davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
open-llm-leaderboard/details_NLUHOPOE__Mistral-7B-random-100000	--- pretty_name: Evaluation run of NLUHOPOE/Mistral-7B-random-100000 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [NLUHOPOE/Mistral-7B-random-100000](https://huggingface.co/NLUHOPOE/Mistral-7B-random-100000)\ \ 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_NLUHOPOE__Mistral-7B-random-100000\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-26T09:57:50.433664](https://huggingface.co/datasets/open-llm-leaderboard/details_NLUHOPOE__Mistral-7B-random-100000/blob/main/results_2024-01-26T09-57-50.433664.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\": 0.5312649011937841,\n\ \ \"acc_stderr\": 0.03412886748292059,\n \"acc_norm\": 0.5384137413136626,\n\ \ \"acc_norm_stderr\": 0.03490969986024582,\n \"mc1\": 0.2864137086903305,\n\ \ \"mc1_stderr\": 0.015826142439502356,\n \"mc2\": 0.43163352782122394,\n\ \ \"mc2_stderr\": 0.014658079708747593\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.4948805460750853,\n \"acc_stderr\": 0.014610624890309157,\n\ \ \"acc_norm\": 0.537542662116041,\n \"acc_norm_stderr\": 0.014570144495075576\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5836486755626369,\n\ \ \"acc_stderr\": 0.004919457850104236,\n \"acc_norm\": 0.7859988050189205,\n\ \ \"acc_norm_stderr\": 0.004092894578418981\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\": 0.4888888888888889,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309172,\n\ \ \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309172\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6037735849056604,\n \"acc_stderr\": 0.030102793781791194,\n\ \ \"acc_norm\": 0.6037735849056604,\n \"acc_norm_stderr\": 0.030102793781791194\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5833333333333334,\n\ \ \"acc_stderr\": 0.04122728707651281,\n \"acc_norm\": 0.5833333333333334,\n\ \ \"acc_norm_stderr\": 0.04122728707651281\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n\ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5317919075144508,\n\ \ \"acc_stderr\": 0.03804749744364764,\n \"acc_norm\": 0.5317919075144508,\n\ \ \"acc_norm_stderr\": 0.03804749744364764\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.4765957446808511,\n \"acc_stderr\": 0.03265019475033582,\n\ \ \"acc_norm\": 0.4765957446808511,\n \"acc_norm_stderr\": 0.03265019475033582\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.3684210526315789,\n\ \ \"acc_stderr\": 0.04537815354939392,\n \"acc_norm\": 0.3684210526315789,\n\ \ \"acc_norm_stderr\": 0.04537815354939392\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\"\ : 0.3306878306878307,\n \"acc_stderr\": 0.024229965298425086,\n \"\ acc_norm\": 0.3306878306878307,\n \"acc_norm_stderr\": 0.024229965298425086\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.5806451612903226,\n \"acc_stderr\": 0.028071588901091838,\n \"\ acc_norm\": 0.5806451612903226,\n \"acc_norm_stderr\": 0.028071588901091838\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.3793103448275862,\n \"acc_stderr\": 0.034139638059062345,\n \"\ acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.034139638059062345\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.03567969772268049,\n\ \ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.03567969772268049\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.702020202020202,\n \"acc_stderr\": 0.03258630383836556,\n \"acc_norm\"\ : 0.702020202020202,\n \"acc_norm_stderr\": 0.03258630383836556\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.7668393782383419,\n \"acc_stderr\": 0.03051611137147602,\n\ \ \"acc_norm\": 0.7668393782383419,\n \"acc_norm_stderr\": 0.03051611137147602\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5282051282051282,\n \"acc_stderr\": 0.025310639254933886,\n\ \ \"acc_norm\": 0.5282051282051282,\n \"acc_norm_stderr\": 0.025310639254933886\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683515,\n \ \ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683515\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5504201680672269,\n \"acc_stderr\": 0.03231293497137707,\n \ \ \"acc_norm\": 0.5504201680672269,\n \"acc_norm_stderr\": 0.03231293497137707\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7155963302752294,\n \"acc_stderr\": 0.01934203658770258,\n \"\ acc_norm\": 0.7155963302752294,\n \"acc_norm_stderr\": 0.01934203658770258\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502325,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502325\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.696078431372549,\n \"acc_stderr\": 0.032282103870378935,\n \"\ acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.032282103870378935\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7257383966244726,\n \"acc_stderr\": 0.029041333510598035,\n \ \ \"acc_norm\": 0.7257383966244726,\n \"acc_norm_stderr\": 0.029041333510598035\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6233183856502242,\n\ \ \"acc_stderr\": 0.03252113489929188,\n \"acc_norm\": 0.6233183856502242,\n\ \ \"acc_norm_stderr\": 0.03252113489929188\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.04118438565806298,\n\ \ \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.04118438565806298\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6528925619834711,\n \"acc_stderr\": 0.043457245702925335,\n \"\ acc_norm\": 0.6528925619834711,\n \"acc_norm_stderr\": 0.043457245702925335\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.047500773411999854,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.047500773411999854\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6257668711656442,\n \"acc_stderr\": 0.03802068102899615,\n\ \ \"acc_norm\": 0.6257668711656442,\n \"acc_norm_stderr\": 0.03802068102899615\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.375,\n\ \ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \ \ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8076923076923077,\n\ \ \"acc_stderr\": 0.025819233256483717,\n \"acc_norm\": 0.8076923076923077,\n\ \ \"acc_norm_stderr\": 0.025819233256483717\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7381864623243933,\n\ \ \"acc_stderr\": 0.015720838678445266,\n \"acc_norm\": 0.7381864623243933,\n\ \ \"acc_norm_stderr\": 0.015720838678445266\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5260115606936416,\n \"acc_stderr\": 0.02688264343402289,\n\ \ \"acc_norm\": 0.5260115606936416,\n \"acc_norm_stderr\": 0.02688264343402289\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2681564245810056,\n\ \ \"acc_stderr\": 0.014816119635317012,\n \"acc_norm\": 0.2681564245810056,\n\ \ \"acc_norm_stderr\": 0.014816119635317012\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5915032679738562,\n \"acc_stderr\": 0.028146405993096358,\n\ \ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.028146405993096358\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6109324758842444,\n\ \ \"acc_stderr\": 0.027690337536485372,\n \"acc_norm\": 0.6109324758842444,\n\ \ \"acc_norm_stderr\": 0.027690337536485372\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6080246913580247,\n \"acc_stderr\": 0.027163686038271146,\n\ \ \"acc_norm\": 0.6080246913580247,\n \"acc_norm_stderr\": 0.027163686038271146\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3900709219858156,\n \"acc_stderr\": 0.02909767559946393,\n \ \ \"acc_norm\": 0.3900709219858156,\n \"acc_norm_stderr\": 0.02909767559946393\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3754889178617992,\n\ \ \"acc_stderr\": 0.012367945396728213,\n \"acc_norm\": 0.3754889178617992,\n\ \ \"acc_norm_stderr\": 0.012367945396728213\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904612,\n\ \ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904612\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5147058823529411,\n \"acc_stderr\": 0.020219083895133924,\n \ \ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.020219083895133924\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5818181818181818,\n\ \ \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n\ \ \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.030635655150387638,\n\ \ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.030635655150387638\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6716417910447762,\n\ \ \"acc_stderr\": 0.033206858897443244,\n \"acc_norm\": 0.6716417910447762,\n\ \ \"acc_norm_stderr\": 0.033206858897443244\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4397590361445783,\n\ \ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\ \ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7251461988304093,\n \"acc_stderr\": 0.03424042924691583,\n\ \ \"acc_norm\": 0.7251461988304093,\n \"acc_norm_stderr\": 0.03424042924691583\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2864137086903305,\n\ \ \"mc1_stderr\": 0.015826142439502356,\n \"mc2\": 0.43163352782122394,\n\ \ \"mc2_stderr\": 0.014658079708747593\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7561168113654302,\n \"acc_stderr\": 0.012068923278908189\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.12964366944655042,\n \ \ \"acc_stderr\": 0.00925265775782556\n }\n}\n```" repo_url: https://huggingface.co/NLUHOPOE/Mistral-7B-random-100000 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|arc:challenge\|25_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-26T09-57-50.433664.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|gsm8k\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hellaswag\|10_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-26T09-57-50.433664.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-26T09-57-50.433664.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '/details_harness\|winogrande\|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-26T09-57-50.433664.parquet' - config_name: results data_files: - split: 2024_01_26T09_57_50.433664 path: - results_2024-01-26T09-57-50.433664.parquet - split: latest path: - results_2024-01-26T09-57-50.433664.parquet --- # Dataset Card for Evaluation run of NLUHOPOE/Mistral-7B-random-100000 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [NLUHOPOE/Mistral-7B-random-100000](https://huggingface.co/NLUHOPOE/Mistral-7B-random-100000) 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_NLUHOPOE__Mistral-7B-random-100000", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-26T09:57:50.433664](https://huggingface.co/datasets/open-llm-leaderboard/details_NLUHOPOE__Mistral-7B-random-100000/blob/main/results_2024-01-26T09-57-50.433664.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": 0.5312649011937841, "acc_stderr": 0.03412886748292059, "acc_norm": 0.5384137413136626, "acc_norm_stderr": 0.03490969986024582, "mc1": 0.2864137086903305, "mc1_stderr": 0.015826142439502356, "mc2": 0.43163352782122394, "mc2_stderr": 0.014658079708747593 }, "harness\|arc:challenge\|25": { "acc": 0.4948805460750853, "acc_stderr": 0.014610624890309157, "acc_norm": 0.537542662116041, "acc_norm_stderr": 0.014570144495075576 }, "harness\|hellaswag\|10": { "acc": 0.5836486755626369, "acc_stderr": 0.004919457850104236, "acc_norm": 0.7859988050189205, "acc_norm_stderr": 0.004092894578418981 }, "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": 0.4888888888888889, "acc_stderr": 0.04318275491977976, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.04318275491977976 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5131578947368421, "acc_stderr": 0.04067533136309172, "acc_norm": 0.5131578947368421, "acc_norm_stderr": 0.04067533136309172 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6037735849056604, "acc_stderr": 0.030102793781791194, "acc_norm": 0.6037735849056604, "acc_norm_stderr": 0.030102793781791194 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.04122728707651281, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.04122728707651281 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5317919075144508, "acc_stderr": 0.03804749744364764, "acc_norm": 0.5317919075144508, "acc_norm_stderr": 0.03804749744364764 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4765957446808511, "acc_stderr": 0.03265019475033582, "acc_norm": 0.4765957446808511, "acc_norm_stderr": 0.03265019475033582 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3684210526315789, "acc_stderr": 0.04537815354939392, "acc_norm": 0.3684210526315789, "acc_norm_stderr": 0.04537815354939392 }, "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": 0.3306878306878307, "acc_stderr": 0.024229965298425086, "acc_norm": 0.3306878306878307, "acc_norm_stderr": 0.024229965298425086 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5806451612903226, "acc_stderr": 0.028071588901091838, "acc_norm": 0.5806451612903226, "acc_norm_stderr": 0.028071588901091838 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3793103448275862, "acc_stderr": 0.034139638059062345, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.034139638059062345 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.703030303030303, "acc_stderr": 0.03567969772268049, "acc_norm": 0.703030303030303, "acc_norm_stderr": 0.03567969772268049 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.702020202020202, "acc_stderr": 0.03258630383836556, "acc_norm": 0.702020202020202, "acc_norm_stderr": 0.03258630383836556 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7668393782383419, "acc_stderr": 0.03051611137147602, "acc_norm": 0.7668393782383419, "acc_norm_stderr": 0.03051611137147602 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5282051282051282, "acc_stderr": 0.025310639254933886, "acc_norm": 0.5282051282051282, "acc_norm_stderr": 0.025310639254933886 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3111111111111111, "acc_stderr": 0.028226446749683515, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.028226446749683515 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5504201680672269, "acc_stderr": 0.03231293497137707, "acc_norm": 0.5504201680672269, "acc_norm_stderr": 0.03231293497137707 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526733, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526733 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7155963302752294, "acc_stderr": 0.01934203658770258, "acc_norm": 0.7155963302752294, "acc_norm_stderr": 0.01934203658770258 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03388857118502325, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03388857118502325 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.696078431372549, "acc_stderr": 0.032282103870378935, "acc_norm": 0.696078431372549, "acc_norm_stderr": 0.032282103870378935 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7257383966244726, "acc_stderr": 0.029041333510598035, "acc_norm": 0.7257383966244726, "acc_norm_stderr": 0.029041333510598035 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6233183856502242, "acc_stderr": 0.03252113489929188, "acc_norm": 0.6233183856502242, "acc_norm_stderr": 0.03252113489929188 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6717557251908397, "acc_stderr": 0.04118438565806298, "acc_norm": 0.6717557251908397, "acc_norm_stderr": 0.04118438565806298 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6528925619834711, "acc_stderr": 0.043457245702925335, "acc_norm": 0.6528925619834711, "acc_norm_stderr": 0.043457245702925335 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5925925925925926, "acc_stderr": 0.047500773411999854, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.047500773411999854 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6257668711656442, "acc_stderr": 0.03802068102899615, "acc_norm": 0.6257668711656442, "acc_norm_stderr": 0.03802068102899615 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8076923076923077, "acc_stderr": 0.025819233256483717, "acc_norm": 0.8076923076923077, "acc_norm_stderr": 0.025819233256483717 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7381864623243933, "acc_stderr": 0.015720838678445266, "acc_norm": 0.7381864623243933, "acc_norm_stderr": 0.015720838678445266 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5260115606936416, "acc_stderr": 0.02688264343402289, "acc_norm": 0.5260115606936416, "acc_norm_stderr": 0.02688264343402289 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2681564245810056, "acc_stderr": 0.014816119635317012, "acc_norm": 0.2681564245810056, "acc_norm_stderr": 0.014816119635317012 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5915032679738562, "acc_stderr": 0.028146405993096358, "acc_norm": 0.5915032679738562, "acc_norm_stderr": 0.028146405993096358 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6109324758842444, "acc_stderr": 0.027690337536485372, "acc_norm": 0.6109324758842444, "acc_norm_stderr": 0.027690337536485372 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6080246913580247, "acc_stderr": 0.027163686038271146, "acc_norm": 0.6080246913580247, "acc_norm_stderr": 0.027163686038271146 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3900709219858156, "acc_stderr": 0.02909767559946393, "acc_norm": 0.3900709219858156, "acc_norm_stderr": 0.02909767559946393 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3754889178617992, "acc_stderr": 0.012367945396728213, "acc_norm": 0.3754889178617992, "acc_norm_stderr": 0.012367945396728213 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5147058823529411, "acc_stderr": 0.03035969707904612, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.03035969707904612 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5147058823529411, "acc_stderr": 0.020219083895133924, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.020219083895133924 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5818181818181818, "acc_stderr": 0.04724577405731572, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.04724577405731572 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6448979591836734, "acc_stderr": 0.030635655150387638, "acc_norm": 0.6448979591836734, "acc_norm_stderr": 0.030635655150387638 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6716417910447762, "acc_stderr": 0.033206858897443244, "acc_norm": 0.6716417910447762, "acc_norm_stderr": 0.033206858897443244 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7251461988304093, "acc_stderr": 0.03424042924691583, "acc_norm": 0.7251461988304093, "acc_norm_stderr": 0.03424042924691583 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2864137086903305, "mc1_stderr": 0.015826142439502356, "mc2": 0.43163352782122394, "mc2_stderr": 0.014658079708747593 }, "harness\|winogrande\|5": { "acc": 0.7561168113654302, "acc_stderr": 0.012068923278908189 }, "harness\|gsm8k\|5": { "acc": 0.12964366944655042, "acc_stderr": 0.00925265775782556 } } ``` ## 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]
randomwalksky/cup	--- license: openrail ---
imperialwarrior/open-australian-legal-qa-paraphrased-hard-gemini	--- dataset_info: features: - name: index dtype: 'null' - name: pipeline_1_result dtype: string - name: pipeline_1_result_embeddings dtype: string - name: pipeline_2_context dtype: string - name: pipeline_2_result dtype: string - name: pipeline_2_result_embeddings dtype: string - name: pipeline_3_context dtype: string - name: pipeline_3_result dtype: string - name: pipeline_3_result_embeddings dtype: string - name: pipeline_4_context dtype: string - name: pipeline_4_result dtype: string - name: pipeline_4_result_embeddings dtype: string - name: pipeline_5_context dtype: string - name: pipeline_5_result dtype: string - name: pipeline_5_result_embeddings dtype: string - name: pipeline_6_context dtype: string - name: pipeline_6_result dtype: string - name: pipeline_6_result_embeddings dtype: string - name: pipeline_7_context dtype: string - name: pipeline_7_result dtype: string - name: pipeline_7_result_embeddings dtype: string - name: referenced_question dtype: string - name: answer dtype: string - name: question dtype: string - name: question_non_retrieval_embeddings dtype: string - name: answer_non_retrieval_embeddings dtype: string - name: question_retrieval_embeddings dtype: string - name: answer_retrieval_embeddings dtype: string - name: __index_level_0__ dtype: float64 - name: case_index dtype: float64 - name: pipeline_6_case_indexes sequence: int64 - name: pipeline_7_case_indexes sequence: int64 splits: - name: train num_bytes: 40967131 num_examples: 203 download_size: 14378490 dataset_size: 40967131 configs: - config_name: default data_files: - split: train path: data/train-* ---
zolak/twitter_dataset_79_1713085240	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2654461 num_examples: 6615 download_size: 1346680 dataset_size: 2654461 configs: - config_name: default data_files: - split: train path: data/train-* ---
botp/TigerResearch-pretrain_zh	--- dataset_info: features: - name: dataType dtype: string - name: title dtype: string - name: content dtype: string - name: uniqueKey dtype: string - name: titleUkey dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 58043923125 num_examples: 16905023 download_size: 25662051889 dataset_size: 58043923125 duplicated_from: TigerResearch/pretrain_zh --- # Dataset Card for "pretrain_zh" [Tigerbot](https://github.com/TigerResearch/TigerBot) pretrain数据的中文部分。包含(未压缩前) 中文书籍zh-books 12G, 中文互联网zh-webtext 25G, 中文百科zh-wiki 19G 更多语料请关注开源模型及持续更新 [https://github.com/TigerResearch/TigerBot](https://github.com/TigerResearch/TigerBot) <p align="center" width="40%"> </p> ## Usage ```python import datasets ds_sft = datasets.load_dataset('TigerResearch/pretrain_zh') ```
sam-mosaic/evesix-llama-fmt	--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: language dtype: string - name: response dtype: string splits: - name: train num_bytes: 775938568 num_examples: 486455 download_size: 0 dataset_size: 775938568 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "evesix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
freddyaboulton/gradio-image-urls	--- license: mit ---
severo/doc-image-4	--- size_categories: - n<1K --- # [doc] image dataset 4 This dataset contains 4 jpg image files in the /data directory, with a CSV metadata file providing another data column.
