href="https://discord.gg/rJXGjmxqzS" target="_blank">Join us on Discord</a></p> <p><strong>Special Thanks: <a href ="https://unsloth.ai/" target="_blank"> Unsloth.ai</strong></a></p> </head> <body> <div> <div> <p><strong>About Neural-DPO:</strong> The Unsloth-DPO dataset, inspired by orca_dpo_pairs. This dataset features questions and answers pairs, with a direct focus on Unsloth.ai.</p> <p><strong>Source Data:</strong></p> <ul> <li>orca_dpo_pairs (Inspiration)</li> <li>Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL</li> <li>unsloth.ai/blog/mistral-benchmark</li> </ul> <p><strong>Phrases Removed:</strong></p> <p>To enhance the dataset's coherence and relevance across varied contexts, certain phrases have been selectively omitted.</p> <ul> <li>Couldn't help but</li> <li>Can't resist</li> <li>I'm sorry, but</li> <li>As an AI</li> <li>However, it is important to</li> <li>Cannot provide</li> <li>And others</li> </ul> </div> </div> </body>

gallantVN/en_vi_DPO

--- license: apache-2.0 task_categories: - translation - reinforcement-learning size_categories: - 10K<n<100K ---

autoevaluate/autoeval-eval-lener_br-lener_br-2a71c5-1777061681

--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: pierreguillou/ner-bert-large-cased-pt-lenerbr metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: validation 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: pierreguillou/ner-bert-large-cased-pt-lenerbr * Dataset: lener_br * Config: lener_br * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.

ktmeng/mec

--- license: mit ---

ola13/small-the_pile

--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: perplexity_score dtype: float64 - name: pile_set_name dtype: string splits: - name: train num_bytes: 606056668 num_examples: 100000 download_size: 328667964 dataset_size: 606056668 --- # Dataset Card for "small-the_pile" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CVasNLPExperiments/Food101_test_google_flan_t5_xl_mode_A_ns_25250

--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices num_bytes: 10610673 num_examples: 25250 download_size: 1146498 dataset_size: 10610673 --- # Dataset Card for "Food101_test_google_flan_t5_xl_mode_A_ns_25250" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jkv53/13F_Reports_with_labels

--- dataset_info: features: - name: title dtype: string - name: body dtype: string - name: label dtype: string splits: - name: train num_bytes: 12642773 num_examples: 1113 download_size: 3334911 dataset_size: 12642773 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "13F_Reports_with_labels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kenanjeff/ComVG

--- license: creativeml-openrail-m task_categories: - zero-shot-classification tags: - code size_categories: - 1K<n<10K --- Compositional Visual Genome (ComVG) <br/> ComVG benchmark aims to test vision-language models ability in text-to-image retrieval. <br/> We selected 542 high-quality images from Visual Genome and created 5400 datapoints in ComVG. <br/> Each datapoint contains a postive and negative image. The negative image is a mutated variant with singular discrepancies in subject, object, or predicate.<br/> For more details on creation process, please refer: https://arxiv.org/abs/2211.13854

open-llm-leaderboard/details_922CA__Silicon-Monika-7b

--- pretty_name: Evaluation run of 922CA/Silicon-Monika-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [922CA/Silicon-Monika-7b](https://huggingface.co/922CA/Silicon-Monika-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_922CA__Silicon-Monika-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-29T12:04:26.776279](https://huggingface.co/datasets/open-llm-leaderboard/details_922CA__Silicon-Monika-7b/blob/main/results_2024-02-29T12-04-26.776279.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\": 0.6283372065747487,\n\ \ \"acc_stderr\": 0.03245805597450051,\n \"acc_norm\": 0.6301488013494124,\n\ \ \"acc_norm_stderr\": 0.03311159741284949,\n \"mc1\": 0.3525091799265606,\n\ \ \"mc1_stderr\": 0.016724646380756544,\n \"mc2\": 0.5214295545813499,\n\ \ \"mc2_stderr\": 0.015004393759780037\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5972696245733788,\n \"acc_stderr\": 0.014332236306790152,\n\ \ \"acc_norm\": 0.6313993174061433,\n \"acc_norm_stderr\": 0.014097810678042194\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6231826329416451,\n\ \ \"acc_stderr\": 0.004835981632401601,\n \"acc_norm\": 0.8264289982075284,\n\ \ \"acc_norm_stderr\": 0.003779661224651475\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n\ \ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\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\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\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\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n\ \ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n\ \ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663434,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663434\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n\ \ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155243,\n \"\ acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155243\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.7612903225806451,\n\ \ \"acc_stderr\": 0.024251071262208837,\n \"acc_norm\": 0.7612903225806451,\n\ \ \"acc_norm_stderr\": 0.024251071262208837\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.5024630541871922,\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\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\ acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6384615384615384,\n \"acc_stderr\": 0.024359581465396993,\n\ \ \"acc_norm\": 0.6384615384615384,\n \"acc_norm_stderr\": 0.024359581465396993\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871923,\n \ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871923\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135363,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135363\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8165137614678899,\n \"acc_stderr\": 0.016595259710399306,\n \"\ acc_norm\": 0.8165137614678899,\n \"acc_norm_stderr\": 0.016595259710399306\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\ acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676177,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676177\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\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\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\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\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8290598290598291,\n\ \ \"acc_stderr\": 0.024662496845209818,\n \"acc_norm\": 0.8290598290598291,\n\ \ \"acc_norm_stderr\": 0.024662496845209818\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n\ \ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\ \ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\ \ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27262569832402234,\n\ \ \"acc_stderr\": 0.014893391735249612,\n \"acc_norm\": 0.27262569832402234,\n\ \ \"acc_norm_stderr\": 0.014893391735249612\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.02633661346904663,\n\ \ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.02633661346904663\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6975308641975309,\n \"acc_stderr\": 0.025557653981868062,\n\ \ \"acc_norm\": 0.6975308641975309,\n \"acc_norm_stderr\": 0.025557653981868062\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4517601043024772,\n\ \ \"acc_stderr\": 0.012710662233660245,\n \"acc_norm\": 0.4517601043024772,\n\ \ \"acc_norm_stderr\": 0.012710662233660245\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6580882352941176,\n \"acc_stderr\": 0.02881472242225419,\n\ \ \"acc_norm\": 0.6580882352941176,\n \"acc_norm_stderr\": 0.02881472242225419\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6372549019607843,\n \"acc_stderr\": 0.019450768432505514,\n \ \ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.019450768432505514\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\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\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835816,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835816\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3525091799265606,\n\ \ \"mc1_stderr\": 0.016724646380756544,\n \"mc2\": 0.5214295545813499,\n\ \ \"mc2_stderr\": 0.015004393759780037\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7821625887924231,\n \"acc_stderr\": 0.011601066079939324\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6050037907505686,\n \ \ \"acc_stderr\": 0.013465354969973198\n }\n}\n```" repo_url: https://huggingface.co/922CA/Silicon-Monika-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_02_29T12_04_26.776279 path: - '**/details_harness|arc:challenge|25_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-29T12-04-26.776279.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|gsm8k|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hellaswag|10_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-04-26.776279.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-management|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-04-26.776279.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|truthfulqa:mc|0_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-29T12-04-26.776279.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_29T12_04_26.776279 path: - '**/details_harness|winogrande|5_2024-02-29T12-04-26.776279.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-29T12-04-26.776279.parquet' - config_name: results data_files: - split: 2024_02_29T12_04_26.776279 path: - results_2024-02-29T12-04-26.776279.parquet - split: latest path: - results_2024-02-29T12-04-26.776279.parquet --- # Dataset Card for Evaluation run of 922CA/Silicon-Monika-7b  Dataset automatically created during the evaluation run of model [922CA/Silicon-Monika-7b](https://huggingface.co/922CA/Silicon-Monika-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_922CA__Silicon-Monika-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-29T12:04:26.776279](https://huggingface.co/datasets/open-llm-leaderboard/details_922CA__Silicon-Monika-7b/blob/main/results_2024-02-29T12-04-26.776279.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": 0.6283372065747487, "acc_stderr": 0.03245805597450051, "acc_norm": 0.6301488013494124, "acc_norm_stderr": 0.03311159741284949, "mc1": 0.3525091799265606, "mc1_stderr": 0.016724646380756544, "mc2": 0.5214295545813499, "mc2_stderr": 0.015004393759780037 }, "harness|arc:challenge|25": { "acc": 0.5972696245733788, "acc_stderr": 0.014332236306790152, "acc_norm": 0.6313993174061433, "acc_norm_stderr": 0.014097810678042194 }, "harness|hellaswag|10": { "acc": 0.6231826329416451, "acc_stderr": 0.004835981632401601, "acc_norm": 0.8264289982075284, "acc_norm_stderr": 0.003779661224651475 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "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": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "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": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663434, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663434 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5724137931034483, "acc_stderr": 0.041227371113703316, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.041227371113703316 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155243, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155243 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7612903225806451, "acc_stderr": 0.024251071262208837, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.024251071262208837 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.03517945038691063, "acc_norm": 0.5024630541871922, "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": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217487, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217487 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8860103626943006, "acc_stderr": 0.022935144053919443, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6384615384615384, "acc_stderr": 0.024359581465396993, "acc_norm": 0.6384615384615384, "acc_norm_stderr": 0.024359581465396993 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.027840811495871923, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.027840811495871923 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135363, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135363 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8165137614678899, "acc_stderr": 0.016595259710399306, "acc_norm": 0.8165137614678899, "acc_norm_stderr": 0.016595259710399306 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676177, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676177 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "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": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8290598290598291, "acc_stderr": 0.024662496845209818, "acc_norm": 0.8290598290598291, "acc_norm_stderr": 0.024662496845209818 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8084291187739464, "acc_stderr": 0.014072859310451949, "acc_norm": 0.8084291187739464, "acc_norm_stderr": 0.014072859310451949 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7109826589595376, "acc_stderr": 0.02440517393578323, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.02440517393578323 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.27262569832402234, "acc_stderr": 0.014893391735249612, "acc_norm": 0.27262569832402234, "acc_norm_stderr": 0.014893391735249612 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.696078431372549, "acc_stderr": 0.02633661346904663, "acc_norm": 0.696078431372549, "acc_norm_stderr": 0.02633661346904663 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7202572347266881, "acc_stderr": 0.02549425935069491, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.02549425935069491 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6975308641975309, "acc_stderr": 0.025557653981868062, "acc_norm": 0.6975308641975309, "acc_norm_stderr": 0.025557653981868062 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4645390070921986, "acc_stderr": 0.02975238965742705, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.02975238965742705 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4517601043024772, "acc_stderr": 0.012710662233660245, "acc_norm": 0.4517601043024772, "acc_norm_stderr": 0.012710662233660245 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6580882352941176, "acc_stderr": 0.02881472242225419, "acc_norm": 0.6580882352941176, "acc_norm_stderr": 0.02881472242225419 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6372549019607843, "acc_stderr": 0.019450768432505514, "acc_norm": 0.6372549019607843, "acc_norm_stderr": 0.019450768432505514 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "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": 0.8507462686567164, "acc_stderr": 0.02519692987482706, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482706 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness|hendrycksTest-virology|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835816, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835816 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness|truthfulqa:mc|0": { "mc1": 0.3525091799265606, "mc1_stderr": 0.016724646380756544, "mc2": 0.5214295545813499, "mc2_stderr": 0.015004393759780037 }, "harness|winogrande|5": { "acc": 0.7821625887924231, "acc_stderr": 0.011601066079939324 }, "harness|gsm8k|5": { "acc": 0.6050037907505686, "acc_stderr": 0.013465354969973198 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

chats-bug/multiple-subject-gen

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: subject_lines dtype: string - name: text dtype: string splits: - name: train num_bytes: 78493229 num_examples: 59489 - name: test num_bytes: 4030472 num_examples: 3132 download_size: 10833380 dataset_size: 82523701 --- # Dataset Card for "multiple-subject-gen" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fewshot-goes-multilingual/cs_facebook-comments

--- annotations_creators: - found language: - cs language_creators: - found license: - cc-by-nc-sa-3.0 multilinguality: - monolingual pretty_name: Czech Facebook comments size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - text-classification task_ids: - sentiment-classification --- # Dataset Card for Czech Facebook comments ## Dataset Description The dataset contains user comments from Facebook. Each comment contains text, sentiment (positive/negative/neutral). The dataset has in total (train+validation+test) 6,600 reviews. The data is balanced. ## Dataset Features Each sample contains: - `comment_id`: unique string identifier of the comment. - `sentiment_str`: string representation of the rating - "pozitivní" / "neutrální" / "negativní" - `sentiment_int`: integer representation of the rating (1=positive, 0=neutral, -1=negative) - `comment`: the string of the comment ## Dataset Source The data is a processed adaptation of [Facebook CZ Corpus](https://liks.fav.zcu.cz/sentiment/). This adaptation is label-balanced.