cj-mills/cvat-keypoint-toy-dataset	--- license: mit ---
ovior/twitter_dataset_1713091046	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2394653 num_examples: 6954 download_size: 1377260 dataset_size: 2394653 configs: - config_name: default data_files: - split: train path: data/train-* ---
jxie/qg-tagging	--- dataset_info: features: - name: inputs sequence: sequence: float64 - name: label dtype: int64 splits: - name: train num_bytes: 6944726400 num_examples: 1600000 - name: val num_bytes: 868957000 num_examples: 200000 - name: test num_bytes: 868286700 num_examples: 200000 download_size: 3812296127 dataset_size: 8681970100 --- # Dataset Card for "qg-tagging" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
WinterSchool/ROCO	--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: conversations struct: - name: data list: - name: answer dtype: string - name: question dtype: string splits: - name: train num_bytes: 403971903.769 num_examples: 2101 download_size: 403037177 dataset_size: 403971903.769 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - en tags: - medical size_categories: - 1K<n<10K --- This dataset is made using raw data from [ROCO(Radiology Objects in COntext)]( https://www.semanticscholar.org/paper/Radiology-Objects-in-COntext-(ROCO)%3A-A-Multimodal-Pelka-Koitka/a564fabf130ff6e2742cfba90c7a4018937d764d), a multimodal image dataset, with the aim of detecting the interplay between visual elements and semantic relations present in radiology images. For each image in the original raw dataset we used the associated caption to generate a simulated conversation about the image between a user and a chatbot.
zyxleo/cord_donut_multitask	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: task dtype: string - name: image_path dtype: string - name: ground_truth dtype: string - name: labels sequence: int64 - name: input_ids sequence: int64 splits: - name: train num_bytes: 1260759 num_examples: 800 - name: test num_bytes: 93059 num_examples: 100 - name: validation num_bytes: 86619 num_examples: 100 download_size: 299877 dataset_size: 1440437 --- # Dataset Card for "cord_donut_multitask" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
phanvancongthanh/data_part02	--- dataset_info: features: - name: smiles dtype: string splits: - name: train num_bytes: 5777591178 num_examples: 138701675 download_size: 3034948930 dataset_size: 5777591178 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "data_part02" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
loubnabnl/rmarkdown_checks	--- dataset_info: features: - name: entities list: - name: context dtype: string - name: end dtype: int64 - name: score dtype: float32 - name: start dtype: int64 - name: tag dtype: string - name: value dtype: string - name: max_stars_repo_path dtype: string - name: max_stars_repo_name dtype: string - name: max_stars_count dtype: int64 - name: content dtype: string - name: id dtype: string - name: new_content dtype: string - name: modified dtype: bool - name: references dtype: string splits: - name: train num_bytes: 113015277.61771259 num_examples: 3493 download_size: 62607907 dataset_size: 113015277.61771259 --- # Dataset Card for "rmarkdown_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/kanroji_mitsuri_demonslayer	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kanroji Mitsuri (Demon Slayer) This is the dataset of Kanroji Mitsuri (Demon Slayer), containing 101 images and their tags. The core tags of this character are `pink_hair, long_hair, multicolored_hair, green_hair, braid, mole, mole_under_eye, gradient_hair, green_eyes, twin_braids, breasts, bangs`, 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 \| 101 \| 73.73 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 101 \| 57.92 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 189 \| 109.13 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 101 \| 73.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 189 \| 133.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/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/kanroji_mitsuri_demonslayer', 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 \| 12 \| ![](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, closed_mouth, demon_slayer_uniform, portrait, eyelashes, smile, looking_at_viewer \| \| 1 \| 23 \| ![](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, long_sleeves, demon_slayer_uniform, solo, haori, black_skirt, holding_sword, cleavage, looking_at_viewer, open_clothes, jacket, miniskirt, pleated_skirt, two-tone_hair, katana, partially_unbuttoned, closed_mouth, wide_sleeves, green_thighhighs, ribbed_legwear, belt \| \| 2 \| 5 \| ![](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, demon_slayer_uniform, holding_sword, solo, closed_mouth, katana, from_side, portrait, profile \| \| 3 \| 8 \| ![](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, kimono, solo, looking_at_viewer, cleavage, collarbone, haori, upper_body, blush, floral_print, open_mouth, smile, tears \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| closed_mouth \| demon_slayer_uniform \| portrait \| eyelashes \| smile \| looking_at_viewer \| long_sleeves \| haori \| black_skirt \| holding_sword \| cleavage \| open_clothes \| jacket \| miniskirt \| pleated_skirt \| two-tone_hair \| katana \| partially_unbuttoned \| wide_sleeves \| green_thighhighs \| ribbed_legwear \| belt \| from_side \| profile \| kimono \| collarbone \| upper_body \| blush \| floral_print \| open_mouth \| tears \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:---------------\|:-----------------------\|:-----------\|:------------\|:--------\|:--------------------\|:---------------\|:--------\|:--------------\|:----------------\|:-----------\|:---------------\|:---------\|:------------\|:----------------\|:----------------\|:---------\|:-----------------------\|:---------------\|:-------------------\|:-----------------\|:-------\|:------------\|:----------\|:---------\|:-------------\|:-------------\|:--------\|:---------------\|:-------------\|:--------\| \| 0 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 23 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| 3 \| 8 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| \| \| \| X \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \|
zhengxuanzenwu/fair_glue_qnli	--- dataset_info: features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment - name: idx dtype: int32 splits: - name: train num_bytes: 25612443 num_examples: 104743 - name: validation num_bytes: 250467.5086948563 num_examples: 1000 - name: test num_bytes: 1368304 num_examples: 5463 download_size: 18562140 dataset_size: 27231214.508694857 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
miguelinc/oratorialab	--- license: cc-by-sa-4.0 task_categories: - image-classification --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### 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]
HaawkeNeural/latent_diffusion	--- license: other ---
CesarLeblanc/geoplantbert_fill_mask_dataset	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 881264679 num_examples: 900042 - name: test num_bytes: 97851633 num_examples: 99958 download_size: 388245641 dataset_size: 979116312 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
coref-data/litbank_indiscrim	--- dataset_info: - config_name: split_0 features: - name: sentences list: - name: id dtype: int64 - name: misc struct: - name: parse_tree dtype: string - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: deprel dtype: string - name: end_char dtype: int64 - name: feats dtype: string - name: head dtype: int64 - name: id dtype: int64 - name: lemma dtype: string - name: misc dtype: string - name: start_char dtype: int64 - name: text dtype: string - name: upos dtype: string - name: xpos dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 66722053 num_examples: 80 - name: validation num_bytes: 9538946 num_examples: 10 - name: test num_bytes: 10206291 num_examples: 10 download_size: 44024474 dataset_size: 86467290 - config_name: split_1 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 51521261 num_examples: 80 - name: validation num_bytes: 8300522 num_examples: 10 - name: test num_bytes: 7127546 num_examples: 10 download_size: 40296693 dataset_size: 66949329 - config_name: split_2 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 51695718 num_examples: 80 - name: validation num_bytes: 7127546 num_examples: 10 - name: test num_bytes: 8126065 num_examples: 10 download_size: 40287905 dataset_size: 66949329 - config_name: split_3 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 52504381 num_examples: 80 - name: validation num_bytes: 8126065 num_examples: 10 - name: test num_bytes: 6318883 num_examples: 10 download_size: 40292412 dataset_size: 66949329 - config_name: split_4 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 54684836 num_examples: 80 - name: validation num_bytes: 6318883 num_examples: 10 - name: test num_bytes: 5945610 num_examples: 10 download_size: 40283365 dataset_size: 66949329 - config_name: split_5 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 53798360 num_examples: 80 - name: validation num_bytes: 5945610 num_examples: 10 - name: test num_bytes: 7205359 num_examples: 10 download_size: 40284379 dataset_size: 66949329 - config_name: split_6 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 53481831 num_examples: 80 - name: validation num_bytes: 7205359 num_examples: 10 - name: test num_bytes: 6262139 num_examples: 10 download_size: 40294155 dataset_size: 66949329 - config_name: split_7 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 54849391 num_examples: 80 - name: validation num_bytes: 6262139 num_examples: 10 - name: test num_bytes: 5837799 num_examples: 10 download_size: 40294847 dataset_size: 66949329 - config_name: split_8 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 56921350 num_examples: 80 - name: validation num_bytes: 5837799 num_examples: 10 - name: test num_bytes: 4190180 num_examples: 10 download_size: 40292974 dataset_size: 66949329 - config_name: split_9 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 55123923 num_examples: 80 - name: validation num_bytes: 4190180 num_examples: 10 - name: test num_bytes: 7635226 num_examples: 10 download_size: 40294593 dataset_size: 66949329 configs: - config_name: split_0 data_files: - split: train path: split_0/train-* - split: validation path: split_0/validation-* - split: test path: split_0/test-* - config_name: split_1 data_files: - split: train path: split_1/train-* - split: validation path: split_1/validation-* - split: test path: split_1/test-* - config_name: split_2 data_files: - split: train path: split_2/train-* - split: validation path: split_2/validation-* - split: test path: split_2/test-* - config_name: split_3 data_files: - split: train path: split_3/train-* - split: validation path: split_3/validation-* - split: test path: split_3/test-* - config_name: split_4 data_files: - split: train path: split_4/train-* - split: validation path: split_4/validation-* - split: test path: split_4/test-* - config_name: split_5 data_files: - split: train path: split_5/train-* - split: validation path: split_5/validation-* - split: test path: split_5/test-* - config_name: split_6 data_files: - split: train path: split_6/train-* - split: validation path: split_6/validation-* - split: test path: split_6/test-* - config_name: split_7 data_files: - split: train path: split_7/train-* - split: validation path: split_7/validation-* - split: test path: split_7/test-* - config_name: split_8 data_files: - split: train path: split_8/train-* - split: validation path: split_8/validation-* - split: test path: split_8/test-* - config_name: split_9 data_files: - split: train path: split_9/train-* - split: validation path: split_9/validation-* - split: test path: split_9/test-* --- This dataset was generated by reformatting [`coref-data/litbank_raw`](https://huggingface.co/datasets/coref-data/litbank_raw) into the indiscrim coreference format. See that repo for dataset details. See [ianporada/coref-data](https://github.com/ianporada/coref-data) for additional conversion details and the conversion script. Please create an issue in the repo above or in this dataset repo for any questions.
CyberHarem/akutsu_ruri_ahogirl	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Akutsu Ruri This is the dataset of Akutsu Ruri, containing 109 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 \| 109 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 224 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 109 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 109 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 109 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 109 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 109 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 224 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 224 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 224 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
Innominate/LargeConvo2048	--- dataset_info: features: - name: input dtype: string splits: - name: train num_bytes: 1793023981 num_examples: 984989 download_size: 974905351 dataset_size: 1793023981 task_categories: - text-generation --- A large dataset to train Churro. Every element is under 2048 tokens, when tokenized using the LLaMA Tokenizer.
KenBars/item_rec	--- license: mit ---
qq371/11111	--- license: epl-2.0 ---
FaalSa/f1	--- 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: 79710 num_examples: 1 - name: validation num_bytes: 80190 num_examples: 1 - name: test num_bytes: 80670 num_examples: 1 download_size: 69501 dataset_size: 240570 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
satyanshu404/MS-Marco-Prompt-generation	--- license: unknown ---
andersonbcdefg/dolly-ai-filtered	--- dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string splits: - name: train num_bytes: 2939273 num_examples: 5444 download_size: 0 dataset_size: 2939273 --- # Dataset Card for "dolly-ai-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JovialValley/phoneme_totalMapped0	--- dataset_info: features: - name: input_values sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 108844668 num_examples: 389 - name: test num_bytes: 27494376 num_examples: 98 download_size: 137098876 dataset_size: 136339044 --- # Dataset Card for "phoneme_totalMapped0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
julienmercier/mobile-eye-tracking-dataset-v3	--- license: cc-by-nc-nd-4.0 ---
huggingartists/joni-mitchell	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/joni-mitchell" ## 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: 0.703544 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/ed9a330b2539058076e0c48398599b09.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/joni-mitchell"> <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">Joni Mitchell</div> <a href="https://genius.com/artists/joni-mitchell"> <div style="text-align: center; font-size: 14px;">@joni-mitchell</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/joni-mitchell). ### 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/joni-mitchell") ``` ## 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\| \|------:\|---------:\|---:\| \|559\| -\| -\| '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/joni-mitchell") 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)
KPrashanth/articles_dataset	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 13354657 num_examples: 3188 - name: validation num_bytes: 3257643 num_examples: 798 - name: test num_bytes: 4221414 num_examples: 997 download_size: 9383756 dataset_size: 20833714 task_categories: - text-generation - summarization - text2text-generation - text-classification --- # Dataset Card for "articles_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_fblgit__una-cybertron-7b-v3-OMA	--- pretty_name: Evaluation run of fblgit/una-cybertron-7b-v3-OMA dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [fblgit/una-cybertron-7b-v3-OMA](https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA)\ \ 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_fblgit__una-cybertron-7b-v3-OMA\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T14:22:11.823260](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v3-OMA/blob/main/results_2023-12-16T14-22-11.823260.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\": 0.6407887213707157,\n\ \ \"acc_stderr\": 0.032306194957506966,\n \"acc_norm\": 0.6401991329877219,\n\ \ \"acc_norm_stderr\": 0.03297436021123899,\n \"mc1\": 0.5801713586291309,\n\ \ \"mc1_stderr\": 0.017277030301775766,\n \"mc2\": 0.6984571807866093,\n\ \ \"mc2_stderr\": 0.01516400593831668\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7098976109215017,\n \"acc_stderr\": 0.013261573677520766,\n\ \ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869155\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7183827922724557,\n\ \ \"acc_stderr\": 0.0044886843979795015,\n \"acc_norm\": 0.8794064927305317,\n\ \ \"acc_norm_stderr\": 0.0032498873947065044\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-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\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7291666666666666,\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\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\"\ : 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3968253968253968,\n \"acc_stderr\": 0.02519710107424649,\n \"\ acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.02519710107424649\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4523809523809524,\n\ \ \"acc_stderr\": 0.04451807959055328,\n \"acc_norm\": 0.4523809523809524,\n\ \ \"acc_norm_stderr\": 0.04451807959055328\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\": 0.8,\n\ \ \"acc_stderr\": 0.022755204959542943,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.022755204959542943\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n\ \ \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\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\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479047,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479047\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\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602354,\n\ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602354\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\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650155,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650155\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8137254901960784,\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\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7557251908396947,\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\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516301,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516301\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371802\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577612,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577612\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.45251396648044695,\n\ \ \"acc_stderr\": 0.016646914804438775,\n \"acc_norm\": 0.45251396648044695,\n\ \ \"acc_norm_stderr\": 0.016646914804438775\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n\ \ \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7041800643086816,\n\ \ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\ \ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.0286619962023353,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.0286619962023353\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6584967320261438,\n \"acc_stderr\": 0.019184639328092487,\n \ \ \"acc_norm\": 0.6584967320261438,\n \"acc_norm_stderr\": 0.019184639328092487\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.025196929874827075,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.025196929874827075\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685517,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685517\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\": 0.5801713586291309,\n\ \ \"mc1_stderr\": 0.017277030301775766,\n \"mc2\": 0.6984571807866093,\n\ \ \"mc2_stderr\": 0.01516400593831668\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047987\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6770280515542078,\n \ \ \"acc_stderr\": 0.012880360794851815\n }\n}\n```" repo_url: https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|arc:challenge\|25_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-16T14-22-11.823260.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|gsm8k\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hellaswag\|10_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T14-22-11.823260.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T14-22-11.823260.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '/details_harness\|winogrande\|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-16T14-22-11.823260.parquet' - config_name: results data_files: - split: 2023_12_16T14_22_11.823260 path: - results_2023-12-16T14-22-11.823260.parquet - split: latest path: - results_2023-12-16T14-22-11.823260.parquet --- # Dataset Card for Evaluation run of fblgit/una-cybertron-7b-v3-OMA <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [fblgit/una-cybertron-7b-v3-OMA](https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA) 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_fblgit__una-cybertron-7b-v3-OMA", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T14:22:11.823260](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v3-OMA/blob/main/results_2023-12-16T14-22-11.823260.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": 0.6407887213707157, "acc_stderr": 0.032306194957506966, "acc_norm": 0.6401991329877219, "acc_norm_stderr": 0.03297436021123899, "mc1": 0.5801713586291309, "mc1_stderr": 0.017277030301775766, "mc2": 0.6984571807866093, "mc2_stderr": 0.01516400593831668 }, "harness\|arc:challenge\|25": { "acc": 0.7098976109215017, "acc_stderr": 0.013261573677520766, "acc_norm": 0.7303754266211604, "acc_norm_stderr": 0.012968040686869155 }, "harness\|hellaswag\|10": { "acc": 0.7183827922724557, "acc_stderr": 0.0044886843979795015, "acc_norm": 0.8794064927305317, "acc_norm_stderr": 0.0032498873947065044 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "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": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566017, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566017 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6473988439306358, "acc_stderr": 0.03643037168958548, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.03643037168958548 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.048108401480826346, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.048108401480826346 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.02519710107424649, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.02519710107424649 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4523809523809524, "acc_stderr": 0.04451807959055328, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.04451807959055328 }, "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": 0.8, "acc_stderr": 0.022755204959542943, "acc_norm": 0.8, "acc_norm_stderr": 0.022755204959542943 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.46798029556650245, "acc_stderr": 0.035107665979592154, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.035107665979592154 }, "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": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479047, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479047 }, "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": 0.6410256410256411, "acc_stderr": 0.024321738484602354, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.024321738484602354 }, "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": 0.634453781512605, "acc_stderr": 0.031282177063684614, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.031282177063684614 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8366972477064221, "acc_stderr": 0.01584825580650155, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.01584825580650155 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "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": 0.7520661157024794, "acc_stderr": 0.03941897526516301, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.03941897526516301 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371802, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371802 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577612, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577612 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.45251396648044695, "acc_stderr": 0.016646914804438775, "acc_norm": 0.45251396648044695, "acc_norm_stderr": 0.016646914804438775 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6993464052287581, "acc_stderr": 0.02625605383571896, "acc_norm": 0.6993464052287581, "acc_norm_stderr": 0.02625605383571896 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7041800643086816, "acc_stderr": 0.025922371788818763, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.025922371788818763 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303957, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303957 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6654411764705882, "acc_stderr": 0.0286619962023353, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.0286619962023353 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6584967320261438, "acc_stderr": 0.019184639328092487, "acc_norm": 0.6584967320261438, "acc_norm_stderr": 0.019184639328092487 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.025196929874827075, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.025196929874827075 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685517, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685517 }, "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": 0.5801713586291309, "mc1_stderr": 0.017277030301775766, "mc2": 0.6984571807866093, "mc2_stderr": 0.01516400593831668 }, "harness\|winogrande\|5": { "acc": 0.8208366219415943, "acc_stderr": 0.010777949156047987 }, "harness\|gsm8k\|5": { "acc": 0.6770280515542078, "acc_stderr": 0.012880360794851815 } } ``` ## 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]
hippocrates/MedQA_one_shot_test	--- dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: train num_bytes: 2491204 num_examples: 1273 - name: valid num_bytes: 2491204 num_examples: 1273 - name: test num_bytes: 2491204 num_examples: 1273 download_size: 2482272 dataset_size: 7473612 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
Tristan/olm-wikipedia-20221220-1-percent-tokenized-766	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 300178944 num_examples: 65143 download_size: 93964466 dataset_size: 300178944 --- # Dataset Card for "olm-wikipedia-20221220-1-percent-tokenized-766" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/imai_midori_shirobako	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Imai Midori This is the dataset of Imai Midori, containing 276 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 \| 276 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 611 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 276 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 276 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 276 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 276 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 276 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 611 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 611 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 611 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
open-llm-leaderboard/details_Aryanne__sheared-plus-westlake-50_75p	--- pretty_name: Evaluation run of Aryanne/sheared-plus-westlake-50_75p dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aryanne/sheared-plus-westlake-50_75p](https://huggingface.co/Aryanne/sheared-plus-westlake-50_75p)\ \ 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_Aryanne__sheared-plus-westlake-50_75p\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-23T22:04:31.166175](https://huggingface.co/datasets/open-llm-leaderboard/details_Aryanne__sheared-plus-westlake-50_75p/blob/main/results_2024-01-23T22-04-31.166175.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\": 0.2672140448144015,\n\ \ \"acc_stderr\": 0.03127543112931,\n \"acc_norm\": 0.26909676356851875,\n\ \ \"acc_norm_stderr\": 0.03210076459110669,\n \"mc1\": 0.2668298653610771,\n\ \ \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.42638955634632064,\n\ \ \"mc2_stderr\": 0.014788435851867392\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.3310580204778157,\n \"acc_stderr\": 0.013752062419817836,\n\ \ \"acc_norm\": 0.34044368600682595,\n \"acc_norm_stderr\": 0.013847460518892983\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4441346345349532,\n\ \ \"acc_stderr\": 0.004958537988993581,\n \"acc_norm\": 0.5804620593507269,\n\ \ \"acc_norm_stderr\": 0.004924748500639335\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.32592592592592595,\n\ \ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.32592592592592595,\n\ \ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.27631578947368424,\n \"acc_stderr\": 0.03639057569952924,\n\ \ \"acc_norm\": 0.27631578947368424,\n \"acc_norm_stderr\": 0.03639057569952924\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.32,\n\ \ \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \ \ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.24150943396226415,\n \"acc_stderr\": 0.026341480371118355,\n\ \ \"acc_norm\": 0.24150943396226415,\n \"acc_norm_stderr\": 0.026341480371118355\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\"\ : 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.23699421965317918,\n\ \ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n\ \ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n\ \ \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.20425531914893616,\n \"acc_stderr\": 0.026355158413349417,\n\ \ \"acc_norm\": 0.20425531914893616,\n \"acc_norm_stderr\": 0.026355158413349417\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2543859649122807,\n\ \ \"acc_stderr\": 0.04096985139843671,\n \"acc_norm\": 0.2543859649122807,\n\ \ \"acc_norm_stderr\": 0.04096985139843671\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.22758620689655173,\n \"acc_stderr\": 0.03493950380131184,\n\ \ \"acc_norm\": 0.22758620689655173,\n \"acc_norm_stderr\": 0.03493950380131184\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.25925925925925924,\n \"acc_stderr\": 0.02256989707491841,\n \"\ acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.02256989707491841\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.1984126984126984,\n\ \ \"acc_stderr\": 0.03567016675276863,\n \"acc_norm\": 0.1984126984126984,\n\ \ \"acc_norm_stderr\": 0.03567016675276863\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.24516129032258063,\n \"acc_stderr\": 0.02447224384089553,\n \"\ acc_norm\": 0.24516129032258063,\n \"acc_norm_stderr\": 0.02447224384089553\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114468,\n \"\ acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114468\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.28484848484848485,\n \"acc_stderr\": 0.035243908445117836,\n\ \ \"acc_norm\": 0.28484848484848485,\n \"acc_norm_stderr\": 0.035243908445117836\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.21212121212121213,\n \"acc_stderr\": 0.029126522834586818,\n \"\ acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.029126522834586818\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.27461139896373055,\n \"acc_stderr\": 0.03221024508041156,\n\ \ \"acc_norm\": 0.27461139896373055,\n \"acc_norm_stderr\": 0.03221024508041156\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.2692307692307692,\n \"acc_stderr\": 0.022489389793654824,\n\ \ \"acc_norm\": 0.2692307692307692,\n \"acc_norm_stderr\": 0.022489389793654824\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2777777777777778,\n \"acc_stderr\": 0.02730914058823018,\n \ \ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.02730914058823018\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.19747899159663865,\n \"acc_stderr\": 0.025859164122051463,\n\ \ \"acc_norm\": 0.19747899159663865,\n \"acc_norm_stderr\": 0.025859164122051463\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.20733944954128442,\n \"acc_stderr\": 0.017381415563608664,\n \"\ acc_norm\": 0.20733944954128442,\n \"acc_norm_stderr\": 