GEM/wiki_cat_sum

--- annotations_creators: - automatically-created language_creators: - unknown language: - en license: - cc-by-sa-3.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - summarization task_ids: [] pretty_name: wiki_cat_sum --- # Dataset Card for GEM/wiki_cat_sum ## Dataset Description - **Homepage:** https://github.com/lauhaide/WikiCatSum - **Repository:** https://datashare.ed.ac.uk/handle/10283/3368 - **Paper:** https://arxiv.org/abs/1906.04687 - **Leaderboard:** N/A - **Point of Contact:** Laura Perez-Beltrachini ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/wiki_cat_sum). ### Dataset Summary WikiCatSum is an English summarization dataset in three domains: animals, companies, and film. It provides multiple paragraphs of text paired with a summary of the paragraphs. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/wiki_cat_sum') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/wiki_cat_sum). #### website [Github](https://github.com/lauhaide/WikiCatSum) #### paper [Arxiv](https://arxiv.org/abs/1906.04687) #### authors Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain) ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage   [Github](https://github.com/lauhaide/WikiCatSum) #### Download   [Website](https://datashare.ed.ac.uk/handle/10283/3368) #### Paper   [Arxiv](https://arxiv.org/abs/1906.04687) #### BibTex   ``` @inproceedings{perez-beltrachini-etal-2019-generating, title = "Generating Summaries with Topic Templates and Structured Convolutional Decoders", author = "Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1504", doi = "10.18653/v1/P19-1504", } ``` #### Contact Name    Laura Perez-Beltrachini #### Contact Email   lperez@ed.ac.uk #### Has a Leaderboard?   no ### Languages and Intended Use #### Multilingual?    no #### Covered Languages    `English` #### License    cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported #### Intended Use   Research on multi-document abstractive summarisation. #### Primary Task   Summarization #### Communicative Goal    Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents. ### Credit #### Curation Organization Type(s)   `industry`, `academic` #### Curation Organization(s)   Google Cloud Platform, University of Edinburgh #### Dataset Creators   Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain) #### Funding   Google Cloud Platform, European Research Council #### Who added the Dataset to GEM?   Ronald Cardenas (University of Edinburgh) Laura Perez-Beltrachini (University of Edinburgh) ### Dataset Structure #### Data Fields   - `id`: ID of the data example - `title`: Is the Wikipedia article's title - `paragraphs`: Is the ranked list of paragraphs from the set of crawled texts - `summary`: Is constituted by a list of sentences together with their corresponding topic label #### Example Instance   This is a truncated example from the animal setting: ``` {'gem_id': 'animal-train-1', 'gem_parent_id': 'animal-train-1', 'id': '2652', 'paragraphs': ["lytrosis (hulst) of louisiana vernon antoine brou jr. 2005. southern lepidopterists' news, 27: 7 ., ..."], 'references': ['lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.'], 'summary': {'text': ['lytrosis unitaria , the common lytrosis moth , is a species of moth of the geometridae family .', 'it is found in north america , including arkansas , georgia , iowa , massachusetts , new hampshire , new jersey , new york , north carolina , ohio , oklahoma , ontario , pennsylvania , south carolina , tennessee , texas , virginia , west virginia and wisconsin .', 'the wingspan is about 50 mm .', 'the larvae feed on rosa , crataegus , amelanchier , acer , quercus and viburnum species . '], 'topic': [29, 20, 9, 8]}, 'target': 'lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.', 'title': 'lytrosis unitaria'} ``` #### Data Splits   Nb of instances in train/valid/test are 50,938/2,855/2,831 #### Splitting Criteria   The data was split i.i.d., i.e. uniformly split into training, validation, and test datasets. ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM?   Evaluation of models' performance on noisy (document, summary) pairs and long inputs. Evaluate models' capabilities to generalise and mitigate biases. #### Similar Datasets   no #### Unique Language Coverage   no #### Ability that the Dataset measures   Capabilities to generalise, mitigate biases, factual correctness. ### GEM-Specific Curation #### Modificatied for GEM?   yes #### GEM Modifications   `annotations added` #### Modification Details   We provide topic labels for summary sentences. #### Additional Splits?   no ### Getting Started with the Task #### Pointers to Resources   - [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198) - [Generating Summaries with Topic Templates and Structured Convolutional Decoders](https://arxiv.org/abs/1906.04687) - [Noisy Self-Knowledge Distillation for Text Summarization](https://arxiv.org/abs/2009.07032) And all references in these papers. ## Previous Results ### Previous Results #### Measured Model Abilities   Capabilities to generalise, mitigate biases, factual correctness. #### Metrics   `ROUGE`, `BERT-Score`, `MoverScore`, `Other: Other Metrics` #### Other Metrics   - Abstract/Copy - Factual accuracy based on the score of (Goodrich et al., 2019) and the relation extraction system of (Sorokin and Gurevych, 2017). #### Proposed Evaluation   Human based are Question Answering and Ranking (Content, Fluency and Repetition) #### Previous results available?   yes #### Other Evaluation Approaches   Those listed above. #### Relevant Previous Results   Generating Summaries with Topic Templates and Structured Convolutional Decoders https://arxiv.org/abs/1906.04687 Noisy Self-Knowledge Distillation for Text Summarization https://arxiv.org/abs/2009.07032 ## Dataset Curation ### Original Curation #### Original Curation Rationale   The dataset is a subset of the WikiSum (Liu et al., 2018) dataset focusing on summaries of entities in three domains (Film, Company, and Animal). It is multi-document summarisation where input-output pairs for each example entity are created as follows. The input is a set of paragraphs collected from i) documents in the Reference section of the entity's Wikipedia page plus ii) documents collected from the top ten search results after querying Google search engine with the entity name. The output summary is the Wikipedia abstract for the entity. #### Communicative Goal   Generate descriptive summaries with specific domains, where certain topics are discussed and generally in specific orders. #### Sourced from Different Sources   yes #### Source Details   WikiSum (Liu et al., 2018) ### Language Data #### How was Language Data Obtained?   `Other` #### Topics Covered   The dataset and task focuses on summaries for entities in three domains: Company, Film, and Animal. #### Data Validation   not validated #### Data Preprocessing   Summary sentences are associated with a topic label. There is a topic model for each domain. #### Was Data Filtered?   not filtered ### Structured Annotations #### Additional Annotations?    automatically created #### Annotation Service?   no #### Annotation Values   Each summary sentences was annotated with a topic label. There is a topic model for each of the three domains. This was used to guide a hierarchical decoder. #### Any Quality Control?   validated by data curators #### Quality Control Details   Manual inspection of a sample of topics assigned to sentences. The number of topics was selected based on the performance of the summarisation model. ### Consent #### Any Consent Policy?   no #### Justification for Using the Data   The dataset is base on Wikipedia and referenced and retrieved documents crawled from the Web. ### Private Identifying Information (PII) #### Contains PII?    unlikely #### Any PII Identification?   no identification ### Maintenance #### Any Maintenance Plan?   no ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data   no ### Impact on Under-Served Communities #### Addresses needs of underserved Communities?   no ### Discussion of Biases #### Any Documented Social Biases?   yes #### Links and Summaries of Analysis Work   This dataset is based on Wikipedia and thus biases analysis on other Wikipedia-based datasets are potentially true for WikiCatSum. For instance, see analysis for the ToTTo dataset here [1]. [1] Automatic Construction of Evaluation Suites for Natural Language Generation Datasets https://openreview.net/forum?id=CSi1eu_2q96 ## Considerations for Using the Data ### PII Risks and Liability ### Licenses #### Copyright Restrictions on the Dataset   `public domain` #### Copyright Restrictions on the Language Data   `public domain` ### Known Technical Limitations

Amitnaik1718/indian_food_images

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': burger '1': butter_naan '2': chai '3': chapati '4': chole_bhature '5': dal_makhani '6': dhokla '7': fried_rice '8': idli '9': jalebi '10': kaathi_rolls '11': kadai_paneer '12': kulfi '13': masala_dosa '14': momos '15': paani_puri '16': pakode '17': pav_bhaji '18': pizza '19': samosa splits: - name: train num_bytes: 1269067280.1594334 num_examples: 5328 - name: test num_bytes: 229322892.3925666 num_examples: 941 download_size: 1601553689 dataset_size: 1498390172.552 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

umesh16071973/HRMS_training_Dataset

--- license: apache-2.0 ---

allandclive/Luganda_news_articles

--- task_categories: - text2text-generation - text-generation language: - lg size_categories: - 10K<n<100K --- # Luganda News Articles Luganda (lug) is one of the most spoken languages in Uganda. Scrapped from https://www.bukedde.co.ug/ & https://gambuuze.ug/

davanstrien/satclip

--- tags: - geospatial pretty_name: S2-100K --- # Dataset Card for S2-100K  > The S2-100K dataset is a dataset of 100,000 multi-spectral satellite images sampled from Sentinel-2 via the Microsoft Planetary Computer. Copernicus Sentinel data is captured between Jan 1, 2021 and May 17, 2023. The dataset is sampled approximately uniformly over landmass and only includes images without cloud coverage. The dataset is available for research purposes only. If you use the dataset, please cite our paper. More information on the dataset can be found in our paper. See this [GitHub repo](https://github.com/microsoft/satclip/) for more details. ## Dataset Details ### Dataset Description > SatCLIP trains location and image encoders via contrastive learning, by matching images to their corresponding locations. This is analogous to the CLIP approach, which matches images to their corresponding text. > Through this process, the location encoder learns characteristics of a location, as represented by satellite imagery. - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  To download the dataset you can use the `huggingface_hub` library. ```python from huggingface_hub import snapshot_download snapshot_download("davanstrien/satclip", local_dir='.', repo_type='dataset') ``` Alternatively you can run ```bash # Make sure you have git-lfs installed (https://git-lfs.com) git lfs install git clone https://huggingface.co/datasets/davanstrien/satclip ``` To extract the images you can run the following command. ```bash ls image/*.tar.xz |xargs -n1 tar -xzf ``` ## Citation  **BibTeX:** ``` @article{klemmer2023satclip, title={SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery}, author={Klemmer, Konstantin and Rolf, Esther and Robinson, Caleb and Mackey, Lester and Ru{\ss}wurm, Marc}, journal={arXiv preprint arXiv:2311.17179}, year={2023} } ```

Locutusque/hyperion-v3.0

--- license: apache-2.0 dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: float64 - name: source dtype: string splits: - name: train num_bytes: 3210068995.811935 num_examples: 1665372 download_size: 1497036692 dataset_size: 3210068995.811935 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering - text-generation language: - en size_categories: - 1M<n<10M --- Hyperion-3.0 has significantly improved performance over its predecessors. "I found that having more code datasets than general purpose datasets ironically decreases performance in both coding and general tasks." Data sources: - OpenOrca/SlimOrca - cognitivecomputations/dolphin (300k examples) - microsoft/orca-math-word-problems-200k (60k examples) - glaiveai/glaive-code-assistant - Vezora/Tested-22k-Python-Alpaca - Unnatural Instructions - BI55/MedText - LDJnr/Pure-Dove - Various domain-specific datasets by Camel - teknium/GPTeacher-General-Instruct - WizardLM Evol Instruct 70K and 140K - Various chat log datasets by Collective Cognition - totally-not-an-llm/EverythingLM-data-V3 - Crystalcareai/alpaca-gpt4-COT - m-a-p/Code-Feedback - Various medical datasets by CogStack - jondurbin/airoboros-3.2 - garage-bAInd/Open-Platypus - Lmsys chat 1M - GPT-4 Generations only - FuseAI/FuseChat-Mixture - abacusai/SystemChat - Locutusque/ToM-sharegpt4 - Locutusque/arc-cot

thesven/bengali-ai-train-set-tiny

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: valid num_bytes: 961362832 num_examples: 1000 - name: train num_bytes: 9612150048 num_examples: 10000 download_size: 1670313269 dataset_size: 10573512880 --- # Dataset Card for "bengali-ai-train-set-tiny" # Dataset Description - **Homepage:** [OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking](https://arxiv.org/abs/2305.09688) - **Paper:** [OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking](https://arxiv.org/abs/2305.09688) ### Whisper Model Information - **Model Homepage:** [openai/whisper-tiny on Hugging Face](https://huggingface.co/openai/whisper-tiny) - **Model Paper:** [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) ## Dataset Summary This dataset is designed to help finetune the `openai/whisper-tiny` model with additional information in the Bengali language. It consists of an additional 11,000 labeled audio samples from the OOD-Speech dataset, specifically designed for out-of-distribution benchmarking in Bengali. ## Supported Tasks and Leaderboards The primary task supported by this dataset is automatic speech recognition (ASR) in the Bengali language, specifically for finetuning the `openai/whisper-tiny` model. ## Languages The dataset is in Bengali. ## Dataset Structure ### Data Instances Each instance in the dataset consists of an audio sample in Bengali along with its corresponding transcription. ### Data Fields - `audio`: The audio sample in Bengali. - `transcription`: The corresponding transcription of the audio sample in Bengali. ### Data Splits The dataset is split into training and validation sets. The training set consists of 10,000 samples, and the validation set consists of 1,000 samples. ## Additional Information ### Dataset Curators The dataset has been curated by "thesven". ### Licensing Information Licensing information for the OOD-Speech dataset can be found in the original paper. ### Citation Information @article{OOD-Speech2023, title={OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking}, author={Authors of the OOD-Speech paper}, journal={arXiv preprint arXiv:2305.09688}, year={2023} }

Bonfire79/clinrec_01

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 178226 num_examples: 134 download_size: 61701 dataset_size: 178226 configs: - config_name: default data_files: - split: train path: data/train-* ---

pkuHaowei/stanford-cars

--- dataset_info: features: - name: image dtype: image - name: label dtype: int64 splits: - name: train num_bytes: 245338060.0 num_examples: 8144 - name: test num_bytes: 241985926.875 num_examples: 8041 download_size: 482701950 dataset_size: 487323986.875 --- # Dataset Card for "stanford-cars" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

proserve/medical-instruct-mixer

--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 889897065.0 num_examples: 528642 - name: test num_bytes: 68932987.0 num_examples: 28501 download_size: 482795421 dataset_size: 958830052.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Sijuade/Cats-Dogs-Birds

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' splits: - name: train num_bytes: 2858440330.32 num_examples: 13344 download_size: 2752316017 dataset_size: 2858440330.32 ---

arieg/color_spec_cls

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '10' '1': '140' '2': '141' '3': '190' '4': '193' '5': '194' '6': '197' '7': '2' '8': '200' '9': '5' splits: - name: train num_bytes: 10354796.0 num_examples: 100 download_size: 10356873 dataset_size: 10354796.0 --- # Dataset Card for "color_spec_cls" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

roszcz/tmp-midi-clip

--- dataset_info: features: - name: midi_filename dtype: string - name: pitch sequence: int16 length: 32 - name: dstart_bin sequence: int16 length: 32 - name: duration_bin sequence: int16 length: 32 - name: velocity_bin sequence: int16 length: 32 splits: - name: train num_bytes: 118752197 num_examples: 352232 - name: validation num_bytes: 13434506 num_examples: 39754 - name: test num_bytes: 15540656 num_examples: 46073 download_size: 21481498 dataset_size: 147727359 --- # Dataset Card for "tmp-midi-clip" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tarasabkar/IEMOCAP_Speech

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: emotion dtype: class_label: names: 0: ang 1: hap 2: neu 3: sad splits: - name: Session1 num_bytes: 167102058.95 num_examples: 1085 - name: Session2 num_bytes: 150799933.454 num_examples: 1023 - name: Session3 num_bytes: 167088514.51 num_examples: 1151 - name: Session4 num_bytes: 145505839.808 num_examples: 1031 - name: Session5 num_bytes: 170307009.46 num_examples: 1241 download_size: 788399921 dataset_size: 800803356.182 --- # Dataset Card for "IEMOCAP_Speech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ShariThomas/dataset_sample

--- license: mit ---

Ammar-Azman/crawl-doktorbudak

--- license: mit ---

CyberHarem/mizusawa_matsuri_citrus

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Mizusawa Matsuri This is the dataset of Mizusawa Matsuri, containing 90 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 | 90 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 197 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 233 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 90 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 90 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 90 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 197 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 197 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 171 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 233 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 233 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

tyzhu/find_first_sent_train_100_eval_10_dec

--- configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string - name: text dtype: string splits: - name: validation num_bytes: 11337 num_examples: 10 - name: train num_bytes: 379104 num_examples: 210 download_size: 197674 dataset_size: 390441 --- # Dataset Card for "find_first_sent_train_100_eval_10_dec" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gentilrenard/lmd_ukraine_comments

--- language: - fr license: mit size_categories: - 100K<n<1M task_categories: - text-classification pretty_name: Comments under Le Monde Ukraine war articles (1 year) dataset_info: features: - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 133853 num_examples: 323 - name: validation num_bytes: 54736 num_examples: 139 - name: unlabeled num_bytes: 64192366 num_examples: 174891 download_size: 39789476 dataset_size: 64380955 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: unlabeled path: data/unlabeled-* --- ## Comments under Le Monde Ukraine War Articles (1 Year) ### Description This dataset contains 175k comments extracted from Le Monde articles about the Ukraine war during its first year (February 2022 to 2023). Among these, around 500 comments are manually labeled into categories: 0. Explicit support for Ukraine, 1. pro Russia, 2. "Other". ### Dataset Structure #### Features - `text`: The comment text (string). - `label`: The label for the comment (integer). The labels are as follows: - 0: pro_Ukraine - 1: pro_Russia - 2: other - 4: no_label (the unlabeled data). #### Splits Train and validation are manually labeled. Unlabeled data could be used for knowledge distillation for instance. - `train`: 323 examples. - `validation`: 139 examples. - `unlabeled`: 174,891 examples. ### Additional Information - **Homepage**: [Project Repository](https://github.com/matthieuvion/lmd_classi) - **License**: MIT License - **Language**: French - **Task Categories**: Text Classification - **Size Categories**: 100K < n < 1M

ml6team/xsum_nl

--- annotations_creators: - machine-generated language_creators: - machine-generated language: - nl language_bcp47: - nl-BE license: - unknown multilinguality: - monolingual pretty_name: XSum NL size_categories: - unknown source_datasets: - extended|xsum task_categories: - conditional-text-generation task_ids: - summarization --- # Dataset Card for XSum NL ## 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:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is a machine translated dataset. It's the [XSum dataset](https://huggingface.co/datasets/xsum) translated with [this model](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) from English to Dutch. See the [Hugginface page of the original dataset](https://huggingface.co/datasets/xsum) for more information on the format of this dataset. Use with: ```python from datasets import load_dataset load_dataset("csv", "ml6team/xsum_nl") ``` ### Languages Dutch ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `id`: BBC ID of the article. - `document`: a string containing the body of the news article - `summary`: a string containing a one sentence summary of the article. ### Data Splits - `train` - `test` - `validation` ## 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 Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

jtgot/ServicesClassificationData

--- license: apache-2.0 ---

MruganKulkarni/restomenuu

--- license: mit ---

Circularmachines/batch_indexing_machine_230529_006

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 156741720.0 num_examples: 720 download_size: 156752582 dataset_size: 156741720.0 --- # Dataset Card for "batch_indexing_machine_230529_006" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