0.017381415563608664\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.3287037037037037,\n \"acc_stderr\": 0.03203614084670058,\n \"\ acc_norm\": 0.3287037037037037,\n \"acc_norm_stderr\": 0.03203614084670058\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604246,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604246\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.26582278481012656,\n \"acc_stderr\": 0.02875679962965834,\n \ \ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.02875679962965834\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.31390134529147984,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2809917355371901,\n \"acc_stderr\": 0.04103203830514512,\n \"\ acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.04103203830514512\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.3055555555555556,\n\ \ \"acc_stderr\": 0.044531975073749834,\n \"acc_norm\": 0.3055555555555556,\n\ \ \"acc_norm_stderr\": 0.044531975073749834\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.2822085889570552,\n \"acc_stderr\": 0.03536117886664742,\n\ \ \"acc_norm\": 0.2822085889570552,\n \"acc_norm_stderr\": 0.03536117886664742\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3482142857142857,\n\ \ \"acc_stderr\": 0.04521829902833585,\n \"acc_norm\": 0.3482142857142857,\n\ \ \"acc_norm_stderr\": 0.04521829902833585\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.1650485436893204,\n \"acc_stderr\": 0.036756688322331886,\n\ \ \"acc_norm\": 0.1650485436893204,\n \"acc_norm_stderr\": 0.036756688322331886\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2264957264957265,\n\ \ \"acc_stderr\": 0.02742100729539294,\n \"acc_norm\": 0.2264957264957265,\n\ \ \"acc_norm_stderr\": 0.02742100729539294\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.23499361430395913,\n\ \ \"acc_stderr\": 0.01516202415227844,\n \"acc_norm\": 0.23499361430395913,\n\ \ \"acc_norm_stderr\": 0.01516202415227844\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.2976878612716763,\n \"acc_stderr\": 0.024617055388677003,\n\ \ \"acc_norm\": 0.2976878612716763,\n \"acc_norm_stderr\": 0.024617055388677003\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.24916201117318434,\n\ \ \"acc_stderr\": 0.014465893829859926,\n \"acc_norm\": 0.24916201117318434,\n\ \ \"acc_norm_stderr\": 0.014465893829859926\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.23202614379084968,\n \"acc_stderr\": 0.02417084087934101,\n\ \ \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.02417084087934101\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.29260450160771706,\n\ \ \"acc_stderr\": 0.025839898334877983,\n \"acc_norm\": 0.29260450160771706,\n\ \ \"acc_norm_stderr\": 0.025839898334877983\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.2191358024691358,\n \"acc_stderr\": 0.023016705640262192,\n\ \ \"acc_norm\": 0.2191358024691358,\n \"acc_norm_stderr\": 0.023016705640262192\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.2198581560283688,\n \"acc_stderr\": 0.024706141070705474,\n \ \ \"acc_norm\": 0.2198581560283688,\n \"acc_norm_stderr\": 0.024706141070705474\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2379400260756193,\n\ \ \"acc_stderr\": 0.010875700787694228,\n \"acc_norm\": 0.2379400260756193,\n\ \ \"acc_norm_stderr\": 0.010875700787694228\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.35661764705882354,\n \"acc_stderr\": 0.029097209568411955,\n\ \ \"acc_norm\": 0.35661764705882354,\n \"acc_norm_stderr\": 0.029097209568411955\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.25980392156862747,\n \"acc_stderr\": 0.017740899509177795,\n \ \ \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.017740899509177795\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.20909090909090908,\n\ \ \"acc_stderr\": 0.038950910157241364,\n \"acc_norm\": 0.20909090909090908,\n\ \ \"acc_norm_stderr\": 0.038950910157241364\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.20408163265306123,\n \"acc_stderr\": 0.025801283475090506,\n\ \ \"acc_norm\": 0.20408163265306123,\n \"acc_norm_stderr\": 0.025801283475090506\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.25870646766169153,\n\ \ \"acc_stderr\": 0.030965903123573012,\n \"acc_norm\": 0.25870646766169153,\n\ \ \"acc_norm_stderr\": 0.030965903123573012\n },\n \"harness\|hendrycksTest-us_foreign_policy\|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-virology\|5\": {\n \"acc\": 0.2710843373493976,\n\ \ \"acc_stderr\": 0.03460579907553027,\n \"acc_norm\": 0.2710843373493976,\n\ \ \"acc_norm_stderr\": 0.03460579907553027\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.28654970760233917,\n \"acc_stderr\": 0.03467826685703826,\n\ \ \"acc_norm\": 0.28654970760233917,\n \"acc_norm_stderr\": 0.03467826685703826\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2668298653610771,\n\ \ \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.42638955634632064,\n\ \ \"mc2_stderr\": 0.014788435851867392\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.569060773480663,\n \"acc_stderr\": 0.013917796623335964\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/Aryanne/sheared-plus-westlake-50_75p leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|arc:challenge\|25_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-23T22-04-31.166175.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|gsm8k\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hellaswag\|10_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-23T22-04-31.166175.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-23T22-04-31.166175.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '/details_harness\|winogrande\|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-23T22-04-31.166175.parquet' - config_name: results data_files: - split: 2024_01_23T22_04_31.166175 path: - results_2024-01-23T22-04-31.166175.parquet - split: latest path: - results_2024-01-23T22-04-31.166175.parquet --- # Dataset Card for Evaluation run of Aryanne/sheared-plus-westlake-50_75p <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Aryanne/sheared-plus-westlake-50_75p](https://huggingface.co/Aryanne/sheared-plus-westlake-50_75p) 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_Aryanne__sheared-plus-westlake-50_75p", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-23T22:04:31.166175](https://huggingface.co/datasets/open-llm-leaderboard/details_Aryanne__sheared-plus-westlake-50_75p/blob/main/results_2024-01-23T22-04-31.166175.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": 0.2672140448144015, "acc_stderr": 0.03127543112931, "acc_norm": 0.26909676356851875, "acc_norm_stderr": 0.03210076459110669, "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.42638955634632064, "mc2_stderr": 0.014788435851867392 }, "harness\|arc:challenge\|25": { "acc": 0.3310580204778157, "acc_stderr": 0.013752062419817836, "acc_norm": 0.34044368600682595, "acc_norm_stderr": 0.013847460518892983 }, "harness\|hellaswag\|10": { "acc": 0.4441346345349532, "acc_stderr": 0.004958537988993581, "acc_norm": 0.5804620593507269, "acc_norm_stderr": 0.004924748500639335 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.32592592592592595, "acc_stderr": 0.040491220417025055, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.040491220417025055 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.27631578947368424, "acc_stderr": 0.03639057569952924, "acc_norm": 0.27631578947368424, "acc_norm_stderr": 0.03639057569952924 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.24150943396226415, "acc_stderr": 0.026341480371118355, "acc_norm": 0.24150943396226415, "acc_norm_stderr": 0.026341480371118355 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542129, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542129 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.23699421965317918, "acc_stderr": 0.03242414757483098, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483098 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237655, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237655 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.20425531914893616, "acc_stderr": 0.026355158413349417, "acc_norm": 0.20425531914893616, "acc_norm_stderr": 0.026355158413349417 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2543859649122807, "acc_stderr": 0.04096985139843671, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.04096985139843671 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.22758620689655173, "acc_stderr": 0.03493950380131184, "acc_norm": 0.22758620689655173, "acc_norm_stderr": 0.03493950380131184 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.02256989707491841, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.02256989707491841 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.1984126984126984, "acc_stderr": 0.03567016675276863, "acc_norm": 0.1984126984126984, "acc_norm_stderr": 0.03567016675276863 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.24516129032258063, "acc_stderr": 0.02447224384089553, "acc_norm": 0.24516129032258063, "acc_norm_stderr": 0.02447224384089553 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.26108374384236455, "acc_stderr": 0.030903796952114468, "acc_norm": 0.26108374384236455, "acc_norm_stderr": 0.030903796952114468 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.28484848484848485, "acc_stderr": 0.035243908445117836, "acc_norm": 0.28484848484848485, "acc_norm_stderr": 0.035243908445117836 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.21212121212121213, "acc_stderr": 0.029126522834586818, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.029126522834586818 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.27461139896373055, "acc_stderr": 0.03221024508041156, "acc_norm": 0.27461139896373055, "acc_norm_stderr": 0.03221024508041156 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2692307692307692, "acc_stderr": 0.022489389793654824, "acc_norm": 0.2692307692307692, "acc_norm_stderr": 0.022489389793654824 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.02730914058823018, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.02730914058823018 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.19747899159663865, "acc_stderr": 0.025859164122051463, "acc_norm": 0.19747899159663865, "acc_norm_stderr": 0.025859164122051463 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763743, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763743 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.20733944954128442, "acc_stderr": 0.017381415563608664, "acc_norm": 0.20733944954128442, "acc_norm_stderr": 0.017381415563608664 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.3287037037037037, "acc_stderr": 0.03203614084670058, "acc_norm": 0.3287037037037037, "acc_norm_stderr": 0.03203614084670058 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604246, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604246 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.26582278481012656, "acc_stderr": 0.02875679962965834, "acc_norm": 0.26582278481012656, "acc_norm_stderr": 0.02875679962965834 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.31390134529147984, "acc_stderr": 0.031146796482972465, "acc_norm": 0.31390134529147984, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2809917355371901, "acc_stderr": 0.04103203830514512, "acc_norm": 0.2809917355371901, "acc_norm_stderr": 0.04103203830514512 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.3055555555555556, "acc_stderr": 0.044531975073749834, "acc_norm": 0.3055555555555556, "acc_norm_stderr": 0.044531975073749834 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.2822085889570552, "acc_stderr": 0.03536117886664742, "acc_norm": 0.2822085889570552, "acc_norm_stderr": 0.03536117886664742 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3482142857142857, "acc_stderr": 0.04521829902833585, "acc_norm": 0.3482142857142857, "acc_norm_stderr": 0.04521829902833585 }, "harness\|hendrycksTest-management\|5": { "acc": 0.1650485436893204, "acc_stderr": 0.036756688322331886, "acc_norm": 0.1650485436893204, "acc_norm_stderr": 0.036756688322331886 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2264957264957265, "acc_stderr": 0.02742100729539294, "acc_norm": 0.2264957264957265, "acc_norm_stderr": 0.02742100729539294 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.23499361430395913, "acc_stderr": 0.01516202415227844, "acc_norm": 0.23499361430395913, "acc_norm_stderr": 0.01516202415227844 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.2976878612716763, "acc_stderr": 0.024617055388677003, "acc_norm": 0.2976878612716763, "acc_norm_stderr": 0.024617055388677003 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24916201117318434, "acc_stderr": 0.014465893829859926, "acc_norm": 0.24916201117318434, "acc_norm_stderr": 0.014465893829859926 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.23202614379084968, "acc_stderr": 0.02417084087934101, "acc_norm": 0.23202614379084968, "acc_norm_stderr": 0.02417084087934101 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.29260450160771706, "acc_stderr": 0.025839898334877983, "acc_norm": 0.29260450160771706, "acc_norm_stderr": 0.025839898334877983 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2191358024691358, "acc_stderr": 0.023016705640262192, "acc_norm": 0.2191358024691358, "acc_norm_stderr": 0.023016705640262192 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2198581560283688, "acc_stderr": 0.024706141070705474, "acc_norm": 0.2198581560283688, "acc_norm_stderr": 0.024706141070705474 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2379400260756193, "acc_stderr": 0.010875700787694228, "acc_norm": 0.2379400260756193, "acc_norm_stderr": 0.010875700787694228 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.35661764705882354, "acc_stderr": 0.029097209568411955, "acc_norm": 0.35661764705882354, "acc_norm_stderr": 0.029097209568411955 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.25980392156862747, "acc_stderr": 0.017740899509177795, "acc_norm": 0.25980392156862747, "acc_norm_stderr": 0.017740899509177795 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.20909090909090908, "acc_stderr": 0.038950910157241364, "acc_norm": 0.20909090909090908, "acc_norm_stderr": 0.038950910157241364 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.20408163265306123, "acc_stderr": 0.025801283475090506, "acc_norm": 0.20408163265306123, "acc_norm_stderr": 0.025801283475090506 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.25870646766169153, "acc_stderr": 0.030965903123573012, "acc_norm": 0.25870646766169153, "acc_norm_stderr": 0.030965903123573012 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553027, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553027 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.28654970760233917, "acc_stderr": 0.03467826685703826, "acc_norm": 0.28654970760233917, "acc_norm_stderr": 0.03467826685703826 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.42638955634632064, "mc2_stderr": 0.014788435851867392 }, "harness\|winogrande\|5": { "acc": 0.569060773480663, "acc_stderr": 0.013917796623335964 }, "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 <!-- 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]
open-llm-leaderboard/details_kekmodel__StopCarbon-10.7B-v3	--- pretty_name: Evaluation run of kekmodel/StopCarbon-10.7B-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kekmodel/StopCarbon-10.7B-v3](https://huggingface.co/kekmodel/StopCarbon-10.7B-v3)\ \ 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_kekmodel__StopCarbon-10.7B-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-30T10:15:50.941228](https://huggingface.co/datasets/open-llm-leaderboard/details_kekmodel__StopCarbon-10.7B-v3/blob/main/results_2023-12-30T10-15-50.941228.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\": 0.6649827029825734,\n\ \ \"acc_stderr\": 0.03166471620730208,\n \"acc_norm\": 0.6659533597079996,\n\ \ \"acc_norm_stderr\": 0.03230745700615819,\n \"mc1\": 0.572827417380661,\n\ \ \"mc1_stderr\": 0.017316834410963926,\n \"mc2\": 0.7193506614125464,\n\ \ \"mc2_stderr\": 0.014949525122441177\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6860068259385665,\n \"acc_stderr\": 0.013562691224726293,\n\ \ \"acc_norm\": 0.7098976109215017,\n \"acc_norm_stderr\": 0.013261573677520764\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7180840470025891,\n\ \ \"acc_stderr\": 0.004490130691020433,\n \"acc_norm\": 0.8856801433977295,\n\ \ \"acc_norm_stderr\": 0.003175490413694419\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness\|hendrycksTest-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\": 0.743421052631579,\n \"acc_stderr\": 0.0355418036802569,\n\ \ \"acc_norm\": 0.743421052631579,\n \"acc_norm_stderr\": 0.0355418036802569\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.72,\n\ \ \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\"\ : {\n \"acc\": 0.5,\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\": 0.34,\n\ \ \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \ \ \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-college_medicine\|5\"\ : {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n\ \ \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.38235294117647056,\n\ \ \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n\ \ \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768077,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768077\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.6212765957446809,\n\ \ \"acc_stderr\": 0.03170995606040655,\n \"acc_norm\": 0.6212765957446809,\n\ \ \"acc_norm_stderr\": 0.03170995606040655\n },\n \"harness\|hendrycksTest-econometrics\|5\"\ : {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n\ \ \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n\ \ },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\"\ : 0.6206896551724138,\n \"acc_stderr\": 0.040434618619167466,\n \"\ acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.040434618619167466\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4947089947089947,\n \"acc_stderr\": 0.02574986828855657,\n \"\ acc_norm\": 0.4947089947089947,\n \"acc_norm_stderr\": 0.02574986828855657\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8161290322580645,\n \"acc_stderr\": 0.022037217340267822,\n \"\ acc_norm\": 0.8161290322580645,\n \"acc_norm_stderr\": 0.022037217340267822\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"\ acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\ \ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857406,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857406\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634332,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634332\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242741,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242741\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669235,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669235\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5648148148148148,\n \"acc_stderr\": 0.03381200005643527,\n \"\ acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.03381200005643527\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8529411764705882,\n \"acc_stderr\": 0.02485747808025046,\n \"\ acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02485747808025046\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8565400843881856,\n \"acc_stderr\": 0.022818291821017012,\n \ \ \"acc_norm\": 0.8565400843881856,\n \"acc_norm_stderr\": 0.022818291821017012\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.03160295143776678,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.03160295143776678\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\": 0.03749492448709696,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709696\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\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\": 0.8543689320388349,\n \"acc_stderr\": 0.03492606476623791,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.03492606476623791\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8504273504273504,\n\ \ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\ \ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.80970625798212,\n\ \ \"acc_stderr\": 0.014036945850381396,\n \"acc_norm\": 0.80970625798212,\n\ \ \"acc_norm_stderr\": 0.014036945850381396\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4201117318435754,\n\ \ \"acc_stderr\": 0.016507671073256402,\n \"acc_norm\": 0.4201117318435754,\n\ \ \"acc_norm_stderr\": 0.016507671073256402\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.02463004897982478,\n\ \ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.02463004897982478\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7266881028938906,\n\ \ \"acc_stderr\": 0.025311765975426122,\n \"acc_norm\": 0.7266881028938906,\n\ \ \"acc_norm_stderr\": 0.025311765975426122\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7746913580246914,\n \"acc_stderr\": 0.023246202647819753,\n\ \ \"acc_norm\": 0.7746913580246914,\n \"acc_norm_stderr\": 0.023246202647819753\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4908735332464146,\n\ \ \"acc_stderr\": 0.01276810860164001,\n \"acc_norm\": 0.4908735332464146,\n\ \ \"acc_norm_stderr\": 0.01276810860164001\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7463235294117647,\n \"acc_stderr\": 0.026431329870789527,\n\ \ \"acc_norm\": 0.7463235294117647,\n \"acc_norm_stderr\": 0.026431329870789527\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\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\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\ \ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.02553843336857834,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.02553843336857834\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.032180937956023566,\n\ \ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.032180937956023566\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.572827417380661,\n\ \ \"mc1_stderr\": 0.017316834410963926,\n \"mc2\": 0.7193506614125464,\n\ \ \"mc2_stderr\": 0.014949525122441177\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8318863456985004,\n \"acc_stderr\": 0.010510336954166732\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6322971948445792,\n \ \ \"acc_stderr\": 0.013281630503395475\n }\n}\n```" repo_url: https://huggingface.co/kekmodel/StopCarbon-10.7B-v3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|arc:challenge\|25_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-30T10-15-50.941228.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|gsm8k\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hellaswag\|10_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T10-15-50.941228.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-30T10-15-50.941228.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '/details_harness\|winogrande\|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-30T10-15-50.941228.parquet' - config_name: results data_files: - split: 2023_12_30T10_15_50.941228 path: - results_2023-12-30T10-15-50.941228.parquet - split: latest path: - results_2023-12-30T10-15-50.941228.parquet --- # Dataset Card for Evaluation run of kekmodel/StopCarbon-10.7B-v3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [kekmodel/StopCarbon-10.7B-v3](https://huggingface.co/kekmodel/StopCarbon-10.7B-v3) 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_kekmodel__StopCarbon-10.7B-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-30T10:15:50.941228](https://huggingface.co/datasets/open-llm-leaderboard/details_kekmodel__StopCarbon-10.7B-v3/blob/main/results_2023-12-30T10-15-50.941228.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": 0.6649827029825734, "acc_stderr": 0.03166471620730208, "acc_norm": 0.6659533597079996, "acc_norm_stderr": 0.03230745700615819, "mc1": 0.572827417380661, "mc1_stderr": 0.017316834410963926, "mc2": 0.7193506614125464, "mc2_stderr": 0.014949525122441177 }, "harness\|arc:challenge\|25": { "acc": 0.6860068259385665, "acc_stderr": 0.013562691224726293, "acc_norm": 0.7098976109215017, "acc_norm_stderr": 0.013261573677520764 }, "harness\|hellaswag\|10": { "acc": 0.7180840470025891, "acc_stderr": 0.004490130691020433, "acc_norm": 0.8856801433977295, "acc_norm_stderr": 0.003175490413694419 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "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": 0.743421052631579, "acc_stderr": 0.0355418036802569, "acc_norm": 0.743421052631579, "acc_norm_stderr": 0.0355418036802569 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.690566037735849, "acc_stderr": 0.028450154794118637, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118637 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768077, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768077 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6212765957446809, "acc_stderr": 0.03170995606040655, "acc_norm": 0.6212765957446809, "acc_norm_stderr": 0.03170995606040655 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6206896551724138, "acc_stderr": 0.040434618619167466, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.040434618619167466 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4947089947089947, "acc_stderr": 0.02574986828855657, "acc_norm": 0.4947089947089947, "acc_norm_stderr": 0.02574986828855657 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8161290322580645, "acc_stderr": 0.022037217340267822, "acc_norm": 0.8161290322580645, "acc_norm_stderr": 0.022037217340267822 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.35555555555555557, "acc_stderr": 0.029185714949857406, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.029185714949857406 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.029344572500634332, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.029344572500634332 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242741, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242741 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669235, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669235 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5648148148148148, "acc_stderr": 0.03381200005643527, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.03381200005643527 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8529411764705882, "acc_stderr": 0.02485747808025046, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.02485747808025046 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8565400843881856, "acc_stderr": 0.022818291821017012, "acc_norm": 0.8565400843881856, "acc_norm_stderr": 0.022818291821017012 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6681614349775785, "acc_stderr": 0.03160295143776678, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.03160295143776678 }, "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": 0.03749492448709696, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.03749492448709696 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037182, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037182 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "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": 0.8543689320388349, "acc_stderr": 0.03492606476623791, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.03492606476623791 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.80970625798212, "acc_stderr": 0.014036945850381396, "acc_norm": 0.80970625798212, "acc_norm_stderr": 0.014036945850381396 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7514450867052023, "acc_stderr": 0.023267528432100174, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.023267528432100174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4201117318435754, "acc_stderr": 0.016507671073256402, "acc_norm": 0.4201117318435754, "acc_norm_stderr": 0.016507671073256402 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.02463004897982478, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.02463004897982478 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7266881028938906, "acc_stderr": 0.025311765975426122, "acc_norm": 0.7266881028938906, "acc_norm_stderr": 0.025311765975426122 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7746913580246914, "acc_stderr": 0.023246202647819753, "acc_norm": 0.7746913580246914, "acc_norm_stderr": 0.023246202647819753 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4908735332464146, "acc_stderr": 0.01276810860164001, "acc_norm": 0.4908735332464146, "acc_norm_stderr": 0.01276810860164001 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7463235294117647, "acc_stderr": 0.026431329870789527, "acc_norm": 0.7463235294117647, "acc_norm_stderr": 0.026431329870789527 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6781045751633987, "acc_stderr": 0.018901015322093092, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.018901015322093092 }, "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": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.845771144278607, "acc_stderr": 0.02553843336857834, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.02553843336857834 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7719298245614035, "acc_stderr": 0.032180937956023566, "acc_norm": 0.7719298245614035, "acc_norm_stderr": 0.032180937956023566 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.572827417380661, "mc1_stderr": 0.017316834410963926, "mc2": 0.7193506614125464, "mc2_stderr": 0.014949525122441177 }, "harness\|winogrande\|5": { "acc": 0.8318863456985004, "acc_stderr": 0.010510336954166732 }, "harness\|gsm8k\|5": { "acc": 0.6322971948445792, "acc_stderr": 0.013281630503395475 } } ``` ## 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 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open-llm-leaderboard/details_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1	--- pretty_name: Evaluation run of Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1](https://huggingface.co/Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1)\ \ 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_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-01T21:44:48.551629](https://huggingface.co/datasets/open-llm-leaderboard/details_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1/blob/main/results_2024-02-01T21-44-48.551629.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\": 0.235616909983485,\n\ \ \"acc_stderr\": 0.030096196864815804,\n \"acc_norm\": 0.23617812988980863,\n\ \ \"acc_norm_stderr\": 0.030895352600212644,\n \"mc1\": 0.2460220318237454,\n\ \ \"mc1_stderr\": 0.015077219200662578,\n \"mc2\": 0.49848823283731625,\n\ \ \"mc2_stderr\": 0.016449164481650215\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.2090443686006826,\n \"acc_stderr\": 0.011882746987406458,\n\ \ \"acc_norm\": 0.2645051194539249,\n \"acc_norm_stderr\": 0.012889272949313366\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.25632344154550885,\n\ \ \"acc_stderr\": 0.004357101984278612,\n \"acc_norm\": 0.2568213503286198,\n\ \ \"acc_norm_stderr\": 0.00435987151963954\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.18518518518518517,\n\ \ \"acc_stderr\": 0.03355677216313141,\n \"acc_norm\": 0.18518518518518517,\n\ \ \"acc_norm_stderr\": 0.03355677216313141\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.24150943396226415,\n \"acc_stderr\": 0.026341480371118376,\n\ \ \"acc_norm\": 0.24150943396226415,\n \"acc_norm_stderr\": 0.026341480371118376\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816507,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816507\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.2023121387283237,\n\ \ \"acc_stderr\": 0.03063114553919882,\n \"acc_norm\": 0.2023121387283237,\n\ \ \"acc_norm_stderr\": 0.03063114553919882\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\ \ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.21052631578947367,\n\ \ \"acc_stderr\": 0.038351539543994194,\n \"acc_norm\": 0.21052631578947367,\n\ \ \"acc_norm_stderr\": 0.038351539543994194\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\ acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036625,\n \ \ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036625\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.1774193548387097,\n\ \ \"acc_stderr\": 0.02173254068932927,\n \"acc_norm\": 0.1774193548387097,\n\ \ \"acc_norm_stderr\": 0.02173254068932927\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.18719211822660098,\n \"acc_stderr\": 0.027444924966882618,\n\ \ \"acc_norm\": 0.18719211822660098,\n \"acc_norm_stderr\": 0.027444924966882618\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.2222222222222222,\n \"acc_stderr\": 0.029620227874790482,\n \"\ acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.029620227874790482\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.2,\n \"acc_stderr\": 0.020280805062535722,\n \"acc_norm\"\ : 0.2,\n \"acc_norm_stderr\": 0.020280805062535722\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\"\ : {\n \"acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n\ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.026265024608275882,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.026265024608275882\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.19205298013245034,\n \"acc_stderr\": 0.032162984205936135,\n \"\ acc_norm\": 0.19205298013245034,\n \"acc_norm_stderr\": 0.032162984205936135\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.1889908256880734,\n \"acc_stderr\": 0.016785481159203634,\n \"\ acc_norm\": 0.1889908256880734,\n \"acc_norm_stderr\": 0.016785481159203634\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.14814814814814814,\n \"acc_stderr\": 0.024227629273728356,\n \"\ acc_norm\": 0.14814814814814814,\n \"acc_norm_stderr\": 0.024227629273728356\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.3004484304932735,\n\ \ \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.3004484304932735,\n\ \ \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.22137404580152673,\n \"acc_stderr\": 0.0364129708131373,\n\ \ \"acc_norm\": 0.22137404580152673,\n \"acc_norm_stderr\": 0.0364129708131373\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2905982905982906,\n\ \ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\ \ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.24393358876117496,\n\ \ \"acc_stderr\": 0.015357212665829489,\n \"acc_norm\": 0.24393358876117496,\n\ \ \"acc_norm_stderr\": 0.015357212665829489\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.2581699346405229,\n \"acc_stderr\": 0.025058503316958157,\n\ \ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.025058503316958157\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.1864951768488746,\n\ \ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\ \ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.02841820861940679,\n\ \ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.02841820861940679\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness\|hendrycksTest-public_relations\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\ \ },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.18775510204081633,\n\ \ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\ \ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\ \ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\ \ },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-virology\|5\"\ : {\n \"acc\": 0.2710843373493976,\n \"acc_stderr\": 0.034605799075530276,\n\ \ \"acc_norm\": 0.2710843373493976,\n \"acc_norm_stderr\": 0.034605799075530276\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.32748538011695905,\n\ \ \"acc_stderr\": 0.035993357714560276,\n \"acc_norm\": 0.32748538011695905,\n\ \ \"acc_norm_stderr\": 0.035993357714560276\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 0.2460220318237454,\n \"mc1_stderr\": 0.015077219200662578,\n\ \ \"mc2\": 0.49848823283731625,\n \"mc2_stderr\": 0.016449164481650215\n\ \ },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.4940805051302289,\n\ \ \"acc_stderr\": 0.014051500838485807\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 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_01T21_44_48.551629 path: - '/details_harness\|arc:challenge\|25_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-01T21-44-48.551629.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|gsm8k\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hellaswag\|10_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-01T21-44-48.551629.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-01T21-44-48.551629.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '/details_harness\|winogrande\|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-01T21-44-48.551629.parquet' - config_name: results data_files: - split: 2024_02_01T21_44_48.551629 path: - results_2024-02-01T21-44-48.551629.parquet - split: latest path: - results_2024-02-01T21-44-48.551629.parquet --- # Dataset Card for Evaluation run of Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1](https://huggingface.co/Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1) 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_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-01T21:44:48.551629](https://huggingface.co/datasets/open-llm-leaderboard/details_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1/blob/main/results_2024-02-01T21-44-48.551629.