diguinn17/diguito

--- license: openrail ---

open-llm-leaderboard/details_sail__Sailor-7B

--- pretty_name: Evaluation run of sail/Sailor-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [sail/Sailor-7B](https://huggingface.co/sail/Sailor-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 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_sail__Sailor-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-03T06:19:10.406963](https://huggingface.co/datasets/open-llm-leaderboard/details_sail__Sailor-7B/blob/main/results_2024-03-03T06-19-10.406963.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\": 0.5442833810166537,\n\ \ \"acc_stderr\": 0.0340188362831904,\n \"acc_norm\": 0.5493690106415303,\n\ \ \"acc_norm_stderr\": 0.03472015784641931,\n \"mc1\": 0.2607099143206854,\n\ \ \"mc1_stderr\": 0.015368841620766373,\n \"mc2\": 0.40083895524705915,\n\ \ \"mc2_stderr\": 0.013870866160876278\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4718430034129693,\n \"acc_stderr\": 0.014588204105102202,\n\ \ \"acc_norm\": 0.49829351535836175,\n \"acc_norm_stderr\": 0.014611305705056992\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5520812587134037,\n\ \ \"acc_stderr\": 0.0049626384463959845,\n \"acc_norm\": 0.7620991834295957,\n\ \ \"acc_norm_stderr\": 0.004249278842903416\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n\ \ \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n\ \ \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5855263157894737,\n \"acc_stderr\": 0.04008973785779206,\n\ \ \"acc_norm\": 0.5855263157894737,\n \"acc_norm_stderr\": 0.04008973785779206\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5735849056603773,\n \"acc_stderr\": 0.030437794342983056,\n\ \ \"acc_norm\": 0.5735849056603773,\n \"acc_norm_stderr\": 0.030437794342983056\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5416666666666666,\n\ \ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n\ \ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.4682080924855491,\n\ \ \"acc_stderr\": 0.03804749744364763,\n \"acc_norm\": 0.4682080924855491,\n\ \ \"acc_norm_stderr\": 0.03804749744364763\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.044405219061793275,\n\ \ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.044405219061793275\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.71,\n \"acc_stderr\": 0.04560480215720685,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.04560480215720685\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.49361702127659574,\n \"acc_stderr\": 0.032683358999363366,\n\ \ \"acc_norm\": 0.49361702127659574,\n \"acc_norm_stderr\": 0.032683358999363366\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n\ \ \"acc_stderr\": 0.04404556157374767,\n \"acc_norm\": 0.32456140350877194,\n\ \ \"acc_norm_stderr\": 0.04404556157374767\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.37037037037037035,\n \"acc_stderr\": 0.024870815251057093,\n \"\ acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.024870815251057093\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n\ \ \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n\ \ \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.635483870967742,\n\ \ \"acc_stderr\": 0.027379871229943252,\n \"acc_norm\": 0.635483870967742,\n\ \ \"acc_norm_stderr\": 0.027379871229943252\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.03465304488406795,\n\ \ \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.03465304488406795\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n\ \ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.03742597043806586,\n\ \ \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.03742597043806586\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6868686868686869,\n \"acc_stderr\": 0.033042050878136525,\n \"\ acc_norm\": 0.6868686868686869,\n \"acc_norm_stderr\": 0.033042050878136525\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7564766839378239,\n \"acc_stderr\": 0.030975436386845443,\n\ \ \"acc_norm\": 0.7564766839378239,\n \"acc_norm_stderr\": 0.030975436386845443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.4948717948717949,\n \"acc_stderr\": 0.025349672906838653,\n\ \ \"acc_norm\": 0.4948717948717949,\n \"acc_norm_stderr\": 0.025349672906838653\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \ \ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5126050420168067,\n \"acc_stderr\": 0.032468167657521745,\n\ \ \"acc_norm\": 0.5126050420168067,\n \"acc_norm_stderr\": 0.032468167657521745\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7174311926605504,\n \"acc_stderr\": 0.019304243497707152,\n \"\ acc_norm\": 0.7174311926605504,\n \"acc_norm_stderr\": 0.019304243497707152\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.03395322726375797,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.03395322726375797\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7254901960784313,\n \"acc_stderr\": 0.031321798030832904,\n \"\ acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.031321798030832904\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6666666666666666,\n \"acc_stderr\": 0.0306858205966108,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.0306858205966108\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\ \ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\ \ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.042258754519696365,\n\ \ \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.042258754519696365\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6859504132231405,\n \"acc_stderr\": 0.042369647530410184,\n \"\ acc_norm\": 0.6859504132231405,\n \"acc_norm_stderr\": 0.042369647530410184\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5740740740740741,\n\ \ \"acc_stderr\": 0.0478034362693679,\n \"acc_norm\": 0.5740740740740741,\n\ \ \"acc_norm_stderr\": 0.0478034362693679\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6134969325153374,\n \"acc_stderr\": 0.03825825548848607,\n\ \ \"acc_norm\": 0.6134969325153374,\n \"acc_norm_stderr\": 0.03825825548848607\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.04453254836326468,\n\ \ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.04453254836326468\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n\ \ \"acc_stderr\": 0.025140935950335428,\n \"acc_norm\": 0.8205128205128205,\n\ \ \"acc_norm_stderr\": 0.025140935950335428\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7266922094508301,\n\ \ \"acc_stderr\": 0.015936681062628556,\n \"acc_norm\": 0.7266922094508301,\n\ \ \"acc_norm_stderr\": 0.015936681062628556\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.026483392042098174,\n\ \ \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.026483392042098174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24581005586592178,\n\ \ \"acc_stderr\": 0.014400296429225629,\n \"acc_norm\": 0.24581005586592178,\n\ \ \"acc_norm_stderr\": 0.014400296429225629\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6111111111111112,\n \"acc_stderr\": 0.02791405551046801,\n\ \ \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.02791405551046801\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6205787781350482,\n\ \ \"acc_stderr\": 0.027559949802347824,\n \"acc_norm\": 0.6205787781350482,\n\ \ \"acc_norm_stderr\": 0.027559949802347824\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6203703703703703,\n \"acc_stderr\": 0.027002521034516478,\n\ \ \"acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.027002521034516478\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.41134751773049644,\n \"acc_stderr\": 0.029354911159940975,\n \ \ \"acc_norm\": 0.41134751773049644,\n \"acc_norm_stderr\": 0.029354911159940975\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.37614080834419816,\n\ \ \"acc_stderr\": 0.012372214430599826,\n \"acc_norm\": 0.37614080834419816,\n\ \ \"acc_norm_stderr\": 0.012372214430599826\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.030372836961539352,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.030372836961539352\n \ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\"\ : 0.5228758169934641,\n \"acc_stderr\": 0.020206653187884786,\n \"\ acc_norm\": 0.5228758169934641,\n \"acc_norm_stderr\": 0.020206653187884786\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\ \ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\ \ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6612244897959184,\n \"acc_stderr\": 0.030299506562154185,\n\ \ \"acc_norm\": 0.6612244897959184,\n \"acc_norm_stderr\": 0.030299506562154185\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7313432835820896,\n\ \ \"acc_stderr\": 0.03134328358208954,\n \"acc_norm\": 0.7313432835820896,\n\ \ \"acc_norm_stderr\": 0.03134328358208954\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.695906432748538,\n \"acc_stderr\": 0.0352821125824523,\n\ \ \"acc_norm\": 0.695906432748538,\n \"acc_norm_stderr\": 0.0352821125824523\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2607099143206854,\n\ \ \"mc1_stderr\": 0.015368841620766373,\n \"mc2\": 0.40083895524705915,\n\ \ \"mc2_stderr\": 0.013870866160876278\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.691397000789266,\n \"acc_stderr\": 0.012982160200926574\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.33358605003790753,\n \ \ \"acc_stderr\": 0.012987282131410809\n }\n}\n```" repo_url: https://huggingface.co/sail/Sailor-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_03_02T22_29_37.991675 path: - '**/details_harness|arc:challenge|25_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|arc:challenge|25_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-03T06-19-10.406963.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|gsm8k|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|gsm8k|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hellaswag|10_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hellaswag|10_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-02T22-29-37.991675.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-03T06-19-10.406963.parquet' 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'**/details_harness|hendrycksTest-public_relations|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-03T06-19-10.406963.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-management|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-management|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T06-19-10.406963.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|truthfulqa:mc|0_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|truthfulqa:mc|0_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-03T06-19-10.406963.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_02T22_29_37.991675 path: - '**/details_harness|winogrande|5_2024-03-02T22-29-37.991675.parquet' - split: 2024_03_03T06_19_10.406963 path: - '**/details_harness|winogrande|5_2024-03-03T06-19-10.406963.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-03T06-19-10.406963.parquet' - config_name: results data_files: - split: 2024_03_02T22_29_37.991675 path: - results_2024-03-02T22-29-37.991675.parquet - split: 2024_03_03T06_19_10.406963 path: - results_2024-03-03T06-19-10.406963.parquet - split: latest path: - results_2024-03-03T06-19-10.406963.parquet --- # Dataset Card for Evaluation run of sail/Sailor-7B  Dataset automatically created during the evaluation run of model [sail/Sailor-7B](https://huggingface.co/sail/Sailor-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_sail__Sailor-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-03T06:19:10.406963](https://huggingface.co/datasets/open-llm-leaderboard/details_sail__Sailor-7B/blob/main/results_2024-03-03T06-19-10.406963.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": 0.5442833810166537, "acc_stderr": 0.0340188362831904, "acc_norm": 0.5493690106415303, "acc_norm_stderr": 0.03472015784641931, "mc1": 0.2607099143206854, "mc1_stderr": 0.015368841620766373, "mc2": 0.40083895524705915, "mc2_stderr": 0.013870866160876278 }, "harness|arc:challenge|25": { "acc": 0.4718430034129693, "acc_stderr": 0.014588204105102202, "acc_norm": 0.49829351535836175, "acc_norm_stderr": 0.014611305705056992 }, "harness|hellaswag|10": { "acc": 0.5520812587134037, "acc_stderr": 0.0049626384463959845, "acc_norm": 0.7620991834295957, "acc_norm_stderr": 0.004249278842903416 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4740740740740741, "acc_stderr": 0.04313531696750574, "acc_norm": 0.4740740740740741, "acc_norm_stderr": 0.04313531696750574 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5855263157894737, "acc_stderr": 0.04008973785779206, "acc_norm": 0.5855263157894737, "acc_norm_stderr": 0.04008973785779206 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5735849056603773, "acc_stderr": 0.030437794342983056, "acc_norm": 0.5735849056603773, "acc_norm_stderr": 0.030437794342983056 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5416666666666666, "acc_stderr": 0.04166666666666665, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.04166666666666665 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4682080924855491, "acc_stderr": 0.03804749744364763, "acc_norm": 0.4682080924855491, "acc_norm_stderr": 0.03804749744364763 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.27450980392156865, "acc_stderr": 0.044405219061793275, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.044405219061793275 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.71, "acc_stderr": 0.04560480215720685, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720685 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.49361702127659574, "acc_stderr": 0.032683358999363366, "acc_norm": 0.49361702127659574, "acc_norm_stderr": 0.032683358999363366 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.32456140350877194, "acc_stderr": 0.04404556157374767, "acc_norm": 0.32456140350877194, "acc_norm_stderr": 0.04404556157374767 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728763, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.37037037037037035, "acc_stderr": 0.024870815251057093, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.024870815251057093 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.373015873015873, "acc_stderr": 0.04325506042017086, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.04325506042017086 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.635483870967742, "acc_stderr": 0.027379871229943252, "acc_norm": 0.635483870967742, "acc_norm_stderr": 0.027379871229943252 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.41379310344827586, "acc_stderr": 0.03465304488406795, "acc_norm": 0.41379310344827586, "acc_norm_stderr": 0.03465304488406795 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6424242424242425, "acc_stderr": 0.03742597043806586, "acc_norm": 0.6424242424242425, "acc_norm_stderr": 0.03742597043806586 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6868686868686869, "acc_stderr": 0.033042050878136525, "acc_norm": 0.6868686868686869, "acc_norm_stderr": 0.033042050878136525 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7564766839378239, "acc_stderr": 0.030975436386845443, "acc_norm": 0.7564766839378239, "acc_norm_stderr": 0.030975436386845443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4948717948717949, "acc_stderr": 0.025349672906838653, "acc_norm": 0.4948717948717949, "acc_norm_stderr": 0.025349672906838653 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2740740740740741, "acc_stderr": 0.027195934804085626, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.027195934804085626 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5126050420168067, "acc_stderr": 0.032468167657521745, "acc_norm": 0.5126050420168067, "acc_norm_stderr": 0.032468167657521745 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.037101857261199946, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.037101857261199946 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7174311926605504, "acc_stderr": 0.019304243497707152, "acc_norm": 0.7174311926605504, "acc_norm_stderr": 0.019304243497707152 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4537037037037037, "acc_stderr": 0.03395322726375797, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.03395322726375797 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7254901960784313, "acc_stderr": 0.031321798030832904, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.031321798030832904 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6666666666666666, "acc_stderr": 0.0306858205966108, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.0306858205966108 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6547085201793722, "acc_stderr": 0.03191100192835794, "acc_norm": 0.6547085201793722, "acc_norm_stderr": 0.03191100192835794 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6335877862595419, "acc_stderr": 0.042258754519696365, "acc_norm": 0.6335877862595419, "acc_norm_stderr": 0.042258754519696365 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6859504132231405, "acc_stderr": 0.042369647530410184, "acc_norm": 0.6859504132231405, "acc_norm_stderr": 0.042369647530410184 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5740740740740741, "acc_stderr": 0.0478034362693679, "acc_norm": 0.5740740740740741, "acc_norm_stderr": 0.0478034362693679 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6134969325153374, "acc_stderr": 0.03825825548848607, "acc_norm": 0.6134969325153374, "acc_norm_stderr": 0.03825825548848607 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5178571428571429, "acc_stderr": 0.047427623612430116, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.7184466019417476, "acc_stderr": 0.04453254836326468, "acc_norm": 0.7184466019417476, "acc_norm_stderr": 0.04453254836326468 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8205128205128205, "acc_stderr": 0.025140935950335428, "acc_norm": 0.8205128205128205, "acc_norm_stderr": 0.025140935950335428 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7266922094508301, "acc_stderr": 0.015936681062628556, "acc_norm": 0.7266922094508301, "acc_norm_stderr": 0.015936681062628556 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5895953757225434, "acc_stderr": 0.026483392042098174, "acc_norm": 0.5895953757225434, "acc_norm_stderr": 0.026483392042098174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24581005586592178, "acc_stderr": 0.014400296429225629, "acc_norm": 0.24581005586592178, "acc_norm_stderr": 0.014400296429225629 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6111111111111112, "acc_stderr": 0.02791405551046801, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.02791405551046801 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6205787781350482, "acc_stderr": 0.027559949802347824, "acc_norm": 0.6205787781350482, "acc_norm_stderr": 0.027559949802347824 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6203703703703703, "acc_stderr": 0.027002521034516478, "acc_norm": 0.6203703703703703, "acc_norm_stderr": 0.027002521034516478 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.41134751773049644, "acc_stderr": 0.029354911159940975, "acc_norm": 0.41134751773049644, "acc_norm_stderr": 0.029354911159940975 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.37614080834419816, "acc_stderr": 0.012372214430599826, "acc_norm": 0.37614080834419816, "acc_norm_stderr": 0.012372214430599826 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5, "acc_stderr": 0.030372836961539352, "acc_norm": 0.5, "acc_norm_stderr": 0.030372836961539352 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5228758169934641, "acc_stderr": 0.020206653187884786, "acc_norm": 0.5228758169934641, "acc_norm_stderr": 0.020206653187884786 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6181818181818182, "acc_stderr": 0.046534298079135075, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.046534298079135075 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6612244897959184, "acc_stderr": 0.030299506562154185, "acc_norm": 0.6612244897959184, "acc_norm_stderr": 0.030299506562154185 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7313432835820896, "acc_stderr": 0.03134328358208954, "acc_norm": 0.7313432835820896, "acc_norm_stderr": 0.03134328358208954 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness|hendrycksTest-virology|5": { "acc": 0.45180722891566266, "acc_stderr": 0.03874371556587953, "acc_norm": 0.45180722891566266, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.695906432748538, "acc_stderr": 0.0352821125824523, "acc_norm": 0.695906432748538, "acc_norm_stderr": 0.0352821125824523 }, "harness|truthfulqa:mc|0": { "mc1": 0.2607099143206854, "mc1_stderr": 0.015368841620766373, "mc2": 0.40083895524705915, "mc2_stderr": 0.013870866160876278 }, "harness|winogrande|5": { "acc": 0.691397000789266, "acc_stderr": 0.012982160200926574 }, "harness|gsm8k|5": { "acc": 0.33358605003790753, "acc_stderr": 0.012987282131410809 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

danjacobellis/audio_har_descript_44kHz_frames_1200

--- dataset_info: features: - name: codes dtype: array2_d: shape: - 9 - 640 dtype: float32 - name: label dtype: class_label: names: '0': No Activity '1': Writing '2': Drawing '3': Cutting paper '4': Typing on keyboard '5': Typing on phone '6': Browsing on phone '7': Clapping '8': Shuffling cards '9': Scratching '10': Wiping table '11': Brushing hair '12': Washing hands '13': Drinking '14': Eating snacks '15': Brushing teeth '16': Chopping '17': Grating '18': Frying '19': Sweeping '20': Vacuuming '21': Washing dishes '22': Filling water '23': Using microwave - name: label_str dtype: string - name: participant dtype: int32 splits: - name: train num_bytes: 28945873 num_examples: 669 download_size: 9026774 dataset_size: 28945873 configs: - config_name: default data_files: - split: train path: data/train-* ---

johannes-garstenauer/structs_token_size_4_use_pd_True_full_amt_False_div_20

--- dataset_info: features: - name: struct dtype: string splits: - name: train num_bytes: 25156800 num_examples: 237600 download_size: 7394910 dataset_size: 25156800 --- # Dataset Card for "structs_token_size_4_use_pd_True_full_amt_False_div_20" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

senhorsapo/rem

--- license: openrail ---

mtkinit/Super5473892

--- pretty_name: Super5473892 tags: - aaa --- # Super5473892 Created from AIOD platform