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": 0.235616909983485, "acc_stderr": 0.030096196864815804, "acc_norm": 0.23617812988980863, "acc_norm_stderr": 0.030895352600212644, "mc1": 0.2460220318237454, "mc1_stderr": 0.015077219200662578, "mc2": 0.49848823283731625, "mc2_stderr": 0.016449164481650215 }, "harness\|arc:challenge\|25": { "acc": 0.2090443686006826, "acc_stderr": 0.011882746987406458, "acc_norm": 0.2645051194539249, "acc_norm_stderr": 0.012889272949313366 }, "harness\|hellaswag\|10": { "acc": 0.25632344154550885, "acc_stderr": 0.004357101984278612, "acc_norm": 0.2568213503286198, "acc_norm_stderr": 0.00435987151963954 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03355677216313141, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03355677216313141 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.24150943396226415, "acc_stderr": 0.026341480371118376, "acc_norm": 0.24150943396226415, "acc_norm_stderr": 0.026341480371118376 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.25, "acc_stderr": 0.03621034121889507, "acc_norm": 0.25, "acc_norm_stderr": 0.03621034121889507 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816507, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816507 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.2023121387283237, "acc_stderr": 0.03063114553919882, "acc_norm": 0.2023121387283237, "acc_norm_stderr": 0.03063114553919882 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.21052631578947367, "acc_stderr": 0.038351539543994194, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.038351539543994194 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.20899470899470898, "acc_stderr": 0.02094048156533486, "acc_norm": 0.20899470899470898, "acc_norm_stderr": 0.02094048156533486 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04040610178208841, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04040610178208841 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.19, "acc_stderr": 0.03942772444036625, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036625 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.18719211822660098, "acc_stderr": 0.027444924966882618, "acc_norm": 0.18719211822660098, "acc_norm_stderr": 0.027444924966882618 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.2222222222222222, "acc_stderr": 0.029620227874790482, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.029620227874790482 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2, "acc_stderr": 0.020280805062535722, "acc_norm": 0.2, "acc_norm_stderr": 0.020280805062535722 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2111111111111111, "acc_stderr": 0.024882116857655075, "acc_norm": 0.2111111111111111, "acc_norm_stderr": 0.024882116857655075 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.20588235294117646, "acc_stderr": 0.026265024608275882, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.026265024608275882 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.19205298013245034, "acc_stderr": 0.032162984205936135, "acc_norm": 0.19205298013245034, "acc_norm_stderr": 0.032162984205936135 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.1889908256880734, "acc_stderr": 0.016785481159203634, "acc_norm": 0.1889908256880734, "acc_norm_stderr": 0.016785481159203634 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.14814814814814814, "acc_stderr": 0.024227629273728356, "acc_norm": 0.14814814814814814, "acc_norm_stderr": 0.024227629273728356 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.270042194092827, "acc_stderr": 0.028900721906293426, "acc_norm": 0.270042194092827, "acc_norm_stderr": 0.028900721906293426 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.3004484304932735, "acc_stderr": 0.030769352008229143, "acc_norm": 0.3004484304932735, "acc_norm_stderr": 0.030769352008229143 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.22137404580152673, "acc_stderr": 0.0364129708131373, "acc_norm": 0.22137404580152673, "acc_norm_stderr": 0.0364129708131373 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.042365112580946336, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.042365112580946336 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness\|hendrycksTest-management\|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2905982905982906, "acc_stderr": 0.02974504857267404, "acc_norm": 0.2905982905982906, "acc_norm_stderr": 0.02974504857267404 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.24393358876117496, "acc_stderr": 0.015357212665829489, "acc_norm": 0.24393358876117496, "acc_norm_stderr": 0.015357212665829489 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.2581699346405229, "acc_stderr": 0.025058503316958157, "acc_norm": 0.2581699346405229, "acc_norm_stderr": 0.025058503316958157 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.1864951768488746, "acc_stderr": 0.02212243977248077, "acc_norm": 0.1864951768488746, "acc_norm_stderr": 0.02212243977248077 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432417, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432417 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.3235294117647059, "acc_stderr": 0.02841820861940679, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.02841820861940679 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.2710843373493976, "acc_stderr": 0.034605799075530276, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.034605799075530276 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.32748538011695905, "acc_stderr": 0.035993357714560276, "acc_norm": 0.32748538011695905, "acc_norm_stderr": 0.035993357714560276 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2460220318237454, "mc1_stderr": 0.015077219200662578, "mc2": 0.49848823283731625, "mc2_stderr": 0.016449164481650215 }, "harness\|winogrande\|5": { "acc": 0.4940805051302289, "acc_stderr": 0.014051500838485807 }, "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 <!-- 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]
senhorsapo/yor	--- license: openrail ---
ibm/Wish-QA-ASQA-Falcon	--- dataset_info: features: - name: id dtype: string - name: old_question dtype: string - name: old_answer dtype: string - name: passage_1 dtype: string - name: passage_2 dtype: string - name: passage_3 dtype: string - name: text dtype: string - name: qa dtype: string - name: question dtype: string - name: answer dtype: string - name: doc_score dtype: float64 - name: score_qa dtype: float64 - name: ans_num_words dtype: int64 - name: text_num_words dtype: int64 - name: text_longer_1.5 dtype: int64 - name: input dtype: string - name: output 0 answer dtype: string splits: - name: train num_bytes: 23433520 num_examples: 4354 download_size: 14082055 dataset_size: 23433520 --- # Dataset Card for "Wish-QA-ASQA-Falcon" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yekta/banchan	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6899876.0 num_examples: 13 download_size: 6901557 dataset_size: 6899876.0 --- # Dataset Card for "banchan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Deojoandco/dialogturns_not_generated_train	--- dataset_info: features: - name: url dtype: string - name: id dtype: string - name: num_comments dtype: int64 - name: name dtype: string - name: title dtype: string - name: body dtype: string - name: score dtype: int64 - name: upvote_ratio dtype: float64 - name: distinguished dtype: string - name: over_18 dtype: bool - name: created_utc dtype: int64 - name: comments list: - name: body dtype: string - name: created_utc dtype: float64 - name: distinguished dtype: string - name: id dtype: string - name: permalink dtype: string - name: score dtype: int64 - name: best_num_comments dtype: int64 - name: query dtype: string - name: dialog dtype: string - name: annotation_success dtype: bool - name: annotation_text dtype: string - name: turns_generated dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 7511834 num_examples: 284 download_size: 4057449 dataset_size: 7511834 --- # Dataset Card for "dialogturns_not_generated_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nexdata/Handwriting_OCR_Data_of_Japanese_and_Korean	--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Handwriting_OCR_Data_of_Japanese_and_Korean ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.nexdata.ai/datasets/127?source=Huggingface - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary 100 People - Handwriting OCR Data of Japanese and Korean,. This dadaset was collected from 100 subjects including 50 Japanese, 49 Koreans and 1 Afghan. For different subjects, the corpus are different. The data diversity includes multiple cellphone models and different corpus. This dataset can be used for tasks, such as handwriting OCR data of Japanese and Korean. For more details, please refer to the link: https://www.nexdata.ai/datasets/127?source=Huggingface ### Supported Tasks and Leaderboards image-to-text, computer-vision: The dataset can be used to train a model for image-to-text. ### Languages Japanese, Korean ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
arieg/bw_spec_cls_80_11	--- 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': '27855' '1': '27856' '2': '27866' '3': '27945' '4': '27953' '5': '27975' '6': '27978' '7': '27981' '8': '27987' '9': '28241' '10': '28260' '11': '28477' '12': '28478' '13': '28479' '14': '28480' '15': '28481' '16': '28482' '17': '28483' '18': '28484' '19': '28485' '20': '28546' '21': '28548' '22': '28553' '23': '28571' '24': '28608' '25': '29045' '26': '29128' '27': '29180' '28': '29243' '29': '29245' '30': '29255' '31': '29271' '32': '29272' '33': '29355' '34': '29465' '35': '29480' '36': '29587' '37': '29602' '38': '29673' '39': '29718' '40': '29719' '41': '29720' '42': '29721' '43': '29738' '44': '29739' '45': '29740' '46': '29741' '47': '29742' '48': '29744' '49': '29745' '50': '29746' '51': '29747' '52': '29750' '53': '29752' '54': '29807' '55': '29813' '56': '29816' '57': '29961' '58': '29971' '59': '30041' '60': '30043' '61': '30050' '62': '30056' '63': '30058' '64': '30059' '65': '30090' '66': '30095' '67': '30120' '68': '30196' '69': '30198' '70': '30230' '71': '30486' '72': '30487' '73': '30488' '74': '30519' '75': '30520' '76': '30521' '77': '30522' '78': '30636' '79': '30690' splits: - name: train num_bytes: 89109867.2 num_examples: 1600 download_size: 88188426 dataset_size: 89109867.2 --- # Dataset Card for "bw_spec_cls_80_11" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-wmt14-de-en-fbedb0-67643145604	--- type: predictions tags: - autotrain - evaluation datasets: - wmt14 eval_info: task: translation model: leukas/byt5-large-wmt14-deen metrics: ['bleu'] dataset_name: wmt14 dataset_config: de-en dataset_split: test col_mapping: source: translation.de target: translation.en --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Translation * Model: leukas/byt5-large-wmt14-deen * Dataset: wmt14 * Config: de-en * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@seeed](https://huggingface.co/seeed) for evaluating this model.
Carlisle/msmacro-passage-non-abs-small	--- license: mit ---
vgaraujov/fapesp	--- language: - en - es - pt license: - cc-by-2.0 task_categories: - translation dataset_info: - config_name: en-pt features: - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 47417503 num_examples: 160975 - name: validation num_bytes: 405055 num_examples: 1375 - name: test num_bytes: 407579 num_examples: 1447 download_size: 29615550 dataset_size: 48230137 - config_name: es-pt features: - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 47480897 num_examples: 158197 - name: validation num_bytes: 377101 num_examples: 1302 - name: test num_bytes: 400915 num_examples: 1379 download_size: 29829573 dataset_size: 48258913 configs: - config_name: en-pt data_files: - split: train path: en-pt/train-* - split: validation path: en-pt/validation-* - split: test path: en-pt/test-* - config_name: es-pt data_files: - split: train path: es-pt/train-* - split: validation path: es-pt/validation-* - split: test path: es-pt/test-* ---
ibranze/araproje_arc_tr_f2	--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: validation num_bytes: 86423.0 num_examples: 250 download_size: 46973 dataset_size: 86423.0 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "araproje_arc_tr_f2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ywan111/macbook-dataset-b4	--- license: apache-2.0 ---
MLNavigator/russian-retrieval	--- license: mit --- Based on Sberquad - Answer converted to human affordable answer. - Context augmented with some pices of texts from wiki accordant to text on tematic and keywords. - This dataset cold be used for training retrieval LLM models or modificators for ability of LLM to retrieve target information from collection of tematic related texts. - Dataset has version with SOURCE data for generating answer with specifing source document for right answer. See file retrieval_dataset_src.jsonl Dataset consists of 45278 examples in russian language of format: { 'text': 'text with correct answer', 'q': 'question text', 'a': 'correct answer text', 'context': 'text of 4-10 text chunks, one with right answer and others relevant with text and question on tematic and keywords' } Length of one example of context + question + answer is less than 7000 symbols. It should be less than 2048 tokens of rugpt tokenizer. File retrieval_dataset_src.jsonl has additionally SOURCE data for every text chunk in context, also SOURCE of right answer is set in answer. This variant of dataset is useful if you need extract answer with specifing source of the right answer. { 'text': 'text with correct answer', 'q': 'question text', 'a': 'correct answer text with SOURCE data of text', 'context': 'text of 4-10 text chunks, one with right answer and others relevant with text and question on tematic and keywords. Each of text chunks has it's own SOURCE data' } All SOURCE data are sintetic generated and not real.
taesiri/imagenet_hard_review_data	--- license: mit ---
arthurmluz/GPTextSum_data-wiki_1024_results	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 25941 num_examples: 20 download_size: 32992 dataset_size: 25941 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "GPTextSum_data-wiki_1024_results" rouge= {'rouge1': 0.20436494957206813, 'rouge2': 0.06669792477248418, 'rougeL': 0.1645584797463879, 'rougeLsum': 0.1645584797463879} bert= {'precision': 0.7313757807016372, 'recall': 0.6589481264352799, 'f1': 0.6928485721349716}
Jessiecs/llama-2-7b-a3-self-curated	--- dataset_info: features: - name: instruction_generated dtype: string - name: response dtype: string - name: rating_score dtype: string - name: is_high_quality dtype: bool splits: - name: train num_bytes: 231166 num_examples: 128 download_size: 152410 dataset_size: 231166 configs: - config_name: default data_files: - split: train path: data/train-* ---
gianma/eurlexsum_ita_cleaned_8192_232	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: is_camera dtype: bool - name: reference dtype: string - name: summary dtype: string - name: tokenized_len_total dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4119487 num_examples: 228 - name: validation num_bytes: 231666 num_examples: 13 - name: test num_bytes: 253451 num_examples: 13 download_size: 0 dataset_size: 4604604 --- # Dataset Card for "eurlexsum_ita_cleaned_8192_232" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jonathanli/winston-ai-luka-dataset	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: model dtype: string splits: - name: train num_bytes: 29522548 num_examples: 10000 download_size: 16797973 dataset_size: 29522548 configs: - config_name: default data_files: - split: train path: data/train-* ---
sherelyn912/finnews_en_2wk_qa	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 310555.2191464821 num_examples: 1387 - name: test num_bytes: 77694.78085351788 num_examples: 347 download_size: 182095 dataset_size: 388250.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- extracted content from various website articles using code from [FinNLP](https://github.com/AI4Finance-Foundation/FinNLP) and generated questions and answers with OpenAI API gpt3.5-turbo-16k
David-Egea/Creditcard-fraud-detection	--- license: mit --- # Credit Card Fraud Detection This dataset was downloaded from https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud/data adn uploaded for educational purposes.
CronosGhost/wikipedia_fr_snippets	--- license: mit dataset_info: features: - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 8126467952 num_examples: 12138717 download_size: 4271960527 dataset_size: 8126467952 configs: - config_name: default data_files: - split: train path: data/train-* ---
mwong/climate-evidence-related	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 - gpl-3.0 multilinguality: - monolingual paperswithcode_id: climate-fever pretty_name: climate-fever size_categories: - 100K<n<1M source_datasets: - extended\|climate_fever task_categories: - text-classification task_ids: - fact-checking --- ### Dataset Summary This dataset is extracted from Climate Fever dataset (https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html), pre-processed and ready to train and evaluate. The training objective is a text classification task - given a claim and evidence, predict if evidence is related to claim.
gagan3012/FSR	--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: answer dtype: string - name: query dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: test num_bytes: 5874352 num_examples: 3931 download_size: 1688819 dataset_size: 5874352 configs: - config_name: default data_files: - split: test path: data/test-* ---
liuyanchen1015/MULTI_VALUE_mrpc_a_ing	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 194382 num_examples: 670 - name: train num_bytes: 412800 num_examples: 1423 - name: validation num_bytes: 45079 num_examples: 150 download_size: 437103 dataset_size: 652261 --- # Dataset Card for "MULTI_VALUE_mrpc_a_ing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
automated-research-group/llama2_7b_chat-agieval-results	--- dataset_info: config_name: '{''do_sample''=False, ''beams''=1}' features: - name: id dtype: string - name: prediction dtype: string - name: agieval_accuracy dtype: bool splits: - name: train num_bytes: 85792 num_examples: 254 download_size: 46709 dataset_size: 85792 configs: - config_name: '{''do_sample''=False, ''beams''=1}' data_files: - split: train path: '{''do_sample''=False, ''beams''=1}/train-*' ---
coref-data/niv2_winogrande_raw	--- license: apache-2.0 --- # Natural Instructions v2 Winogrande Tasks - Project: https://github.com/allenai/natural-instructions - Data source: [DataProvenanceInitiative/niv2_submix_original](https://huggingface.co/datasets/DataProvenanceInitiative/niv2_submix_original) ## Details This dataset contains all Winogrande examples that were included in the [Flan 2022 collection](https://github.com/google-research/FLAN/tree/main/flan/v2) which were orignally published in Super-Natural-Instructions. The data is copied from the preprocessed Natural Instructions v2 dataset at [DataProvenanceInitiative/niv2_submix_original](https://huggingface.co/datasets/DataProvenanceInitiative/niv2_submix_original). These tasks are: 1. 'task029_winogrande_full_object': Creating a pair of fill in the blank question-answer pairs on objects. 2. 'task030_winogrande_full_person': Creating a pair of fill in the blank questions on persons. 3. 'task031_winogrande_question_generation_object': Writing a fill in the blank question on objects. 4. 'task032_winogrande_question_generation_person': Writing a fill in the blank question on persons. 5. 'task033_winogrande_answer_generation': Answering a fill in the blank question on objects. 6. 'task034_winogrande_question_modification_object': Modifying a fill in the blank question on objects. 7. 'task035_winogrande_question_modification_person': Modifying a fill in the blank question on persons. 8. 'task1391_winogrande_easy_answer_generation': Answering a fill in the blank question on objects. ### Fields - `inputs`: a `string` feature. - `targets`: a `string` feature. - `task_source`: a `string` feature. - `task_name`: a `string` feature. - `template_type`: a `string` feature. ## Citation ``` @inproceedings{wang-etal-2022-super, title = "Super-{N}atural{I}nstructions: Generalization via Declarative Instructions on 1600+ {NLP} Tasks", author = "Wang, Yizhong and Mishra, Swaroop and Alipoormolabashi, Pegah and Kordi, Yeganeh and Mirzaei, Amirreza and Naik, Atharva and Ashok, Arjun and Dhanasekaran, Arut Selvan and Arunkumar, Anjana and Stap, David and Pathak, Eshaan and Karamanolakis, Giannis and Lai, Haizhi and Purohit, Ishan and Mondal, Ishani and Anderson, Jacob and Kuznia, Kirby and Doshi, Krima and Pal, Kuntal Kumar and Patel, Maitreya and Moradshahi, Mehrad and Parmar, Mihir and Purohit, Mirali and Varshney, Neeraj and Kaza, Phani Rohitha and Verma, Pulkit and Puri, Ravsehaj Singh and Karia, Rushang and Doshi, Savan and Sampat, Shailaja Keyur and Mishra, Siddhartha and Reddy A, Sujan and Patro, Sumanta and Dixit, Tanay and Shen, Xudong", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.340", doi = "10.18653/v1/2022.emnlp-main.340", pages = "5085--5109", abstract = "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions{---}training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9{\%} on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.", } ```
Zarakun/audiobooks_ua_test	--- tags: - "audio" configs: - config_name: default data_files: - split: train path: "data/train.parquet" --- ### About dataset It is a dataset of ukrainian audiobooks Each sample contain an approximately 8 seconds od ukrainian speech ### Loading script ``` >>> load_dataset("Zarakun/audiobooks_ua_test") ``` ### Dataset structure Every example has the following: audio - the waveform rate - the sampling rate of the waveform file_id - the id of the speaker duration - the duration of the video in seconds sentence** - the transcript of the video
Shivanshyadav/wrinkled_to_ironed_clothes	--- license: apache-2.0 dataset_info: features: - name: input_image dtype: image - name: instruct_prompt dtype: string - name: output_image dtype: image splits: - name: train num_bytes: 20133465629.31 num_examples: 3022 download_size: 19529267242 dataset_size: 20133465629.31 configs: - config_name: default data_files: - split: train path: data/train-* ---
neerajaabhyankar/hindustani-raag-small	--- license: cc-by-4.0 task_categories: - audio-classification tags: - music - hindustani - raag - raga - raaga pretty_name: Hindustani Raag Identification (Small) size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: '*/train_.mp3' - split: test path: '*/test_.mp3' ---
open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat	--- pretty_name: Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kyujinpy/PlatYi-34B-Llama-Q-FastChat](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat)\ \ 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_kyujinpy__PlatYi-34B-Llama-Q-FastChat\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-10T05:55:07.023442](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat/blob/main/results_2023-12-10T05-55-07.023442.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\": 0.7741514926490987,\n\ \ \"acc_stderr\": 0.027646135380835733,\n \"acc_norm\": 0.7828326159595959,\n\ \ \"acc_norm_stderr\": 0.02814394317924737,\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5362104216200869,\n\ \ \"mc2_stderr\": 0.01504184962981019\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042194,\n\ \ \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6533559051981677,\n\ \ \"acc_stderr\": 0.004749286071559569,\n \"acc_norm\": 0.8525194184425413,\n\ \ \"acc_norm_stderr\": 0.003538596773704832\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\"\ : {\n \"acc\": 0.7555555555555555,\n \"acc_stderr\": 0.03712537833614866,\n\ \ \"acc_norm\": 0.7555555555555555,\n \"acc_norm_stderr\": 0.03712537833614866\n\ \ },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.881578947368421,\n\ \ \"acc_stderr\": 0.026293995855474938,\n \"acc_norm\": 0.881578947368421,\n\ \ \"acc_norm_stderr\": 0.026293995855474938\n },\n \"harness\|hendrycksTest-business_ethics\|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n \ \ },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\":\ \ 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372277,\n \"\ acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372277\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9027777777777778,\n\ \ \"acc_stderr\": 0.024774516250440182,\n \"acc_norm\": 0.9027777777777778,\n\ \ \"acc_norm_stderr\": 0.024774516250440182\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.67,\n\ \ \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956913,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956913\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7283236994219653,\n\ \ \"acc_stderr\": 0.03391750322321659,\n \"acc_norm\": 0.7283236994219653,\n\ \ \"acc_norm_stderr\": 0.03391750322321659\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.04951218252396262,\n\ \ \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.04951218252396262\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.7872340425531915,\n \"acc_stderr\": 0.02675439134803976,\n\ \ \"acc_norm\": 0.7872340425531915,\n \"acc_norm_stderr\": 0.02675439134803976\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5789473684210527,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7724137931034483,\n \"acc_stderr\": 0.03493950380131184,\n\ \ \"acc_norm\": 0.7724137931034483,\n \"acc_norm_stderr\": 0.03493950380131184\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.753968253968254,\n \"acc_stderr\": 0.022182037202948365,\n \"\ acc_norm\": 0.753968253968254,\n \"acc_norm_stderr\": 0.022182037202948365\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.6031746031746031,\n\ \ \"acc_stderr\": 0.043758884927270585,\n \"acc_norm\": 0.6031746031746031,\n\ \ \"acc_norm_stderr\": 0.043758884927270585\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\": 0.9258064516129032,\n\ \ \"acc_stderr\": 0.01490952930054621,\n \"acc_norm\": 0.9258064516129032,\n\ \ \"acc_norm_stderr\": 0.01490952930054621\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6847290640394089,\n \"acc_stderr\": 0.03269080871970186,\n\ \ \"acc_norm\": 0.6847290640394089,\n \"acc_norm_stderr\": 0.03269080871970186\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \"acc_norm\"\ : 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.0188526702349931,\n \"acc_norm\"\ : 0.9242424242424242,\n \"acc_norm_stderr\": 0.0188526702349931\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.01934807017439698,\n \ \ \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.01934807017439698\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.4925925925925926,\n \"acc_stderr\": 0.0304821923951915,\n \ \ \"acc_norm\": 0.4925925925925926,\n \"acc_norm_stderr\": 0.0304821923951915\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8697478991596639,\n \"acc_stderr\": 0.02186325849485212,\n \ \ \"acc_norm\": 0.8697478991596639,\n \"acc_norm_stderr\": 0.02186325849485212\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.5496688741721855,\n \"acc_stderr\": 0.04062290018683775,\n \"\ acc_norm\": 0.5496688741721855,\n \"acc_norm_stderr\": 0.04062290018683775\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9302752293577982,\n \"acc_stderr\": 0.010919426411848607,\n \"\ acc_norm\": 0.9302752293577982,\n \"acc_norm_stderr\": 0.010919426411848607\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.7222222222222222,\n \"acc_stderr\": 0.0305467452649532,\n \"acc_norm\"\ : 0.7222222222222222,\n \"acc_norm_stderr\": 0.0305467452649532\n },\n\ \ \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.9215686274509803,\n\ \ \"acc_stderr\": 0.018869514646658935,\n \"acc_norm\": 0.9215686274509803,\n\ \ \"acc_norm_stderr\": 0.018869514646658935\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.919831223628692,\n \"acc_stderr\": 0.017676679991891632,\n\ \ \"acc_norm\": 0.919831223628692,\n \"acc_norm_stderr\": 0.017676679991891632\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951538,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951538\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.9338842975206612,\n \"acc_stderr\": 0.022683403691723312,\n \"\ acc_norm\": 0.9338842975206612,\n \"acc_norm_stderr\": 0.022683403691723312\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.03038159675665167,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.03038159675665167\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n\ \ \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6339285714285714,\n\ \ \"acc_stderr\": 0.04572372358737431,\n \"acc_norm\": 0.6339285714285714,\n\ \ \"acc_norm_stderr\": 0.04572372358737431\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.883495145631068,\n \"acc_stderr\": 0.031766839486404054,\n\ \ \"acc_norm\": 0.883495145631068,\n \"acc_norm_stderr\": 0.031766839486404054\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9487179487179487,\n\ \ \"acc_stderr\": 0.014450181176872736,\n \"acc_norm\": 0.9487179487179487,\n\ \ \"acc_norm_stderr\": 0.014450181176872736\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9080459770114943,\n\ \ \"acc_stderr\": 0.010333225570778518,\n \"acc_norm\": 0.9080459770114943,\n\ \ \"acc_norm_stderr\": 0.010333225570778518\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8352601156069365,\n \"acc_stderr\": 0.019971040982442265,\n\ \ \"acc_norm\": 0.8352601156069365,\n \"acc_norm_stderr\": 0.019971040982442265\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.788826815642458,\n\ \ \"acc_stderr\": 0.013650276794312199,\n \"acc_norm\": 0.788826815642458,\n\ \ \"acc_norm_stderr\": 0.013650276794312199\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8660130718954249,\n \"acc_stderr\": 0.019504890618464815,\n\ \ \"acc_norm\": 0.8660130718954249,\n \"acc_norm_stderr\": 0.019504890618464815\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8456591639871383,\n\ \ \"acc_stderr\": 0.020519050342084726,\n \"acc_norm\": 0.8456591639871383,\n\ \ \"acc_norm_stderr\": 0.020519050342084726\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8580246913580247,\n \"acc_stderr\": 0.019420260109438293,\n\ \ \"acc_norm\": 0.8580246913580247,\n \"acc_norm_stderr\": 0.019420260109438293\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6631205673758865,\n \"acc_stderr\": 0.02819553487396673,\n \ \ \"acc_norm\": 0.6631205673758865,\n \"acc_norm_stderr\": 0.02819553487396673\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6186440677966102,\n\ \ \"acc_stderr\": 0.01240550940188812,\n \"acc_norm\": 0.6186440677966102,\n\ \ \"acc_norm_stderr\": 0.01240550940188812\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8272058823529411,\n \"acc_stderr\": 0.022966067585581767,\n\ \ \"acc_norm\": 0.8272058823529411,\n \"acc_norm_stderr\": 0.022966067585581767\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8316993464052288,\n \"acc_stderr\": 0.01513580333869338,\n \ \ \"acc_norm\": 0.8316993464052288,\n \"acc_norm_stderr\": 0.01513580333869338\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8367346938775511,\n \"acc_stderr\": 0.023661699177098615,\n\ \ \"acc_norm\": 0.8367346938775511,\n \"acc_norm_stderr\": 0.023661699177098615\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8706467661691543,\n\ \ \"acc_stderr\": 0.02372983088101853,\n \"acc_norm\": 0.8706467661691543,\n\ \ \"acc_norm_stderr\": 0.02372983088101853\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.93,\n \"acc_stderr\": 0.025643239997624294,\n \ \ \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.025643239997624294\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015578,\n\ \ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015578\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5362104216200869,\n\ \ \"mc2_stderr\": 0.01504184962981019\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8216258879242304,\n \"acc_stderr\": 0.010759352014855944\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.44351781652767247,\n \ \ \"acc_stderr\": 0.013684327592606165\n }\n}\n```" repo_url: https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|arc:challenge\|25_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-10T05-55-07.023442.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|gsm8k\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hellaswag\|10_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-55-07.023442.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-10T05-55-07.023442.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '/details_harness\|winogrande\|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-10T05-55-07.023442.parquet' - config_name: results data_files: - split: 2023_12_10T05_55_07.023442 path: - results_2023-12-10T05-55-07.023442.parquet - split: latest path: - results_2023-12-10T05-55-07.023442.parquet --- # Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat ## Dataset Description - Homepage: - Repository: https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat - 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 [kyujinpy/PlatYi-34B-Llama-Q-FastChat](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat) 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_kyujinpy__PlatYi-34B-Llama-Q-FastChat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T05:55:07.023442](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat/blob/main/results_2023-12-10T05-55-07.023442.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": 0.7741514926490987, "acc_stderr": 0.027646135380835733, "acc_norm": 0.7828326159595959, "acc_norm_stderr": 0.02814394317924737, "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5362104216200869, "mc2_stderr": 0.01504184962981019 }, "harness\|arc:challenge\|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042194, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.013830568927974332 }, "harness\|hellaswag\|10": { "acc": 0.6533559051981677, "acc_stderr": 0.004749286071559569, "acc_norm": 0.8525194184425413, "acc_norm_stderr": 0.003538596773704832 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7555555555555555, "acc_stderr": 0.03712537833614866, "acc_norm": 0.7555555555555555, "acc_norm_stderr": 0.03712537833614866 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.881578947368421, "acc_stderr": 0.026293995855474938, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.026293995855474938 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8075471698113208, "acc_stderr": 0.024262979839372277, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.024262979839372277 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9027777777777778, "acc_stderr": 0.024774516250440182, "acc_norm": 0.9027777777777778, "acc_norm_stderr": 0.024774516250440182 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252606, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956913, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956913 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7283236994219653, "acc_stderr": 0.03391750322321659, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.03391750322321659 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5490196078431373, "acc_stderr": 0.04951218252396262, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.04951218252396262 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7872340425531915, "acc_stderr": 0.02675439134803976, "acc_norm": 0.7872340425531915, "acc_norm_stderr": 0.02675439134803976 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5789473684210527, "acc_stderr": 0.046446020912223177, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.046446020912223177 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7724137931034483, "acc_stderr": 0.03493950380131184, "acc_norm": 0.7724137931034483, "acc_norm_stderr": 0.03493950380131184 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.753968253968254, "acc_stderr": 0.022182037202948365, "acc_norm": 0.753968253968254, "acc_norm_stderr": 0.022182037202948365 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.6031746031746031, "acc_stderr": 0.043758884927270585, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.043758884927270585 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9258064516129032, "acc_stderr": 0.01490952930054621, "acc_norm": 0.9258064516129032, "acc_norm_stderr": 0.01490952930054621 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6847290640394089, "acc_stderr": 0.03269080871970186, "acc_norm": 0.6847290640394089, "acc_norm_stderr": 0.03269080871970186 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9242424242424242, "acc_stderr": 0.0188526702349931, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.0188526702349931 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527033, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527033 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.823076923076923, "acc_stderr": 0.01934807017439698, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.01934807017439698 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4925925925925926, "acc_stderr": 0.0304821923951915, "acc_norm": 0.4925925925925926, "acc_norm_stderr": 0.0304821923951915 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8697478991596639, "acc_stderr": 0.02186325849485212, "acc_norm": 0.8697478991596639, "acc_norm_stderr": 0.02186325849485212 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5496688741721855, "acc_stderr": 0.04062290018683775, "acc_norm": 0.5496688741721855, "acc_norm_stderr": 0.04062290018683775 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9302752293577982, "acc_stderr": 0.010919426411848607, "acc_norm": 0.9302752293577982, "acc_norm_stderr": 0.010919426411848607 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.0305467452649532, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.0305467452649532 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658935, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658935 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.919831223628692, "acc_stderr": 0.017676679991891632, "acc_norm": 0.919831223628692, "acc_norm_stderr": 0.017676679991891632 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8549618320610687, "acc_stderr": 0.03088466108951538, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.03088466108951538 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9338842975206612, "acc_stderr": 0.022683403691723312, "acc_norm": 0.9338842975206612, "acc_norm_stderr": 0.022683403691723312 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.03038159675665167, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.03038159675665167 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6339285714285714, "acc_stderr": 0.04572372358737431, "acc_norm": 0.6339285714285714, "acc_norm_stderr": 0.04572372358737431 }, "harness\|hendrycksTest-management\|5": { "acc": 0.883495145631068, "acc_stderr": 0.031766839486404054, "acc_norm": 0.883495145631068, "acc_norm_stderr": 0.031766839486404054 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9487179487179487, "acc_stderr": 0.014450181176872736, "acc_norm": 0.9487179487179487, "acc_norm_stderr": 0.014450181176872736 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9080459770114943, "acc_stderr": 0.010333225570778518, "acc_norm": 0.9080459770114943, "acc_norm_stderr": 0.010333225570778518 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8352601156069365, "acc_stderr": 0.019971040982442265, "acc_norm": 0.8352601156069365, "acc_norm_stderr": 0.019971040982442265 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.788826815642458, "acc_stderr": 0.013650276794312199, "acc_norm": 0.788826815642458, "acc_norm_stderr": 0.013650276794312199 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8660130718954249, "acc_stderr": 0.019504890618464815, "acc_norm": 0.8660130718954249, "acc_norm_stderr": 0.019504890618464815 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8456591639871383, "acc_stderr": 0.020519050342084726, "acc_norm": 0.8456591639871383, "acc_norm_stderr": 0.020519050342084726 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8580246913580247, "acc_stderr": 0.019420260109438293, "acc_norm": 0.8580246913580247, "acc_norm_stderr": 0.019420260109438293 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6631205673758865, "acc_stderr": 0.02819553487396673, "acc_norm": 0.6631205673758865, "acc_norm_stderr": 0.02819553487396673 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6186440677966102, "acc_stderr": 0.01240550940188812, "acc_norm": 0.6186440677966102, "acc_norm_stderr": 0.01240550940188812 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8272058823529411, "acc_stderr": 0.022966067585581767, "acc_norm": 0.8272058823529411, "acc_norm_stderr": 0.022966067585581767 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8316993464052288, "acc_stderr": 0.01513580333869338, "acc_norm": 0.8316993464052288, "acc_norm_stderr": 0.01513580333869338 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.04309118709946458, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.04309118709946458 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8367346938775511, "acc_stderr": 0.023661699177098615, "acc_norm": 0.8367346938775511, "acc_norm_stderr": 0.023661699177098615 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8706467661691543, "acc_stderr": 0.02372983088101853, "acc_norm": 0.8706467661691543, "acc_norm_stderr": 0.02372983088101853 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.93, "acc_stderr": 0.025643239997624294, "acc_norm": 0.93, "acc_norm_stderr": 0.025643239997624294 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015578, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015578 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5362104216200869, "mc2_stderr": 0.01504184962981019 }, "harness\|winogrande\|5": { "acc": 0.8216258879242304, "acc_stderr": 0.010759352014855944 }, "harness\|gsm8k\|5": { "acc": 0.44351781652767247, "acc_stderr": 0.013684327592606165 } } ``` ### 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]