Faiza3/anime_cloth

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 210633.0 num_examples: 15 download_size: 211995 dataset_size: 210633.0 --- # Dataset Card for "anime_cloth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liyucheng/zhihu_rlhf_3k

--- license: cc-by-2.0 ---

CyberHarem/priestess_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Priestess/普瑞赛斯 (Arknights) This is the dataset of Priestess/普瑞赛斯 (Arknights), containing 22 images and their tags. The core tags of this character are `long_hair, hairband, breasts, brown_hair, black_hair, black_hairband, purple_eyes, bow, hair_between_eyes, large_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 | 22 | 28.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/priestess_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 22 | 24.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/priestess_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 43 | 42.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/priestess_arknights/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/priestess_arknights', 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 | 22 | ![](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, looking_at_viewer, smile, simple_background, long_sleeves, white_background, closed_mouth, shirt, upper_body, jacket, open_clothes | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | smile | simple_background | long_sleeves | white_background | closed_mouth | shirt | upper_body | jacket | open_clothes | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:--------------------|:---------------|:-------------------|:---------------|:--------|:-------------|:---------|:---------------| | 0 | 22 | ![](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 |

Deojoandco/reddit-ah-dialogturns-annotations

--- dataset_info: features: - name: id dtype: string - name: speaker dtype: string - name: text dtype: string - name: annotation dtype: string splits: - name: train num_bytes: 3772164 num_examples: 16055 - name: validation num_bytes: 376937 num_examples: 1641 - name: test num_bytes: 360334 num_examples: 1559 download_size: 0 dataset_size: 4509435 --- # Dataset Card for "reddit-ah-dialogturns-annotations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/oklahoma_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane) This is the dataset of oklahoma/オクラホマ/俄克拉荷马 (Azur Lane), containing 28 images and their tags. The core tags of this character are `ahoge, blue_eyes, breasts, hair_between_eyes, blonde_hair, short_hair, bangs, large_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 | 28 | 34.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 28 | 18.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 65 | 39.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 28 | 30.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 65 | 61.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oklahoma_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/oklahoma_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, detached_sleeves, open_mouth, hat, simple_background, :d, boots, brown_gloves, white_background, brown_skirt, cleavage_cutout, long_sleeves, medium_breasts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | detached_sleeves | open_mouth | hat | simple_background | :d | boots | brown_gloves | white_background | brown_skirt | cleavage_cutout | long_sleeves | medium_breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------------|:-------------|:------|:--------------------|:-----|:--------|:---------------|:-------------------|:--------------|:------------------|:---------------|:-----------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

open-llm-leaderboard/details_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough

--- pretty_name: Evaluation run of AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough](https://huggingface.co/AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough)\ \ 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_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-01T17:09:32.814517](https://huggingface.co/datasets/open-llm-leaderboard/details_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough/blob/main/results_2024-03-01T17-09-32.814517.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\": 0.6122781802684721,\n\ \ \"acc_stderr\": 0.032268454851190724,\n \"acc_norm\": 0.625230553768112,\n\ \ \"acc_norm_stderr\": 0.03315794745802012,\n \"mc1\": 0.2582619339045288,\n\ \ \"mc1_stderr\": 0.015321821688476185,\n \"mc2\": 0.5102578228172799,\n\ \ \"mc2_stderr\": 0.01648564659078862\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3924914675767918,\n \"acc_stderr\": 0.014269634635670717,\n\ \ \"acc_norm\": 0.46245733788395904,\n \"acc_norm_stderr\": 0.014570144495075583\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3632742481577375,\n\ \ \"acc_stderr\": 0.004799599840397383,\n \"acc_norm\": 0.5606452897829117,\n\ \ \"acc_norm_stderr\": 0.004952942072999274\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.0373852067611967,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.0373852067611967\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.04940635630605659,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.04940635630605659\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.046774730044911984,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.046774730044911984\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728762,\n\ \ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728762\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\"\ : 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\ \ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.03287666758603491,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.03287666758603491\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217483,\n \"\ acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217483\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768766,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768766\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6435897435897436,\n \"acc_stderr\": 0.024283140529467305,\n\ \ \"acc_norm\": 0.6435897435897436,\n \"acc_norm_stderr\": 0.024283140529467305\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.031204691225150013,\n\ \ \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.031204691225150013\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8293577981651377,\n \"acc_stderr\": 0.016129271025099857,\n \"\ acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.016129271025099857\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\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290902,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290902\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\ \ \"acc_stderr\": 0.03063659134869981,\n \"acc_norm\": 0.7040358744394619,\n\ \ \"acc_norm_stderr\": 0.03063659134869981\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\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\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286774,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286774\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\": 0.7961165048543689,\n \"acc_stderr\": 0.0398913985953177,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.0398913985953177\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.021901905115073325\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\ \ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\ \ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.02433214677913413,\n\ \ \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.02433214677913413\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3743016759776536,\n\ \ \"acc_stderr\": 0.016185444179457175,\n \"acc_norm\": 0.3743016759776536,\n\ \ \"acc_norm_stderr\": 0.016185444179457175\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.026643278474508755,\n\ \ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.026643278474508755\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.02540719779889016,\n\ \ \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.02540719779889016\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44589308996088656,\n\ \ \"acc_stderr\": 0.012695244711379778,\n \"acc_norm\": 0.44589308996088656,\n\ \ \"acc_norm_stderr\": 0.012695244711379778\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681397,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681397\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360378,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360378\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\": 0.7224489795918367,\n \"acc_stderr\": 0.02866685779027465,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.02866685779027465\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2582619339045288,\n\ \ \"mc1_stderr\": 0.015321821688476185,\n \"mc2\": 0.5102578228172799,\n\ \ \"mc2_stderr\": 0.01648564659078862\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7277032359905288,\n \"acc_stderr\": 0.012510697991453937\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough 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_01T17_09_32.814517 path: - '**/details_harness|arc:challenge|25_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-01T17-09-32.814517.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|gsm8k|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hellaswag|10_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-01T17-09-32.814517.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-management|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T17-09-32.814517.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|truthfulqa:mc|0_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-01T17-09-32.814517.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_01T17_09_32.814517 path: - '**/details_harness|winogrande|5_2024-03-01T17-09-32.814517.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-01T17-09-32.814517.parquet' - config_name: results data_files: - split: 2024_03_01T17_09_32.814517 path: - results_2024-03-01T17-09-32.814517.parquet - split: latest path: - results_2024-03-01T17-09-32.814517.parquet --- # Dataset Card for Evaluation run of AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough  Dataset automatically created during the evaluation run of model [AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough](https://huggingface.co/AtAndDev/Ogno-Monarch-Neurotic-9B-Passthrough) 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_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-01T17:09:32.814517](https://huggingface.co/datasets/open-llm-leaderboard/details_AtAndDev__Ogno-Monarch-Neurotic-9B-Passthrough/blob/main/results_2024-03-01T17-09-32.814517.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": 0.6122781802684721, "acc_stderr": 0.032268454851190724, "acc_norm": 0.625230553768112, "acc_norm_stderr": 0.03315794745802012, "mc1": 0.2582619339045288, "mc1_stderr": 0.015321821688476185, "mc2": 0.5102578228172799, "mc2_stderr": 0.01648564659078862 }, "harness|arc:challenge|25": { "acc": 0.3924914675767918, "acc_stderr": 0.014269634635670717, "acc_norm": 0.46245733788395904, "acc_norm_stderr": 0.014570144495075583 }, "harness|hellaswag|10": { "acc": 0.3632742481577375, "acc_stderr": 0.004799599840397383, "acc_norm": 0.5606452897829117, "acc_norm_stderr": 0.004952942072999274 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.0373852067611967, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.0373852067611967 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6416184971098265, "acc_stderr": 0.036563436533531585, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.036563436533531585 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.04940635630605659, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.04940635630605659 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5617021276595745, "acc_stderr": 0.03243618636108102, "acc_norm": 0.5617021276595745, "acc_norm_stderr": 0.03243618636108102 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.046774730044911984, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.046774730044911984 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5103448275862069, "acc_stderr": 0.04165774775728762, "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728762 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7516129032258064, "acc_stderr": 0.024580028921481003, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481003 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.03287666758603491, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.03287666758603491 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217483, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217483 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768766, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768766 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6435897435897436, "acc_stderr": 0.024283140529467305, "acc_norm": 0.6435897435897436, "acc_norm_stderr": 0.024283140529467305 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6386554621848739, "acc_stderr": 0.031204691225150013, "acc_norm": 0.6386554621848739, "acc_norm_stderr": 0.031204691225150013 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8293577981651377, "acc_stderr": 0.016129271025099857, "acc_norm": 0.8293577981651377, "acc_norm_stderr": 0.016129271025099857 }, "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": 0.8431372549019608, "acc_stderr": 0.02552472232455334, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455334 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290902, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290902 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7040358744394619, "acc_stderr": 0.03063659134869981, "acc_norm": 0.7040358744394619, "acc_norm_stderr": 0.03063659134869981 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7786259541984732, "acc_stderr": 0.03641297081313729, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.03641297081313729 }, "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": 0.7852760736196319, "acc_stderr": 0.03226219377286774, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.03226219377286774 }, "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": 0.7961165048543689, "acc_stderr": 0.0398913985953177, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.0398913985953177 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.021901905115073325, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.021901905115073325 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8263090676883781, "acc_stderr": 0.01354741565866226, "acc_norm": 0.8263090676883781, "acc_norm_stderr": 0.01354741565866226 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7138728323699421, "acc_stderr": 0.02433214677913413, "acc_norm": 0.7138728323699421, "acc_norm_stderr": 0.02433214677913413 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3743016759776536, "acc_stderr": 0.016185444179457175, "acc_norm": 0.3743016759776536, "acc_norm_stderr": 0.016185444179457175 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6830065359477124, "acc_stderr": 0.026643278474508755, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.026643278474508755 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7037037037037037, "acc_stderr": 0.02540719779889016, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.02540719779889016 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48226950354609927, "acc_stderr": 0.02980873964223777, "acc_norm": 0.48226950354609927, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44589308996088656, "acc_stderr": 0.012695244711379778, "acc_norm": 0.44589308996088656, "acc_norm_stderr": 0.012695244711379778 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681397, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681397 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6683006535947712, "acc_stderr": 0.019047485239360378, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.019047485239360378 }, "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": 0.7224489795918367, "acc_stderr": 0.02866685779027465, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.02866685779027465 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623327, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623327 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.038786267710023595, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.038786267710023595 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness|truthfulqa:mc|0": { "mc1": 0.2582619339045288, "mc1_stderr": 0.015321821688476185, "mc2": 0.5102578228172799, "mc2_stderr": 0.01648564659078862 }, "harness|winogrande|5": { "acc": 0.7277032359905288, "acc_stderr": 0.012510697991453937 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

open-llm-leaderboard/details_codellama__CodeLlama-7b-hf

--- pretty_name: Evaluation run of codellama/CodeLlama-7b-hf dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_codellama__CodeLlama-7b-hf\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-14T19:46:33.225068](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-7b-hf/blob/main/results_2023-10-14T19-46-33.225068.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\": 0.0006291946308724832,\n\ \ \"em_stderr\": 0.00025680027497238217,\n \"f1\": 0.05123741610738289,\n\ \ \"f1_stderr\": 0.001242998424746743,\n \"acc\": 0.34582445982552373,\n\ \ \"acc_stderr\": 0.009790248772764803\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0006291946308724832,\n \"em_stderr\": 0.00025680027497238217,\n\ \ \"f1\": 0.05123741610738289,\n \"f1_stderr\": 0.001242998424746743\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05155420773313116,\n \ \ \"acc_stderr\": 0.006090887955262816\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6400947119179163,\n \"acc_stderr\": 0.01348960959026679\n\ \ }\n}\n```" repo_url: https://huggingface.co/codellama/CodeLlama-7b-hf leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|arc:challenge|25_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-26T04:20:17.128606.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_14T19_46_33.225068 path: - '**/details_harness|drop|3_2023-10-14T19-46-33.225068.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-14T19-46-33.225068.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_14T19_46_33.225068 path: - '**/details_harness|gsm8k|5_2023-10-14T19-46-33.225068.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-14T19-46-33.225068.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hellaswag|10_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-26T04:20:17.128606.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-management|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T04:20:17.128606.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_26T04_20_17.128606 path: - '**/details_harness|truthfulqa:mc|0_2023-08-26T04:20:17.128606.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-26T04:20:17.128606.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_14T19_46_33.225068 path: - '**/details_harness|winogrande|5_2023-10-14T19-46-33.225068.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-14T19-46-33.225068.parquet' - config_name: results data_files: - split: 2023_08_26T04_20_17.128606 path: - results_2023-08-26T04:20:17.128606.parquet - split: 2023_10_14T19_46_33.225068 path: - results_2023-10-14T19-46-33.225068.parquet - split: latest path: - results_2023-10-14T19-46-33.225068.parquet --- # Dataset Card for Evaluation run of codellama/CodeLlama-7b-hf ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/codellama/CodeLlama-7b-hf - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_codellama__CodeLlama-7b-hf", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-14T19:46:33.225068](https://huggingface.co/datasets/open-llm-leaderboard/details_codellama__CodeLlama-7b-hf/blob/main/results_2023-10-14T19-46-33.225068.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": 0.0006291946308724832, "em_stderr": 0.00025680027497238217, "f1": 0.05123741610738289, "f1_stderr": 0.001242998424746743, "acc": 0.34582445982552373, "acc_stderr": 0.009790248772764803 }, "harness|drop|3": { "em": 0.0006291946308724832, "em_stderr": 0.00025680027497238217, "f1": 0.05123741610738289, "f1_stderr": 0.001242998424746743 }, "harness|gsm8k|5": { "acc": 0.05155420773313116, "acc_stderr": 0.006090887955262816 }, "harness|winogrande|5": { "acc": 0.6400947119179163, "acc_stderr": 0.01348960959026679 } } ``` ### 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]

MilanHrab/Kosice_training

--- dataset_info: features: - name: name_of_record dtype: string - name: speech_array sequence: float64 - name: sampling_rate dtype: int64 - name: label dtype: string splits: - name: train num_bytes: 1178840561.6 num_examples: 4480 download_size: 894629427 dataset_size: 1178840561.6 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Kosice_training" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/yakumo_ran_touhou

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yakumo_ran/八雲藍/야쿠모란 (Touhou) This is the dataset of yakumo_ran/八雲藍/야쿠모란 (Touhou), containing 500 images and their tags. The core tags of this character are `blonde_hair, short_hair, fox_tail, tail, multiple_tails, yellow_eyes, hat, animal_ears, fox_ears, breasts, pillow_hat`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 614.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 392.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1158 | 786.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 569.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1158 | 1.02 GiB | [Download](https://huggingface.co/datasets/CyberHarem/yakumo_ran_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/yakumo_ran_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 | 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, closed_mouth, long_sleeves, solo, tabard, white_dress, wide_sleeves, bangs, looking_at_viewer, white_headwear, blush, frills, large_breasts, simple_background, white_background | | 1 | 16 | ![](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, bangs, long_sleeves, solo, tabard, white_dress, wide_sleeves, looking_at_viewer, white_headwear, frills, closed_mouth, simple_background, smile, hands_in_opposite_sleeves, hair_between_eyes, white_background, upper_body, blush | | 2 | 18 | ![](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, long_sleeves, solo, tabard, looking_at_viewer, wide_sleeves, hands_in_opposite_sleeves, smile, white_dress, large_breasts, white_background | | 3 | 18 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, hands_in_opposite_sleeves, solo, smile, wide_sleeves | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, looking_at_viewer, solo, blush, open_mouth, smile, tabard, large_breasts, no_headwear, upper_body, animal_ear_fluff | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, fox_mask, solo | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, closed_mouth, jeans, large_breasts, looking_at_viewer, simple_background, slit_pupils, solo, bangs, barefoot, blush, seiza, white_background, blue_pants, full_body, no_tail, short_sleeves, blue_shirt, long_sleeves, sweater | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | long_sleeves | solo | tabard | white_dress | wide_sleeves | bangs | looking_at_viewer | white_headwear | blush | frills | large_breasts | simple_background | white_background | smile | hands_in_opposite_sleeves | hair_between_eyes | upper_body | open_mouth | no_headwear | animal_ear_fluff | fox_mask | jeans | slit_pupils | barefoot | seiza | blue_pants | full_body | no_tail | short_sleeves | blue_shirt | sweater | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:---------------|:-------|:---------|:--------------|:---------------|:--------|:--------------------|:-----------------|:--------|:---------|:----------------|:--------------------|:-------------------|:--------|:----------------------------|:--------------------|:-------------|:-------------|:--------------|:-------------------|:-----------|:--------|:--------------|:-----------|:--------|:-------------|:------------|:----------|:----------------|:-------------|:----------| | 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 | | | | | | | | | | | | | | | | | | | | 1 | 16 | ![](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 | | | | | | | | | | | | | | | | 2 | 18 | ![](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 | | | | | | | | | | | | | | | | | | 3 | 18 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | X | | | X | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | X | X | | | | X | | X | | X | | | X | | | X | X | X | X | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | X | | | | X | X | | X | | X | X | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X |

sudy-super/piece-of-refined-oscar

--- license: apache-2.0 task_categories: - text-generation language: - ja size_categories: - 1M<n<10M --- # Descrption This dataset is part of the [OSCAR-2301](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301) cleaned. There are about 0.5b tokens counted by [calm2](https://huggingface.co/cyberagent/calm2-7b) tokenizer. # NOTE This dataset has not passed sentence end boundary determination or Perplexity Filtering, so there is room for improvement in quality.

nikchar/paper_test_set

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: label dtype: string - name: claim dtype: string - name: evidence_wiki_url dtype: string - name: text dtype: string splits: - name: train num_bytes: 15920562 num_examples: 11073 download_size: 6320618 dataset_size: 15920562 --- # Dataset Card for "paper_test_set" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_mnli_existential_you_have

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 183415 num_examples: 823 - name: dev_mismatched num_bytes: 167912 num_examples: 686 - name: test_matched num_bytes: 181716 num_examples: 817 - name: test_mismatched num_bytes: 154207 num_examples: 688 - name: train num_bytes: 7401659 num_examples: 32434 download_size: 4938545 dataset_size: 8088909 --- # Dataset Card for "MULTI_VALUE_mnli_existential_you_have" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Multimodal-Fatima/OxfordPets_Multimodal_Fatima_opt_175b_LLM_Description_opt175b_downstream_tasks_ViT_L_14

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: text dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: test num_bytes: 3482068.0 num_examples: 100 download_size: 3458504 dataset_size: 3482068.0 --- # Dataset Card for "OxfordPets_Multimodal_Fatima_opt_175b_LLM_Description_opt175b_downstream_tasks_ViT_L_14" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

result-kand2-sdxl-wuerst-karlo/b6ea8c05

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 176 num_examples: 10 download_size: 1328 dataset_size: 176 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "b6ea8c05" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

imageomics/KABR

--- license: cc0-1.0 task_categories: - video-classification tags: - zebra - giraffe - plains zebra - Grevy's zebra - video - animal behavior - behavior recognition - annotation - annotated video - conservation - drone - UAV - imbalanced - Kenya - Mpala Research Centre pretty_name: >- KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos size_categories: - 1M<n<10M --- # Dataset Card for KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos ## Dataset Description - **Homepage:** https://dirtmaxim.github.io/kabr/ - **Repository:** https://github.com/dirtmaxim/kabr-tools - **Paper:** https://openaccess.thecvf.com/content/WACV2024W/CV4Smalls/papers/Kholiavchenko_KABR_In-Situ_Dataset_for_Kenyan_Animal_Behavior_Recognition_From_Drone_WACVW_2024_paper.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary We present a novel high-quality dataset for animal behavior recognition from drone videos. The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors. The drone footage is captured at a resolution of 5472 x 3078 pixels, and the videos were recorded at a frame rate of 29.97 frames per second.  ### Supported Tasks and Leaderboards The results of our evaluation using I3D, SlowFast, and X3D architectures are given in the table below. For each one, the model was trained for 120 epochs with batch size of 5. For more information on these results, see our [paper](coming soon). | Method | All | Giraffes | Plains Zebras | Grevy’s Zebras | | ---- | ---- | ---- | ---- | ---- | | I3D (16x5) | 53.41 | 61.82 | 58.75 | 46.73 | | SlowFast (16x5, 4x5) | 52.92 | 61.15 | 60.60 | 47.42 | | X3D (16x5) | 61.9 | 65.1 | 63.11 | 51.16 | ### Languages English ## Dataset Structure Under `KABR/dataset/image/`, the data has been archived into `.zip` files, which are split into 2GB files. These must be recombined and extracted. After cloning and navigating into the repository, you can use the following commands to do the reconstruction: ```bash cd KABR/dataset/image/ cat giraffes_part_* > giraffes.zip md5sum giraffes.zip # Compare this to what's shown with `cat giraffes_md5.txt` unzip giraffes.zip rm -rf giraffes_part_* # Similarly for `zebras_grevys_part_*` and `zebras_plains_part_*` ``` Alternatively, there is a download script, `download.py`, which allows a download of the entire dataset in its established format without requiring one to clone the repository (cloning requires _at least_ double the size of the dataset to store). To proceed with this approach, download `download.py` to the system where you want to access the data. Then, in the same directory as the script, run the following to begin the download: ``` pip install requests python download.py ``` This script then downloads all the files present in the repository (without making a clone of the `.git` directory, etc.), concatenates the part files to their ZIP archives, verifies the MD5 checksums, extracts, and cleans up so that the folder structure, as described below, is present. Note that it will require approximately 116GB of free space to complete this process, though the final dataset will only take about 61GB of disk space (the script removes the extra files after checking the download was successful). The KABR dataset follows the Charades format: ``` KABR /dataset /image /video_1 /image_1.jpg /image_2.jpg ... /image_n.jpg /video_2 /image_1.jpg /image_2.jpg ... /image_n.jpg ... /video_n /image_1.jpg /image_2.jpg /image_3.jpg ... /image_n.jpg /annotation /classes.json /train.csv /val.csv ``` The dataset can be directly loaded and processed by the [SlowFast](https://github.com/facebookresearch/SlowFast) framework. **Informational Files** * `KABR/configs`: examples of SlowFast framework configs. * `KABR/annotation/distribution.xlsx`: distribution of classes for all videos. **Scripts:** * `image2video.py`: Encode image sequences into the original video. * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `video/G0067.mp4`. * `image2visual.py`: Encode image sequences into the original video with corresponding annotations. * For example, `[image/G0067.1, image/G0067.2, ..., image/G0067.24]` will be encoded into `visual/G0067.mp4`. ### Data Instances **Naming:** Within the image folder, the `video_n` folders are named as follows (X indicates a number): * G0XXX.X - Giraffes * ZP0XXX.X - Plains Zebras * ZG0XXX.X - Grevy's Zebras * Within each of these folders the images are simply `X.jpg`. **Note:** The dataset consists of a total of 1,139,893 frames captured from drone videos. There are 488,638 frames of Grevy's zebras, 492,507 frames of plains zebras, and 158,748 frames of giraffes. ### Data Fields There are 14,764 unique behavioral sequences in the dataset. These consist of eight distinct behaviors: - Walk - Trot - Run: animal is moving at a cantor or gallop - Graze: animal is eating grass or other vegetation - Browse: animal is eating trees or bushes - Head Up: animal is looking around or observe surroundings - Auto-Groom: animal is grooming itself (licking, scratching, or rubbing) - Occluded: animal is not fully visible ### Data Splits Training and validation sets are indicated by their respective CSV files (`train.csv` and `val.csv`), located within the `annotation` folder. ## Dataset Creation ### Curation Rationale We present a novel high-quality dataset for animal behavior recognition from drone videos. The dataset is focused on Kenyan wildlife and contains behaviors of giraffes, plains zebras, and Grevy's zebras. The dataset consists of more than 10 hours of annotated videos, and it includes eight different classes, encompassing seven types of animal behavior and an additional category for occluded instances. In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations. The dataset was collected using drones that flew over the animals in the [Mpala Research Centre](https://mpala.org/) in Kenya, providing high-quality video footage of the animal's natural behaviors. We believe that this dataset will be a valuable resource for researchers working on animal behavior recognition, as it provides a diverse and high-quality set of annotated videos that can be used for evaluating deep learning models. Additionally, the dataset can be used to study the behavior patterns of Kenyan animals and can help to inform conservation efforts and wildlife management strategies.  We provide a detailed description of the dataset and its annotation process, along with some initial experiments on the dataset using conventional deep learning models. The results demonstrate the effectiveness of the dataset for animal behavior recognition and highlight the potential for further research in this area. ### Source Data #### Initial Data Collection and Normalization Data was collected from 6 January 2023 through 21 January 2023 at the [Mpala Research Centre](https://mpala.org/) in Kenya under a Nacosti research license. We used DJI Mavic 2S drones equipped with cameras to record 5.4K resolution videos (5472 x 3078 pixels) from varying altitudes and distances of 10 to 50 meters from the animals (distance was determined by circumstances and safety regulations). Mini-scenes were extracted from these videos to reduce the impact of drone movement and facilitate human annotation. Animals were detected in frame using YOLOv8, then the SORT tracking algorithm was applied to follow their movement. A 400 by 300 pixel window, centered on the animal, was then extracted; this is the mini-scene.  ### Annotations #### Annotation process In the annotation process for this dataset, a team of 10 people was involved, with an expert zoologist overseeing the process. Each behavior was labeled based on its distinctive features, using a standardized set of criteria to ensure consistency and accuracy across the annotations.  ### Personal and Sensitive Information Though there are endangered species included in this data, exact locations are not provided and their safety is assured by their location within the preserve. ## Considerations for Using the Data  ### Other Known Limitations This data exhibits a long-tailed distribution due to the natural variation in frequency of the observed behaviors. ## Additional Information ### Authors * Maksim Kholiavchenko (Rensselaer Polytechnic Institute) - ORCID: 0000-0001-6757-1957 * Jenna Kline (The Ohio State University) * Michelle Ramirez (The Ohio State University) * Sam Stevens (The Ohio State University) * Alec Sheets (The Ohio State University) - ORCID: 0000-0002-3737-1484 * Reshma Ramesh Babu (The Ohio State University) - ORCID: 0000-0002-2517-5347 * Namrata Banerji (The Ohio State University) - ORCID: 0000-0001-6813-0010 * Elizabeth Campolongo (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0846-2413 * Matthew Thompson (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0583-8585 * Nina Van Tiel (Eidgenössische Technische Hochschule Zürich) - ORCID: 0000-0001-6393-5629 * Jackson Miliko (Mpala Research Centre) * Eduardo Bessa (Universidade de Brasília) - ORCID: 0000-0003-0606-5860 * Tanya Berger-Wolf (The Ohio State University) - ORCID: 0000-0001-7610-1412 * Daniel Rubenstein (Princeton University) - ORCID: 0000-0001-9049-5219 * Charles Stewart (Rensselaer Polytechnic Institute) ### Licensing Information This dataset is dedicated to the public domain for the benefit of scientific pursuits. We ask that you cite the dataset and journal paper using the below citations if you make use of it in your research. ### Citation Information #### Dataset ``` @misc{KABR_Data, author = {Kholiavchenko, Maksim and Kline, Jenna and Ramirez, Michelle and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and Miliko, Jackson and Bessa, Eduardo and Duporge, Isla and Berger-Wolf, Tanya and Rubenstein, Daniel and Stewart, Charles}, title = {KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos}, year = {2023}, url = {https://huggingface.co/datasets/imageomics/KABR}, doi = {10.57967/hf/1010}, publisher = {Hugging Face} } ``` #### Paper ``` @inproceedings{kholiavchenko2024kabr, title={KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos}, author={Kholiavchenko, Maksim and Kline, Jenna and Ramirez, Michelle and Stevens, Sam and Sheets, Alec and Babu, Reshma and Banerji, Namrata and Campolongo, Elizabeth and Thompson, Matthew and Van Tiel, Nina and Miliko, Jackson and Bessa, Eduardo and Duporge, Isla and Berger-Wolf, Tanya and Rubenstein, Daniel and Stewart, Charles}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={31-40}, year={2024} } ``` ### Contributions The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

reciprocate/tinygsm_mixtral_8M

--- dataset_info: features: - name: question dtype: string - name: program dtype: string - name: result dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 10716696014 num_examples: 8000000 download_size: 3197472673 dataset_size: 10716696014 configs: - config_name: default data_files: - split: train path: data/train-* ---

rainbow/Andy_Lau

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6985835.0 num_examples: 16 download_size: 6986820 dataset_size: 6985835.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Andy_Lau" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-moral_scenarios-dev

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string splits: - name: dev num_bytes: 4507 num_examples: 5 download_size: 9481 dataset_size: 4507 --- # Dataset Card for "mmlu-moral_scenarios-dev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Soma8622/kokkai_speech

--- license: mit --- # 概要 [データ取得元](https://kokkai.ndl.go.jp/api.html)

Lfu001/image-text

--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: negative_prompt dtype: string splits: - name: train num_bytes: 387510452.0 num_examples: 210 download_size: 387472246 dataset_size: 387510452.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

scene_parse_150

--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - en license: - bsd-3-clause multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|ade20k task_categories: - image-segmentation task_ids: - instance-segmentation paperswithcode_id: ade20k pretty_name: MIT Scene Parsing Benchmark tags: - scene-parsing dataset_info: - config_name: scene_parsing features: - name: image dtype: image - name: annotation dtype: image - name: scene_category dtype: class_label: names: '0': airport_terminal '1': art_gallery '2': badlands '3': ball_pit '4': bathroom '5': beach '6': bedroom '7': booth_indoor '8': botanical_garden '9': bridge '10': bullring '11': bus_interior '12': butte '13': canyon '14': casino_outdoor '15': castle '16': church_outdoor '17': closet '18': coast '19': conference_room '20': construction_site '21': corral '22': corridor '23': crosswalk '24': day_care_center '25': sand '26': elevator_interior '27': escalator_indoor '28': forest_road '29': gangplank '30': gas_station '31': golf_course '32': gymnasium_indoor '33': harbor '34': hayfield '35': heath '36': hoodoo '37': house '38': hunting_lodge_outdoor '39': ice_shelf '40': joss_house '41': kiosk_indoor '42': kitchen '43': landfill '44': library_indoor '45': lido_deck_outdoor '46': living_room '47': locker_room '48': market_outdoor '49': mountain_snowy '50': office '51': orchard '52': arbor '53': bookshelf '54': mews '55': nook '56': preserve '57': traffic_island '58': palace '59': palace_hall '60': pantry '61': patio '62': phone_booth '63': establishment '64': poolroom_home '65': quonset_hut_outdoor '66': rice_paddy '67': sandbox '68': shopfront '69': skyscraper '70': stone_circle '71': subway_interior '72': platform '73': supermarket '74': swimming_pool_outdoor '75': television_studio '76': indoor_procenium '77': train_railway '78': coral_reef '79': viaduct '80': wave '81': wind_farm '82': bottle_storage '83': abbey '84': access_road '85': air_base '86': airfield '87': airlock '88': airplane_cabin '89': airport '90': entrance '91': airport_ticket_counter '92': alcove '93': alley '94': amphitheater '95': amusement_arcade '96': amusement_park '97': anechoic_chamber '98': apartment_building_outdoor '99': apse_indoor '100': apse_outdoor '101': aquarium '102': aquatic_theater '103': aqueduct '104': arcade '105': arch '106': archaelogical_excavation '107': archive '108': basketball '109': football '110': hockey '111': performance '112': rodeo '113': soccer '114': armory '115': army_base '116': arrival_gate_indoor '117': arrival_gate_outdoor '118': art_school '119': art_studio '120': artists_loft '121': assembly_line '122': athletic_field_indoor '123': athletic_field_outdoor '124': atrium_home '125': atrium_public '126': attic '127': auditorium '128': auto_factory '129': auto_mechanics_indoor '130': auto_mechanics_outdoor '131': auto_racing_paddock '132': auto_showroom '133': backstage '134': backstairs '135': badminton_court_indoor '136': badminton_court_outdoor '137': baggage_claim '138': shop '139': exterior '140': balcony_interior '141': ballroom '142': bamboo_forest '143': bank_indoor '144': bank_outdoor '145': bank_vault '146': banquet_hall '147': baptistry_indoor '148': baptistry_outdoor '149': bar '150': barbershop '151': barn '152': barndoor '153': barnyard '154': barrack '155': baseball_field '156': basement '157': basilica '158': basketball_court_indoor '159': basketball_court_outdoor '160': bathhouse '161': batters_box '162': batting_cage_indoor '163': batting_cage_outdoor '164': battlement '165': bayou '166': bazaar_indoor '167': bazaar_outdoor '168': beach_house '169': beauty_salon '170': bedchamber '171': beer_garden '172': beer_hall '173': belfry '174': bell_foundry '175': berth '176': berth_deck '177': betting_shop '178': bicycle_racks '179': bindery '180': biology_laboratory '181': bistro_indoor '182': bistro_outdoor '183': bleachers_indoor '184': bleachers_outdoor '185': boardwalk '186': boat_deck '187': boathouse '188': bog '189': bomb_shelter_indoor '190': bookbindery '191': bookstore '192': bow_window_indoor '193': bow_window_outdoor '194': bowling_alley '195': box_seat '196': boxing_ring '197': breakroom '198': brewery_indoor '199': brewery_outdoor '200': brickyard_indoor '201': brickyard_outdoor '202': building_complex '203': building_facade '204': bullpen '205': burial_chamber '206': bus_depot_indoor '207': bus_depot_outdoor '208': bus_shelter '209': bus_station_indoor '210': bus_station_outdoor '211': butchers_shop '212': cabana '213': cabin_indoor '214': cabin_outdoor '215': cafeteria '216': call_center '217': campsite '218': campus '219': natural '220': urban '221': candy_store '222': canteen '223': car_dealership '224': backseat '225': frontseat '226': caravansary '227': cardroom '228': cargo_container_interior '229': airplane '230': boat '231': freestanding '232': carport_indoor '233': carport_outdoor '234': carrousel '235': casino_indoor '236': catacomb '237': cathedral_indoor '238': cathedral_outdoor '239': catwalk '240': cavern_indoor '241': cavern_outdoor '242': cemetery '243': chalet '244': chaparral '245': chapel '246': checkout_counter '247': cheese_factory '248': chemical_plant '249': chemistry_lab '250': chicken_coop_indoor '251': chicken_coop_outdoor '252': chicken_farm_indoor '253': chicken_farm_outdoor '254': childs_room '255': choir_loft_interior '256': church_indoor '257': circus_tent_indoor '258': circus_tent_outdoor '259': city '260': classroom '261': clean_room '262': cliff '263': booth '264': room '265': clock_tower_indoor '266': cloister_indoor '267': cloister_outdoor '268': clothing_store '269': coast_road '270': cockpit '271': coffee_shop '272': computer_room '273': conference_center '274': conference_hall '275': confessional '276': control_room '277': control_tower_indoor '278': control_tower_outdoor '279': convenience_store_indoor '280': convenience_store_outdoor '281': corn_field '282': cottage '283': cottage_garden '284': courthouse '285': courtroom '286': courtyard '287': covered_bridge_interior '288': crawl_space '289': creek '290': crevasse '291': library '292': cybercafe '293': dacha '294': dairy_indoor '295': dairy_outdoor '296': dam '297': dance_school '298': darkroom '299': delicatessen '300': dentists_office '301': department_store '302': departure_lounge '303': vegetation '304': desert_road '305': diner_indoor '306': diner_outdoor '307': dinette_home '308': vehicle '309': dining_car '310': dining_hall '311': dining_room '312': dirt_track '313': discotheque '314': distillery '315': ditch '316': dock '317': dolmen '318': donjon '319': doorway_indoor '320': doorway_outdoor '321': dorm_room '322': downtown '323': drainage_ditch '324': dress_shop '325': dressing_room '326': drill_rig '327': driveway '328': driving_range_indoor '329': driving_range_outdoor '330': drugstore '331': dry_dock '332': dugout '333': earth_fissure '334': editing_room '335': electrical_substation '336': elevated_catwalk '337': door '338': freight_elevator '339': elevator_lobby '340': elevator_shaft '341': embankment '342': embassy '343': engine_room '344': entrance_hall '345': escalator_outdoor '346': escarpment '347': estuary '348': excavation '349': exhibition_hall '350': fabric_store '351': factory_indoor '352': factory_outdoor '353': fairway '354': farm '355': fastfood_restaurant '356': fence '357': cargo_deck '358': ferryboat_indoor '359': passenger_deck '360': cultivated '361': wild '362': field_road '363': fire_escape '364': fire_station '365': firing_range_indoor '366': firing_range_outdoor '367': fish_farm '368': fishmarket '369': fishpond '370': fitting_room_interior '371': fjord '372': flea_market_indoor '373': flea_market_outdoor '374': floating_dry_dock '375': flood '376': florist_shop_indoor '377': florist_shop_outdoor '378': fly_bridge '379': food_court '380': football_field '381': broadleaf '382': needleleaf '383': forest_fire '384': forest_path '385': formal_garden '386': fort '387': fortress '388': foundry_indoor '389': foundry_outdoor '390': fountain '391': freeway '392': funeral_chapel '393': funeral_home '394': furnace_room '395': galley '396': game_room '397': garage_indoor '398': garage_outdoor '399': garbage_dump '400': gasworks '401': gate '402': gatehouse '403': gazebo_interior '404': general_store_indoor '405': general_store_outdoor '406': geodesic_dome_indoor '407': geodesic_dome_outdoor '408': ghost_town '409': gift_shop '410': glacier '411': glade '412': gorge '413': granary '414': great_hall '415': greengrocery '416': greenhouse_indoor '417': greenhouse_outdoor '418': grotto '419': guardhouse '420': gulch '421': gun_deck_indoor '422': gun_deck_outdoor '423': gun_store '424': hacienda '425': hallway '426': handball_court '427': hangar_indoor '428': hangar_outdoor '429': hardware_store '430': hat_shop '431': hatchery '432': hayloft '433': hearth '434': hedge_maze '435': hedgerow '436': heliport '437': herb_garden '438': highway '439': hill '440': home_office '441': home_theater '442': hospital '443': hospital_room '444': hot_spring '445': hot_tub_indoor '446': hot_tub_outdoor '447': hotel_outdoor '448': hotel_breakfast_area '449': hotel_room '450': hunting_lodge_indoor '451': hut '452': ice_cream_parlor '453': ice_floe '454': ice_skating_rink_indoor '455': ice_skating_rink_outdoor '456': iceberg '457': igloo '458': imaret '459': incinerator_indoor '460': incinerator_outdoor '461': industrial_area '462': industrial_park '463': inn_indoor '464': inn_outdoor '465': irrigation_ditch '466': islet '467': jacuzzi_indoor '468': jacuzzi_outdoor '469': jail_indoor '470': jail_outdoor '471': jail_cell '472': japanese_garden '473': jetty '474': jewelry_shop '475': junk_pile '476': junkyard '477': jury_box '478': kasbah '479': kennel_indoor '480': kennel_outdoor '481': kindergarden_classroom '482': kiosk_outdoor '483': kitchenette '484': lab_classroom '485': labyrinth_indoor '486': labyrinth_outdoor '487': lagoon '488': artificial '489': landing '490': landing_deck '491': laundromat '492': lava_flow '493': lavatory '494': lawn '495': lean-to '496': lecture_room '497': legislative_chamber '498': levee '499': library_outdoor '500': lido_deck_indoor '501': lift_bridge '502': lighthouse '503': limousine_interior '504': liquor_store_indoor '505': liquor_store_outdoor '506': loading_dock '507': lobby '508': lock_chamber '509': loft '510': lookout_station_indoor '511': lookout_station_outdoor '512': lumberyard_indoor '513': lumberyard_outdoor '514': machine_shop '515': manhole '516': mansion '517': manufactured_home '518': market_indoor '519': marsh '520': martial_arts_gym '521': mastaba '522': maternity_ward '523': mausoleum '524': medina '525': menhir '526': mesa '527': mess_hall '528': mezzanine '529': military_hospital '530': military_hut '531': military_tent '532': mine '533': mineshaft '534': mini_golf_course_indoor '535': mini_golf_course_outdoor '536': mission '537': dry '538': water '539': mobile_home '540': monastery_indoor '541': monastery_outdoor '542': moon_bounce '543': moor '544': morgue '545': mosque_indoor '546': mosque_outdoor '547': motel '548': mountain '549': mountain_path '550': mountain_road '551': movie_theater_indoor '552': movie_theater_outdoor '553': mudflat '554': museum_indoor '555': museum_outdoor '556': music_store '557': music_studio '558': misc '559': natural_history_museum '560': naval_base '561': newsroom '562': newsstand_indoor '563': newsstand_outdoor '564': nightclub '565': nuclear_power_plant_indoor '566': nuclear_power_plant_outdoor '567': nunnery '568': nursery '569': nursing_home '570': oasis '571': oast_house '572': observatory_indoor '573': observatory_outdoor '574': observatory_post '575': ocean '576': office_building '577': office_cubicles '578': oil_refinery_indoor '579': oil_refinery_outdoor '580': oilrig '581': operating_room '582': optician '583': organ_loft_interior '584': orlop_deck '585': ossuary '586': outcropping '587': outhouse_indoor '588': outhouse_outdoor '589': overpass '590': oyster_bar '591': oyster_farm '592': acropolis '593': aircraft_carrier_object '594': amphitheater_indoor '595': archipelago '596': questionable '597': assembly_hall '598': assembly_plant '599': awning_deck '600': back_porch '601': backdrop '602': backroom '603': backstage_outdoor '604': backstairs_indoor '605': backwoods '606': ballet '607': balustrade '608': barbeque '609': basin_outdoor '610': bath_indoor '611': bath_outdoor '612': bathhouse_outdoor '613': battlefield '614': bay '615': booth_outdoor '616': bottomland '617': breakfast_table '618': bric-a-brac '619': brooklet '620': bubble_chamber '621': buffet '622': bulkhead '623': bunk_bed '624': bypass '625': byroad '626': cabin_cruiser '627': cargo_helicopter '628': cellar '629': chair_lift '630': cocktail_lounge '631': corner '632': country_house '633': country_road '634': customhouse '635': dance_floor '636': deck-house_boat_deck_house '637': deck-house_deck_house '638': dining_area '639': diving_board '640': embrasure '641': entranceway_indoor '642': entranceway_outdoor '643': entryway_outdoor '644': estaminet '645': farm_building '646': farmhouse '647': feed_bunk '648': field_house '649': field_tent_indoor '650': field_tent_outdoor '651': fire_trench '652': fireplace '653': flashflood '654': flatlet '655': floating_dock '656': flood_plain '657': flowerbed '658': flume_indoor '659': flying_buttress '660': foothill '661': forecourt '662': foreshore '663': front_porch '664': garden '665': gas_well '666': glen '667': grape_arbor '668': grove '669': guardroom '670': guesthouse '671': gymnasium_outdoor '672': head_shop '673': hen_yard '674': hillock '675': housing_estate '676': housing_project '677': howdah '678': inlet '679': insane_asylum '680': outside '681': juke_joint '682': jungle '683': kraal '684': laboratorywet '685': landing_strip '686': layby '687': lean-to_tent '688': loge '689': loggia_outdoor '690': lower_deck '691': luggage_van '692': mansard '693': meadow '694': meat_house '695': megalith '696': mens_store_outdoor '697': mental_institution_indoor '698': mental_institution_outdoor '699': military_headquarters '700': millpond '701': millrace '702': natural_spring '703': nursing_home_outdoor '704': observation_station '705': open-hearth_furnace '706': operating_table '707': outbuilding '708': palestra '709': parkway '710': patio_indoor '711': pavement '712': pawnshop_outdoor '713': pinetum '714': piste_road '715': pizzeria_outdoor '716': powder_room '717': pumping_station '718': reception_room '719': rest_stop '720': retaining_wall '721': rift_valley '722': road '723': rock_garden '724': rotisserie '725': safari_park '726': salon '727': saloon '728': sanatorium '729': science_laboratory '730': scrubland '731': scullery '732': seaside '733': semidesert '734': shelter '735': shelter_deck '736': shelter_tent '737': shore '738': shrubbery '739': sidewalk '740': snack_bar '741': snowbank '742': stage_set '743': stall '744': stateroom '745': store '746': streetcar_track '747': student_center '748': study_hall '749': sugar_refinery '750': sunroom '751': supply_chamber '752': t-bar_lift '753': tannery '754': teahouse '755': threshing_floor '756': ticket_window_indoor '757': tidal_basin '758': tidal_river '759': tiltyard '760': tollgate '761': tomb '762': tract_housing '763': trellis '764': truck_stop '765': upper_balcony '766': vestibule '767': vinery '768': walkway '769': war_room '770': washroom '771': water_fountain '772': water_gate '773': waterscape '774': waterway '775': wetland '776': widows_walk_indoor '777': windstorm '778': packaging_plant '779': pagoda '780': paper_mill '781': park '782': parking_garage_indoor '783': parking_garage_outdoor '784': parking_lot '785': parlor '786': particle_accelerator '787': party_tent_indoor '788': party_tent_outdoor '789': pasture '790': pavilion '791': pawnshop '792': pedestrian_overpass_indoor '793': penalty_box '794': pet_shop '795': pharmacy '796': physics_laboratory '797': piano_store '798': picnic_area '799': pier '800': pig_farm '801': pilothouse_indoor '802': pilothouse_outdoor '803': pitchers_mound '804': pizzeria '805': planetarium_indoor '806': planetarium_outdoor '807': plantation_house '808': playground '809': playroom '810': plaza '811': podium_indoor '812': podium_outdoor '813': police_station '814': pond '815': pontoon_bridge '816': poop_deck '817': porch '818': portico '819': portrait_studio '820': postern '821': power_plant_outdoor '822': print_shop '823': priory '824': promenade '825': promenade_deck '826': pub_indoor '827': pub_outdoor '828': pulpit '829': putting_green '830': quadrangle '831': quicksand '832': quonset_hut_indoor '833': racecourse '834': raceway '835': raft '836': railroad_track '837': railway_yard '838': rainforest '839': ramp '840': ranch '841': ranch_house '842': reading_room '843': reception '844': recreation_room '845': rectory '846': recycling_plant_indoor '847': refectory '848': repair_shop '849': residential_neighborhood '850': resort '851': rest_area '852': restaurant '853': restaurant_kitchen '854': restaurant_patio '855': restroom_indoor '856': restroom_outdoor '857': revolving_door '858': riding_arena '859': river '860': road_cut '861': rock_arch '862': roller_skating_rink_indoor '863': roller_skating_rink_outdoor '864': rolling_mill '865': roof '866': roof_garden '867': root_cellar '868': rope_bridge '869': roundabout '870': roundhouse '871': rubble '872': ruin '873': runway '874': sacristy '875': salt_plain '876': sand_trap '877': sandbar '878': sauna '879': savanna '880': sawmill '881': schoolhouse '882': schoolyard '883': science_museum '884': scriptorium '885': sea_cliff '886': seawall '887': security_check_point '888': server_room '889': sewer '890': sewing_room '891': shed '892': shipping_room '893': shipyard_outdoor '894': shoe_shop '895': shopping_mall_indoor '896': shopping_mall_outdoor '897': shower '898': shower_room '899': shrine '900': signal_box '901': sinkhole '902': ski_jump '903': ski_lodge '904': ski_resort '905': ski_slope '906': sky '907': skywalk_indoor '908': skywalk_outdoor '909': slum '910': snowfield '911': massage_room '912': mineral_bath '913': spillway '914': sporting_goods_store '915': squash_court '916': stable '917': baseball '918': stadium_outdoor '919': stage_indoor '920': stage_outdoor '921': staircase '922': starting_gate '923': steam_plant_outdoor '924': steel_mill_indoor '925': storage_room '926': storm_cellar '927': street '928': strip_mall '929': strip_mine '930': student_residence '931': submarine_interior '932': sun_deck '933': sushi_bar '934': swamp '935': swimming_hole '936': swimming_pool_indoor '937': synagogue_indoor '938': synagogue_outdoor '939': taxistand '940': taxiway '941': tea_garden '942': tearoom '943': teashop '944': television_room '945': east_asia '946': mesoamerican '947': south_asia '948': western '949': tennis_court_indoor '950': tennis_court_outdoor '951': tent_outdoor '952': terrace_farm '953': indoor_round '954': indoor_seats '955': theater_outdoor '956': thriftshop '957': throne_room '958': ticket_booth '959': tobacco_shop_indoor '960': toll_plaza '961': tollbooth '962': topiary_garden '963': tower '964': town_house '965': toyshop '966': track_outdoor '967': trading_floor '968': trailer_park '969': train_interior '970': train_station_outdoor '971': station '972': tree_farm '973': tree_house '974': trench '975': trestle_bridge '976': tundra '977': rail_indoor '978': rail_outdoor '979': road_indoor '980': road_outdoor '981': turkish_bath '982': ocean_deep '983': ocean_shallow '984': utility_room '985': valley '986': van_interior '987': vegetable_garden '988': velodrome_indoor '989': velodrome_outdoor '990': ventilation_shaft '991': veranda '992': vestry '993': veterinarians_office '994': videostore '995': village '996': vineyard '997': volcano '998': volleyball_court_indoor '999': volleyball_court_outdoor '1000': voting_booth '1001': waiting_room '1002': walk_in_freezer '1003': warehouse_indoor '1004': warehouse_outdoor '1005': washhouse_indoor '1006': washhouse_outdoor '1007': watchtower '1008': water_mill '1009': water_park '1010': water_tower '1011': water_treatment_plant_indoor '1012': water_treatment_plant_outdoor '1013': block '1014': cascade '1015': cataract '1016': fan '1017': plunge '1018': watering_hole '1019': weighbridge '1020': wet_bar '1021': wharf '1022': wheat_field '1023': whispering_gallery '1024': widows_walk_interior '1025': windmill '1026': window_seat '1027': barrel_storage '1028': winery '1029': witness_stand '1030': woodland '1031': workroom '1032': workshop '1033': wrestling_ring_indoor '1034': wrestling_ring_outdoor '1035': yard '1036': youth_hostel '1037': zen_garden '1038': ziggurat '1039': zoo '1040': forklift '1041': hollow '1042': hutment '1043': pueblo '1044': vat '1045': perfume_shop '1046': steel_mill_outdoor '1047': orchestra_pit '1048': bridle_path '1049': lyceum '1050': one-way_street '1051': parade_ground '1052': pump_room '1053': recycling_plant_outdoor '1054': chuck_wagon splits: - name: train num_bytes: 8468086 num_examples: 20210 - name: test num_bytes: 744607 num_examples: 3352 - name: validation num_bytes: 838032 num_examples: 2000 download_size: 1179202534 dataset_size: 10050725 - config_name: instance_segmentation features: - name: image dtype: image - name: annotation dtype: image splits: - name: train num_bytes: 862611544 num_examples: 20210 - name: test num_bytes: 212493928 num_examples: 3352 - name: validation num_bytes: 87502294 num_examples: 2000 download_size: 1197393920 dataset_size: 1162607766 --- # Dataset Card for MIT Scene Parsing Benchmark ## 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:** [MIT Scene Parsing Benchmark homepage](http://sceneparsing.csail.mit.edu/) - **Repository:** [Scene Parsing repository (Caffe/Torch7)](https://github.com/CSAILVision/sceneparsing),[Scene Parsing repository (PyTorch)](https://github.com/CSAILVision/semantic-segmentation-pytorch) and [Instance Segmentation repository](https://github.com/CSAILVision/placeschallenge/tree/master/instancesegmentation) - **Paper:** [Scene Parsing through ADE20K Dataset](http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf) and [Semantic Understanding of Scenes through ADE20K Dataset](https://arxiv.org/abs/1608.05442) - **Leaderboard:** [MIT Scene Parsing Benchmark leaderboard](http://sceneparsing.csail.mit.edu/#:~:text=twice%20per%20week.-,leaderboard,-Organizers) - **Point of Contact:** [Bolei Zhou](mailto:bzhou@ie.cuhk.edu.hk) ### Dataset Summary Scene parsing is the task of segmenting and parsing an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing. The data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts. Specifically, the benchmark is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing. There are in total 150 semantic categories included for evaluation, which include e.g. sky, road, grass, and discrete objects like person, car, bed. Note that there are non-uniform distribution of objects occuring in the images, mimicking a more natural object occurrence in daily scene. The goal of this benchmark is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bedThis benchamark is similar to semantic segmentation tasks in COCO and Pascal Dataset, but the data is more scene-centric and with a diverse range of object categories. The data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts. ### Supported Tasks and Leaderboards - `scene-parsing`: The goal of this task is to segment the whole image densely into semantic classes (image regions), where each pixel is assigned a class label such as the region of *tree* and the region of *building*. [The leaderboard](http://sceneparsing.csail.mit.edu/#:~:text=twice%20per%20week.-,leaderboard,-Organizers) for this task ranks the models by considering the mean of the pixel-wise accuracy and class-wise IoU as the final score. Pixel-wise accuracy indicates the ratio of pixels which are correctly predicted, while class-wise IoU indicates the Intersection of Union of pixels averaged over all the 150 semantic categories. Refer to the [Development Kit](https://github.com/CSAILVision/sceneparsing) for the detail. - `instance-segmentation`: The goal of this task is to detect the object instances inside an image and further generate the precise segmentation masks of the objects. Its difference compared to the task of scene parsing is that in scene parsing there is no instance concept for the segmented regions, instead in instance segmentation if there are three persons in the scene, the network is required to segment each one of the person regions. This task doesn't have an active leaderboard. The performance of the instance segmentation algorithms is evaluated by Average Precision (AP, or mAP), following COCO evaluation metrics. For each image, at most 255 top-scoring instance masks are taken across all categories. Each instance mask prediction is only considered if its IoU with ground truth is above a certain threshold. There are 10 IoU thresholds of 0.50:0.05:0.95 for evaluation. The final AP is averaged across 10 IoU thresholds and 100 categories. You can refer to COCO evaluation page for more explanation: http://mscoco.org/dataset/#detections-eval ### Languages English. ## Dataset Structure ### Data Instances A data point comprises an image and its annotation mask, which is `None` in the testing set. The `scene_parsing` configuration has an additional `scene_category` field. #### `scene_parsing` ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=683x512 at 0x1FF32A3EDA0>, 'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=683x512 at 0x1FF32E5B978>, 'scene_category': 0 } ``` #### `instance_segmentation` ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256 at 0x20B51B5C400>, 'annotation': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256 at 0x20B57051B38> } ``` ### Data Fields #### `scene_parsing` - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `annotation`: A `PIL.Image.Image` object containing the annotation mask. - `scene_category`: A scene category for the image (e.g. `airport_terminal`, `canyon`, `mobile_home`). > **Note**: annotation masks contain labels ranging from 0 to 150, where 0 refers to "other objects". Those pixels are not considered in the official evaluation. Refer to [this file](https://github.com/CSAILVision/sceneparsing/blob/master/objectInfo150.csv) for the information about the labels of the 150 semantic categories, including indices, pixel ratios and names. #### `instance_segmentation` - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `annotation`: A `PIL.Image.Image` object containing the annotation mask. > **Note**: in the instance annotation masks, the R(ed) channel encodes category ID, and the G(reen) channel encodes instance ID. Each object instance has a unique instance ID regardless of its category ID. In the dataset, all images have <256 object instances. Refer to [this file (train split)](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/instanceInfo100_train.txt) and to [this file (validation split)](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/instanceInfo100_val.txt) for the information about the labels of the 100 semantic categories. To find the mapping between the semantic categories for `instance_segmentation` and `scene_parsing`, refer to [this file](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/categoryMapping.txt). ### Data Splits The data is split into training, test and validation set. The training data contains 20210 images, the testing data contains 3352 images and the validation data contains 2000 images. ## Dataset Creation ### Curation Rationale The rationale from the paper for the ADE20K dataset from which this benchmark originates: > Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. > The motivation of this work is to collect a dataset that has densely annotated images (every pixel has a semantic label) with a large and an unrestricted open vocabulary. The images in our dataset are manually segmented in great detail, covering a diverse set of scenes, object and object part categories. The challenge for collecting such annotations is finding reliable annotators, as well as the fact that labeling is difficult if the class list is not defined in advance. On the other hand, open vocabulary naming also suffers from naming inconsistencies across different annotators. In contrast, our dataset was annotated by a single expert annotator, providing extremely detailed and exhaustive image annotations. On average, our annotator labeled 29 annotation segments per image, compared to the 16 segments per image labeled by external annotators (like workers from Amazon Mechanical Turk). Furthermore, the data consistency and quality are much higher than that of external annotators. ### Source Data #### Initial Data Collection and Normalization Images come from the LabelMe, SUN datasets, and Places and were selected to cover the 900 scene categories defined in the SUN database. This benchmark was built by selecting the top 150 objects ranked by their total pixel ratios from the ADE20K dataset. As the original images in the ADE20K dataset have various sizes, for simplicity those large-sized images were rescaled to make their minimum heights or widths as 512. Among the 150 objects, there are 35 stuff classes (i.e., wall, sky, road) and 115 discrete objects (i.e., car, person, table). The annotated pixels of the 150 objects occupy 92.75% of all the pixels in the dataset, where the stuff classes occupy 60.92%, and discrete objects occupy 31.83%. #### Who are the source language producers? The same as in the LabelMe, SUN datasets, and Places datasets. ### Annotations #### Annotation process Annotation process for the ADE20K dataset: > **Image Annotation.** For our dataset, we are interested in having a diverse set of scenes with dense annotations of all the objects present. Images come from the LabelMe, SUN datasets, and Places and were selected to cover the 900 scene categories defined in the SUN database. Images were annotated by a single expert worker using the LabelMe interface. Fig. 2 shows a snapshot of the annotation interface and one fully segmented image. The worker provided three types of annotations: object segments with names, object parts, and attributes. All object instances are segmented independently so that the dataset could be used to train and evaluate detection or segmentation algorithms. Datasets such as COCO, Pascal or Cityscape start by defining a set of object categories of interest. However, when labeling all the objects in a scene, working with a predefined list of objects is not possible as new categories appear frequently (see fig. 5.d). Here, the annotator created a dictionary of visual concepts where new classes were added constantly to ensure consistency in object naming. Object parts are associated with object instances. Note that parts can have parts too, and we label these associations as well. For example, the ‘rim’ is a part of a ‘wheel’, which in turn is part of a ‘car’. A ‘knob’ is a part of a ‘door’ that can be part of a ‘cabinet’. The total part hierarchy has a depth of 3. The object and part hierarchy is in the supplementary materials. > **Annotation Consistency.** Defining a labeling protocol is relatively easy when the labeling task is restricted to a fixed list of object classes, however it becomes challenging when the class list is openended. As the goal is to label all the objects within each image, the list of classes grows unbounded. >Many object classes appear only a few times across the entire collection of images. However, those rare >object classes cannot be ignored as they might be important elements for the interpretation of the scene. >Labeling in these conditions becomes difficult because we need to keep a growing list of all the object >classes in order to have a consistent naming across the entire dataset. Despite the annotator’s best effort, >the process is not free of noise. To analyze the annotation consistency we took a subset of 61 randomly >chosen images from the validation set, then asked our annotator to annotate them again (there is a time difference of six months). One expects that there are some differences between the two annotations. A few examples are shown in Fig 3. On average, 82.4% of the pixels got the same label. The remaining 17.6% of pixels had some errors for which we grouped into three error types as follows: > > • Segmentation quality: Variations in the quality of segmentation and outlining of the object boundary. One typical source of error arises when segmenting complex objects such as buildings and trees, which can be segmented with different degrees of precision. 5.7% of the pixels had this type of error. > > • Object naming: Differences in object naming (due to ambiguity or similarity between concepts, for instance calling a big car a ‘car’ in one segmentation and a ‘truck’ in the another one, or a ‘palm tree’ a‘tree’. 6.0% of the pixels had naming issues. These errors can be reduced by defining a very precise terminology, but this becomes much harder with a large growing vocabulary. > > • Segmentation quantity: Missing objects in one of the two segmentations. There is a very large number of objects in each image and some images might be annotated more thoroughly than others. For example, in the third column of Fig 3 the annotator missed some small objects in different annotations. 5.9% of the pixels are due to missing labels. A similar issue existed in segmentation datasets such as the Berkeley Image segmentation dataset. > > The median error values for the three error types are: 4.8%, 0.3% and 2.6% showing that the mean value is dominated by a few images, and that the most common type of error is segmentation quality. To further compare the annotation done by our single expert annotator and the AMT-like annotators, 20 images from the validation set are annotated by two invited external annotators, both with prior experience in image labeling. The first external annotator had 58.5% of inconsistent pixels compared to the segmentation provided by our annotator, and the second external annotator had 75% of the inconsistent pixels. Many of these inconsistencies are due to the poor quality of the segmentations provided by external annotators (as it has been observed with AMT which requires multiple verification steps for quality control). For the best external annotator (the first one), 7.9% of pixels have inconsistent segmentations (just slightly worse than our annotator), 14.9% have inconsistent object naming and 35.8% of the pixels correspond to missing objects, which is due to the much smaller number of objects annotated by the external annotator in comparison with the ones annotated by our expert annotator. The external annotators labeled on average 16 segments per image while our annotator provided 29 segments per image. #### Who are the annotators? Three expert annotators and the AMT-like annotators. ### 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 Refer to the `Annotation Consistency` subsection of `Annotation Process`. ## Additional Information ### Dataset Curators Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso and Antonio Torralba. ### Licensing Information The MIT Scene Parsing Benchmark dataset is licensed under a [BSD 3-Clause License](https://github.com/CSAILVision/sceneparsing/blob/master/LICENSE). ### Citation Information ```bibtex @inproceedings{zhou2017scene, title={Scene Parsing through ADE20K Dataset}, author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2017} } @article{zhou2016semantic, title={Semantic understanding of scenes through the ade20k dataset}, author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio}, journal={arXiv preprint arXiv:1608.05442}, year={2016} } ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

KentoTsu/Bets

--- license: openrail ---

Christoph911/German-legal-SQuAD

--- license: mit ---

EleutherAI/quirky_addition_raw

--- dataset_info: features: - name: id dtype: string - name: template_args struct: - name: character dtype: string - name: op1 dtype: int64 - name: op2 dtype: int64 - name: result dtype: int64 - name: character dtype: string - name: label dtype: bool - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: difficulty_quantile dtype: float64 splits: - name: train num_bytes: 26256000 num_examples: 384000 - name: validation num_bytes: 547000 num_examples: 8000 - name: test num_bytes: 547000 num_examples: 8000 download_size: 13465330 dataset_size: 27350000 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

AdapterOcean/code_instructions_standardized_cluster_10_std

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 8041456 num_examples: 11514 download_size: 3654425 dataset_size: 8041456 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_10_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Cheetor1996/Natsu_Hyuga

--- license: cc-by-2.0 language: - en tags: - art --- **Natsu Hyuga** from **Kansen 5** - *Trained with Anime (full-final-pruned) model* - *Works best with ALL, MIDD, OUTD, and OUTALL LoRA weight blocks, and with 0.4-0.9 weights.*

Intuit-GenSRF/es_mental_health_counseling

--- dataset_info: features: - name: Context dtype: string - name: Response dtype: string - name: split dtype: string - name: text dtype: string - name: text_spanish dtype: string splits: - name: train num_bytes: 13763461 num_examples: 3512 download_size: 7425319 dataset_size: 13763461 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "es_mental_health_counseling" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_ConvexAI__BurningBruce-005

--- pretty_name: Evaluation run of ConvexAI/BurningBruce-005 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ConvexAI/BurningBruce-005](https://huggingface.co/ConvexAI/BurningBruce-005)\ \ 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-005\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-02T18:58:34.137305](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__BurningBruce-005/blob/main/results_2024-02-02T18-58-34.137305.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\": 0.6536170901380703,\n\ \ \"acc_stderr\": 0.032028038336707275,\n \"acc_norm\": 0.6528681277337212,\n\ \ \"acc_norm_stderr\": 0.032697450933548394,\n \"mc1\": 0.543451652386781,\n\ \ \"mc1_stderr\": 0.017437280953183688,\n \"mc2\": 0.6726501530582988,\n\ \ \"mc2_stderr\": 0.015249067039770463\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6945392491467577,\n \"acc_stderr\": 0.013460080478002507,\n\ \ \"acc_norm\": 0.7201365187713311,\n \"acc_norm_stderr\": 0.013119040897725922\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7117108145787692,\n\ \ \"acc_stderr\": 0.004520406331084042,\n \"acc_norm\": 0.8830910177255527,\n\ \ \"acc_norm_stderr\": 0.003206551283257396\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n\ \ \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \ \ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7283018867924528,\n \"acc_stderr\": 0.027377706624670713,\n\ \ \"acc_norm\": 0.7283018867924528,\n \"acc_norm_stderr\": 0.027377706624670713\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.0358687928008034,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.0358687928008034\n },\n \"harness|hendrycksTest-college_chemistry|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_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\": 0.29,\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\": 0.6878612716763006,\n\ \ \"acc_stderr\": 0.035331333893236574,\n \"acc_norm\": 0.6878612716763006,\n\ \ \"acc_norm_stderr\": 0.035331333893236574\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\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\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\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\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n\ \ \"acc_stderr\": 0.023157879349083522,\n \"acc_norm\": 0.7903225806451613,\n\ \ \"acc_norm_stderr\": 0.023157879349083522\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652456,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652456\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886786,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886786\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.034076320938540516,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.034076320938540516\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\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\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43798882681564244,\n\ \ \"acc_stderr\": 0.016593394227564843,\n \"acc_norm\": 0.43798882681564244,\n\ \ \"acc_norm_stderr\": 0.016593394227564843\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.025670259242188936,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.025670259242188936\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\"\ : 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"\ acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4680573663624511,\n\ \ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\ \ \"acc_norm_stderr\": 0.012744149704869649\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.02858270975389845,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.02858270975389845\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6666666666666666,\n \"acc_stderr\": 0.019070985589687495,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.019070985589687495\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\": 0.028123429335142773,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169146,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169146\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.543451652386781,\n\ \ \"mc1_stderr\": 0.017437280953183688,\n \"mc2\": 0.6726501530582988,\n\ \ \"mc2_stderr\": 0.015249067039770463\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.010470796496781096\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7149355572403336,\n \ \ \"acc_stderr\": 0.012435042334904004\n }\n}\n```" repo_url: https://huggingface.co/ConvexAI/BurningBruce-005 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_58_34.137305 path: - '**/details_harness|arc:challenge|25_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-02T18-58-34.137305.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|gsm8k|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hellaswag|10_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-02T18-58-34.137305.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-management|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T18-58-34.137305.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|truthfulqa:mc|0_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-02T18-58-34.137305.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_02T18_58_34.137305 path: - '**/details_harness|winogrande|5_2024-02-02T18-58-34.137305.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-02T18-58-34.137305.parquet' - config_name: results data_files: - split: 2024_02_02T18_58_34.137305 path: - results_2024-02-02T18-58-34.137305.parquet - split: latest path: - results_2024-02-02T18-58-34.137305.parquet --- # Dataset Card for Evaluation run of ConvexAI/BurningBruce-005  Dataset automatically created during the evaluation run of model [ConvexAI/BurningBruce-005](https://huggingface.co/ConvexAI/BurningBruce-005) 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-005", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-02T18:58:34.137305](https://huggingface.co/datasets/open-llm-leaderboard/details_ConvexAI__BurningBruce-005/blob/main/results_2024-02-02T18-58-34.137305.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": 0.6536170901380703, "acc_stderr": 0.032028038336707275, "acc_norm": 0.6528681277337212, "acc_norm_stderr": 0.032697450933548394, "mc1": 0.543451652386781, "mc1_stderr": 0.017437280953183688, "mc2": 0.6726501530582988, "mc2_stderr": 0.015249067039770463 }, "harness|arc:challenge|25": { "acc": 0.6945392491467577, "acc_stderr": 0.013460080478002507, "acc_norm": 0.7201365187713311, "acc_norm_stderr": 0.013119040897725922 }, "harness|hellaswag|10": { "acc": 0.7117108145787692, "acc_stderr": 0.004520406331084042, "acc_norm": 0.8830910177255527, "acc_norm_stderr": 0.003206551283257396 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7283018867924528, "acc_stderr": 0.027377706624670713, "acc_norm": 0.7283018867924528, "acc_norm_stderr": 0.027377706624670713 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.0358687928008034, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.0358687928008034 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "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": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6878612716763006, "acc_stderr": 0.035331333893236574, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.035331333893236574 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "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": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "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": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083522, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083522 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.035158955511656986, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267042, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267042 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3296296296296296, "acc_stderr": 0.02866120111652456, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.02866120111652456 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886786, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886786 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8440366972477065, "acc_stderr": 0.01555580271359017, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.01555580271359017 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5185185185185185, "acc_stderr": 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"acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4680573663624511, "acc_stderr": 0.012744149704869649, "acc_norm": 0.4680573663624511, "acc_norm_stderr": 0.012744149704869649 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6691176470588235, "acc_stderr": 0.02858270975389845, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.02858270975389845 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.019070985589687495, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.019070985589687495 }, "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": 0.028123429335142773, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142773 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169146, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169146 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.543451652386781, "mc1_stderr": 0.017437280953183688, "mc2": 0.6726501530582988, "mc2_stderr": 0.015249067039770463 }, "harness|winogrande|5": { "acc": 0.8334648776637726, "acc_stderr": 0.010470796496781096 }, "harness|gsm8k|5": { "acc": 0.7149355572403336, "acc_stderr": 0.012435042334904004 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

pradeep239/philp_plain_5k

--- license: mit dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 882548358.845 num_examples: 1873 - name: validation num_bytes: 107622995.0 num_examples: 220 - name: test num_bytes: 53224252.0 num_examples: 111 download_size: 771789438 dataset_size: 1043395605.845 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

fathyshalab/reklamation24_schoenheit-wellness-intent

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 178584 num_examples: 397 - name: test num_bytes: 47435 num_examples: 100 download_size: 127871 dataset_size: 226019 --- # Dataset Card for "reklamation24_schoenheit-wellness-intent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-machine_learning-rule-neg

--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 34262 num_examples: 112 download_size: 19343 dataset_size: 34262 --- # Dataset Card for "mmlu-machine_learning-rule-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

denizzhansahin/Turkish_News-2024

--- dataset_info: features: - name: 'Unnamed: 0.2' dtype: int64 - name: Baslik dtype: string - name: Ozet dtype: string - name: Kategori dtype: string - name: Link dtype: string - name: Icerik dtype: string - name: 'Unnamed: 0' dtype: float64 splits: - name: train num_bytes: 49152035.39270457 num_examples: 19170 - name: validation num_bytes: 21068454.60729543 num_examples: 8217 download_size: 40617048 dataset_size: 70220490.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

0x-YuAN/source

--- dataset_info: features: - name: reason dtype: string - name: self_comment dtype: string - name: other_comment dtype: string - name: relatedIssues list: - name: issueRef dtype: string - name: lawName dtype: string splits: - name: train num_bytes: 1975024677 num_examples: 234054 download_size: 553769254 dataset_size: 1975024677 --- # Dataset Card for "source" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Shularp/350k_dataset_health_ar_en_th

--- dataset_info: features: - name: ar dtype: string - name: en dtype: string - name: th dtype: string splits: - name: validation num_bytes: 4370651 num_examples: 10078 - name: test num_bytes: 4378778 num_examples: 10108 - name: train num_bytes: 122924727 num_examples: 268888 download_size: 70750385 dataset_size: 131674156 --- # Dataset Card for "350k_dataset_health_ar_en_th" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

r-three/Phatgoose_flanv2_offline

--- license: mit ---

Codec-SUPERB/quesst14_all_synth

--- configs: - config_name: default data_files: - split: original path: data/original-* - split: academicodec_hifi_16k_320d path: data/academicodec_hifi_16k_320d-* - split: academicodec_hifi_16k_320d_large_uni path: data/academicodec_hifi_16k_320d_large_uni-* - split: academicodec_hifi_24k_320d path: data/academicodec_hifi_24k_320d-* - split: audiodec_24k_320d path: data/audiodec_24k_320d-* - split: dac_16k path: data/dac_16k-* - split: dac_24k path: data/dac_24k-* - split: dac_44k path: data/dac_44k-* - split: encodec_24k_12bps path: data/encodec_24k_12bps-* - split: encodec_24k_1_5bps path: data/encodec_24k_1_5bps-* - split: encodec_24k_24bps path: data/encodec_24k_24bps-* - split: encodec_24k_3bps path: data/encodec_24k_3bps-* - split: encodec_24k_6bps path: data/encodec_24k_6bps-* - split: funcodec_en_libritts_16k_gr1nq32ds320 path: data/funcodec_en_libritts_16k_gr1nq32ds320-* - split: funcodec_en_libritts_16k_gr8nq32ds320 path: data/funcodec_en_libritts_16k_gr8nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds320 path: data/funcodec_en_libritts_16k_nq32ds320-* - split: funcodec_en_libritts_16k_nq32ds640 path: data/funcodec_en_libritts_16k_nq32ds640-* - split: funcodec_zh_en_16k_nq32ds320 path: data/funcodec_zh_en_16k_nq32ds320-* - split: funcodec_zh_en_16k_nq32ds640 path: data/funcodec_zh_en_16k_nq32ds640-* - split: speech_tokenizer_16k path: data/speech_tokenizer_16k-* dataset_info: features: - name: audio dtype: audio: sampling_rate: 8000 - name: id dtype: string splits: - name: original num_bytes: 1368882918.0 num_examples: 13607 - name: academicodec_hifi_16k_320d num_bytes: 2733824255.0 num_examples: 13607 - name: academicodec_hifi_16k_320d_large_uni num_bytes: 2733824255.0 num_examples: 13607 - name: academicodec_hifi_24k_320d num_bytes: 4100996735.0 num_examples: 13607 - name: audiodec_24k_320d num_bytes: 4107921615.0 num_examples: 13607 - name: dac_16k num_bytes: 2736769119.0 num_examples: 13607 - name: dac_24k num_bytes: 4104632271.0 num_examples: 13607 - name: dac_44k num_bytes: 7541396965.0 num_examples: 13607 - name: encodec_24k_12bps num_bytes: 4104632271.0 num_examples: 13607 - name: encodec_24k_1_5bps num_bytes: 4104632271.0 num_examples: 13607 - name: encodec_24k_24bps num_bytes: 4104632271.0 num_examples: 13607 - name: encodec_24k_3bps num_bytes: 4104632271.0 num_examples: 13607 - name: encodec_24k_6bps num_bytes: 4104632271.0 num_examples: 13607 - name: funcodec_en_libritts_16k_gr1nq32ds320 num_bytes: 2736757881.0 num_examples: 13607 - name: funcodec_en_libritts_16k_gr8nq32ds320 num_bytes: 2736757881.0 num_examples: 13607 - name: funcodec_en_libritts_16k_nq32ds320 num_bytes: 2737231757.0 num_examples: 13607 - name: funcodec_en_libritts_16k_nq32ds640 num_bytes: 2737231757.0 num_examples: 13607 - name: funcodec_zh_en_16k_nq32ds320 num_bytes: 2737231757.0 num_examples: 13607 - name: funcodec_zh_en_16k_nq32ds640 num_bytes: 2737231757.0 num_examples: 13607 - name: speech_tokenizer_16k num_bytes: 2740983853.0 num_examples: 13607 download_size: 18905736290 dataset_size: 69114836131.0 --- # Dataset Card for "quesst14_all_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

FaalSa/dataE

--- dataset_info: features: - name: start dtype: timestamp[s] - name: target sequence: float32 - name: item_id dtype: string - name: feat_static_cat sequence: uint64 splits: - name: train num_bytes: 57629 num_examples: 1 - name: validation num_bytes: 58109 num_examples: 1 - name: test num_bytes: 58589 num_examples: 1 download_size: 12910 dataset_size: 174327 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Maikin023/piuvoz

--- license: openrail ---

tollefj/sickr-sts-NOB

--- license: cc-by-4.0 --- # Translated STS dataset to Norwegian Bokmål Machine translated using the *No language left behind* model series, specifically the 1.3B variant: https://huggingface.co/facebook/nllb-200-distilled-1.3B

davanstrien/model_cards_with_readmes_sections

--- dataset_info: features: - name: license dtype: string - name: tags dtype: string - name: is_nc dtype: bool - name: readme_section dtype: string - name: hash dtype: string splits: - name: train num_bytes: 28801782.8572217 num_examples: 32124 download_size: 13668782 dataset_size: 28801782.8572217 --- # Dataset Card for "model_cards_with_readmes_sections" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

huggingartists/scriptonite

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/scriptonite" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 1.251394 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/411d50392aef867fe0e9dd55a074ecfb.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/scriptonite"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Скриптонит (Scriptonite)</div> <a href="https://genius.com/artists/scriptonite"> <div style="text-align: center; font-size: 14px;">@scriptonite</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/scriptonite). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/scriptonite") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |367| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/scriptonite") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

HuggingFaceH4/ultrachat_200k

--- language: - en license: mit size_categories: - 100K<n<1M task_categories: - text-generation pretty_name: UltraChat 200k configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* - split: train_gen path: data/train_gen-* - split: test_gen path: data/test_gen-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 1397058554 num_examples: 207865 - name: test_sft num_bytes: 154695659 num_examples: 23110 - name: train_gen num_bytes: 1347396812 num_examples: 256032 - name: test_gen num_bytes: 148276089 num_examples: 28304 download_size: 1624049723 dataset_size: 3047427114 --- # Dataset Card for UltraChat 200k ## Dataset Description This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic: - Selection of a subset of data for faster supervised fine tuning. - Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?" - Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either. ## Dataset Structure The dataset has four splits, suitable for: * Supervised fine-tuning (`sft`). * Generation ranking (`gen`) via techniques like rejection sampling or PPO. The number of examples per split is shown as follows: | train_sft | test_sft | train_gen | test_gen | |:-------:|:-----------:|:-----:| :-----:| | 207865 | 23110 | 256032 | 28304 | The dataset is stored in parquet format with each entry using the following schema: ``` { "prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "messages":[ { "content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...", "role": "user" }, { "content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...", "role": "assistant" }, { "content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...", "role": "user" }, { "content": "Certainly! ....", "role": "assistant" }, { "content": "That's really interesting! I would love to hear more...", "role": "user" } { "content": "Certainly! ....", "role": "assistant" }, ], "prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af" } ``` ## Citation If you find this dataset is useful in your work, please cite the original UltraChat dataset: ``` @misc{ding2023enhancing, title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou}, year={2023}, eprint={2305.14233}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` You may also wish to cite the Zephyr 7B technical report: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

liuyanchen1015/MULTI_VALUE_sst2_comparative_more_and

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 3080 num_examples: 19 - name: test num_bytes: 6036 num_examples: 38 - name: train num_bytes: 73392 num_examples: 631 download_size: 35653 dataset_size: 82508 --- # Dataset Card for "MULTI_VALUE_sst2_comparative_more_and" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

FINNUMBER/FINCH_TRAIN_QA_1200_per400_NEWFORMAT

--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 5478684 num_examples: 1200 download_size: 2970119 dataset_size: 5478684 configs: - config_name: default data_files: - split: train path: data/train-* ---

Pinhamusic/candicegomes3

--- license: openrail ---