matlok/python-copilot-training-on-ai-research-repos	--- license: - other pretty_name: >- python copilot ai research coding dataset dataset_info: - config_name: view_schema splits: - name: view_schema configs: - config_name: view_schema data_files: - split: view_schema path: files/lok-python-code-ai-core-v1_00000002.parquet size_categories: - 100K<n<1M tags: - python-copilot - python-coding - fine-tuning - training - alpaca - text - coding # supported task_categories # text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, conversational, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, other task_categories: - text-generation # supported task_ids # acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering task_ids: - parsing --- ## Python Copilot AI Research Coding Dataset This dataset is a subset of the matlok python copilot datasets. Please refer to the [Multimodal Python Copilot Training Overview](https://huggingface.co/datasets/matlok/multimodal-python-copilot-training-overview) for more details on how to use this dataset. ### Details Each row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the code), and more. - Rows: 514430 - Size: 674 MB - Data type: text - Format: Extracted code using python AST ### Schema ```json { "args": "string", "class_bases": "string", "class_docstr": "string", "class_docstr_tok": "string", "class_name": "string", "code": "string", "code_tok": "string", "docstr": "string", "docstr_tok": "string", "file_path": "string", "filename": "string", "imports": "string", "is_member": "bool", "label_desc": "string", "label_desc_len": "int64", "label_id": "string", "lend": "int64", "lstart": "int64", "name": "string", "num_all_bases": "float64", "num_bases": "float64", "num_classes": "float64", "num_functions": "int64", "num_imports": "int64", "num_methods": "float64", "raises": "string", "returns": "string", "total_objects": "int64" } ``` ### How to use the dataset ```python from datasets import load_dataset ds = load_dataset("matlok/python-copilot-training-on-ai-research-repos", data_dir="files") ```
Falah/real_military_machinery_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 39077069 num_examples: 100000 download_size: 4335632 dataset_size: 39077069 --- # Dataset Card for "real_military_machinery_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
umm-maybe/ai_images	--- 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': train_dataset - name: text dtype: string splits: - name: train num_bytes: 540439882.0 num_examples: 304 download_size: 540208895 dataset_size: 540439882.0 --- # Dataset Card for "ai_images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/uomi_chihiro_seitokaiyakuindomo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Uomi Chihiro (Seitokai Yakuindomo) This is the dataset of Uomi Chihiro (Seitokai Yakuindomo), containing 208 images and their tags. The core tags of this character are `black_hair, twintails, long_hair, low_twintails, black_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:---------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 208 \| 93.88 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 208 \| 80.86 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 421 \| 158.36 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 208 \| 93.80 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 421 \| 177.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/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/uomi_chihiro_seitokaiyakuindomo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, school_uniform, solo, upper_body, cardigan, purple_eyes, white_shirt, smile, closed_mouth, red_necktie, collared_shirt, jacket, bangs \| \| 1 \| 21 \| ![](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, necktie, school_uniform, solo, cardigan, skirt \| \| 2 \| 5 \| ![](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, profile, school_uniform, solo, collared_shirt, from_side, blazer, closed_mouth, outdoors, red_necktie, upper_body, white_shirt, bangs, blue_eyes, blurry_background, depth_of_field \| \| 3 \| 13 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, solo, twin_braids, anime_coloring, hair_over_shoulder, upper_body \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| school_uniform \| solo \| upper_body \| cardigan \| purple_eyes \| white_shirt \| smile \| closed_mouth \| red_necktie \| collared_shirt \| jacket \| bangs \| necktie \| skirt \| profile \| from_side \| blazer \| outdoors \| blue_eyes \| blurry_background \| depth_of_field \| twin_braids \| anime_coloring \| hair_over_shoulder \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:-------\|:-------------\|:-----------\|:--------------\|:--------------\|:--------\|:---------------\|:--------------\|:-----------------\|:---------\|:--------\|:----------\|:--------\|:----------\|:------------\|:---------\|:-----------\|:------------\|:--------------------\|:-----------------\|:--------------\|:-----------------\|:---------------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 21 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| \| \| X \| \| X \| X \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| 3 \| 13 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \|
Beluuuuuuga/Japanese-Instruction-Linux-Command-169	--- license: cc-by-nc-4.0 task_categories: - question-answering language: - ja size_categories: - n<1K ---
argilla/news-fakenews	--- language: - en size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification dataset_info: features: - name: text dtype: string - name: inputs struct: - name: text dtype: string - name: prediction list: - name: label dtype: string - name: score dtype: float64 - name: prediction_agent dtype: string - name: annotation dtype: 'null' - name: annotation_agent dtype: 'null' - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 227222498 num_examples: 44898 download_size: 138350597 dataset_size: 227222498 --- # Dataset Card for "news-fakenews" ## Dataset Description - Homepage: Kaggle Challenge - Repository: https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset?select=True.csv - Paper: N.A. - Leaderboard: N.A. - Point of Contact: N.A. ### Dataset Summary Can you use this data set to make an algorithm able to determine if an article is fake news or not ? ### Languages english ### Citation Information Acknowledgements Ahmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018. Ahmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138). ### Contributions Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.
roman_urdu_hate_speech	--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - ur license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: roman_urdu_hate_speech tags: - binary classification dataset_info: - config_name: Coarse_Grained features: - name: tweet dtype: string - name: label dtype: class_label: names: '0': Abusive/Offensive '1': Normal splits: - name: train num_bytes: 725719 num_examples: 7208 - name: test num_bytes: 218087 num_examples: 2002 - name: validation num_bytes: 79759 num_examples: 800 download_size: 927937 dataset_size: 1023565 - config_name: Fine_Grained features: - name: tweet dtype: string - name: label dtype: class_label: names: '0': Abusive/Offensive '1': Normal '2': Religious Hate '3': Sexism '4': Profane/Untargeted splits: - name: train num_bytes: 723670 num_examples: 7208 - name: test num_bytes: 219359 num_examples: 2002 - name: validation num_bytes: 723670 num_examples: 7208 download_size: 1519423 dataset_size: 1666699 --- # Dataset Card for roman_urdu_hate_speech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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: [roman_urdu_hate_speech homepage](https://aclanthology.org/2020.emnlp-main.197/) - Repository: [roman_urdu_hate_speech repository](https://github.com/haroonshakeel/roman_urdu_hate_speech) - Paper: [Hate-Speech and Offensive Language Detection in Roman Urdu](https://aclanthology.org/2020.emnlp-main.197.pdf) - Leaderboard: [N/A] - Point of Contact: [M. Haroon Shakeel](mailto:m.shakeel@lums.edu.pk) ### Dataset Summary The Roman Urdu Hate-Speech and Offensive Language Detection (RUHSOLD) dataset is a Roman Urdu dataset of tweets annotated by experts in the relevant language. The authors develop the gold-standard for two sub-tasks. First sub-task is based on binary labels of Hate-Offensive content and Normal content (i.e., inoffensive language). These labels are self-explanatory. The authors refer to this sub-task as coarse-grained classification. Second sub-task defines Hate-Offensive content with four labels at a granular level. These labels are the most relevant for the demographic of users who converse in RU and are defined in related literature. The authors refer to this sub-task as fine-grained classification. The objective behind creating two gold-standards is to enable the researchers to evaluate the hate speech detection approaches on both easier (coarse-grained) and challenging (fine-grained) scenarios. ### Supported Tasks and Leaderboards - 'multi-class-classification', 'text-classification-other-binary classification': The dataset can be used for both multi class classification as well as for binary classification as it contains both coarse grained and fine grained labels. ### Languages The text of this dataset is Roman Urdu. The associated BCP-47 code is 'ur'. ## Dataset Structure ### Data Instances The dataset consists of two parts divided as a set of two types, Coarse grained examples and Fine Grained examples. The difference is that in the coarse grained example the tweets are labelled as abusive or normal whereas in the fine grained version there are several classes of hate associated with a tweet. For the Coarse grained segment of the dataset the label mapping is:- Task 1: Coarse-grained Classification Labels 0: Abusive/Offensive 1: Normal Whereas for the Fine Grained segment of the dataset the label mapping is:- Task 2: Fine-grained Classification Labels 0: Abusive/Offensive 1: Normal 2: Religious Hate 3: Sexism 4: Profane/Untargeted An example from Roman Urdu Hate Speech looks as follows: ``` { 'tweet': 'there are some yahodi daboo like imran chore zakat khore' 'label': 0 } ``` ### Data Fields -tweet:a string denoting the tweet which has been selected by using a random sampling from a tweet base of 50000 tweets to select 10000 tweets and annotated for the dataset. -label:An annotation manually labeled by three independent annotators, during the annotation process, all conflicts are resolved by a majority vote among three annotators. ### Data Splits The data of each of the segments, Coarse Grained and Fine Grained is further split into training, validation and test set. The data is split in train, test, and validation sets with 70,20,10 split ratio using stratification based on fine-grained labels. The use of stratified sampling is deemed necessary to preserve the same labels ratio across all splits. The Final split sizes are as follows: Train Valid Test 7209 2003 801 ## 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 The dataset was created by Hammad Rizwan, Muhammad Haroon Shakeel, Asim Karim during work done at Department of Computer Science, Lahore University of Management Sciences (LUMS), Lahore, Pakistan. ### Licensing Information The licensing status of the dataset hinges on the legal status of the [Roman Urdu Hate Speech Dataset Repository](https://github.com/haroonshakeel/roman_urdu_hate_speech) which is under MIT License. ### Citation Information ```bibtex @inproceedings{rizwan2020hate, title={Hate-speech and offensive language detection in roman Urdu}, author={Rizwan, Hammad and Shakeel, Muhammad Haroon and Karim, Asim}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, pages={2512--2522}, year={2020} } ``` ### Contributions Thanks to [@bp-high](https://github.com/bp-high), for adding this dataset.
emaeon/train7	--- dataset_info: features: - name: code1 dtype: string - name: code2 dtype: string - name: similar dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 9013855023 num_examples: 5000000 download_size: 4017642295 dataset_size: 9013855023 --- # Dataset Card for "train7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nexdata/British_English_Average_Tone_Speech_Synthesis_Corpus	--- task_categories: - text-to-speech language: - en --- # Dataset Card for Nexdata/British_English_Average_Tone_Speech_Synthesis_Corpus ## Description 10 People - British English Average Tone Speech Synthesis Corpus. It is recorded by British English native speakers, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis. For more details, please refer to the link: https://www.nexdata.ai/datasets/1309?source=Huggingface # Specifications ## Format 48,000Hz, 24bit, uncompressed wav, mono channel; ## Recording environment professional recording studio; ## Recording content general narrative sentences, interrogative sentences, etc; ## Speaker british native speaker, 5 male and 5 female, 2 hours per person; ## Device microphone; ## Language British English; ## Annotation word and phoneme transcription, four-level prosodic boundary annotation; ## Application scenarios speech synthesis. # Licensing Information Commercial License
irds/wikir_en59k	--- pretty_name: '`wikir/en59k`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `wikir/en59k` The `wikir/en59k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/wikir#wikir/en59k). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=2,454,785 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/wikir_en59k', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Frej2020Wikir, title={WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={LREC}, year={2020} } @inproceedings{Frej2020MlWikir, title={MLWIKIR: A Python Toolkit for Building Large-scale Wikipedia-based Information Retrieval Datasets in Chinese, English, French, Italian, Japanese, Spanish and More}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={CIRCLE}, year={2020} } ```
aarnow/auditory-skills-test	--- language: - en license: mit dataset_info: features: - name: label dtype: string - name: text dtype: string splits: - name: train num_bytes: 16536 num_examples: 178 download_size: 7410 dataset_size: 16536 configs: - config_name: default data_files: - split: train path: data/train-* --- The purpose of this project is to build a dataset and model to enable an AI powered diagnostic tool that assesses a child's auditory skills and recommends resources and therapies that can bring them to the next stage. The primary user base of this tool is intended to be the parents of a child with hearing loss however it is the hope of the creators of this tool that speech and language pathologists (SLPs) and other early intervention and pediatric practitioners can find use. The model uses a natural language processing (NLP) model for text-classification and converts free text inputted by the parent of a child with hearing loss into 1 of 4 clinical categories: DETECTION, DISCRIMINATION, IDENTIFICATION, CLASSIFICATION. Based on the classification of the child against a given skill a recommendation is made for therapies that can be used to improve the child's competency against a given skill. The value of this approach is that each child is challenged to build upon existing skills while not being given any task too difficult that will result in discouragement.
Ga88/Clovis8	--- license: openrail ---
reglab/land-app-trial	--- license: cc-by-4.0 task_categories: - object-detection language: - en tags: - agriculture - environment size_categories: - 1K<n<10K --- # Land application field trial data ### Intro This dataset is a repository of results from our Land Application Detection Model trial with two organizations. Land application is the process of disposing of agricultural animal waste by spraying it onto fields. [We developed a model](https://github.com/reglab/land-application-detection?tab=readme-ov-file) to detect these practices. This dataset represents the results of a real world trial to verify and label these detected spreads. ### Data description #### Structured data - sent_to_wdnr.csv - Each row is a detected spread that we forwarded to our partners at WDNR - sent_to_elpc.csv - Each row is a detected spread that we forwarded to our partners at ELPC - wdnr_responses.csv - Each row is a response to a detection from sent_to_wdnr.csv which contains a preliminary determination by WDNR staff as to whether the image looks like a spread and if it was determined to be likely spreading, the results of an investigation into said spread. - elpc_responses_raw.csv - Each row is a response to a detection from sent_to_elpc.csv which is the results of the ELPC investigation into that detection through the use of citizen volunteers verifiying in person. - elpc_responses_clean.csv - Same as the raw file but with corrected detection ids to deal with a data entry error. #### Image data - images/ - This directory contains .jpeg images of satellite data fed into the model that were sent to either of the partners. Images were captured by [Planet](https://www.planet.com/) using the PlanetScope sensor, visual spectrum 3m images. ## Citation `@misc {stanford_regulation,_evaluation,_and_governance_lab_2024, author = { {Stanford Regulation, Evaluation, and Governance Lab} }, title = { land-app-trial (Revision b3d0e11) }, year = 2024, url = { https://huggingface.co/datasets/reglab/land-app-trial }, doi = { 10.57967/hf/1733 }, publisher = { Hugging Face } }`
zolak/twitter_dataset_79_1713209820	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 1387710 num_examples: 3432 download_size: 680273 dataset_size: 1387710 configs: - config_name: default data_files: - split: train path: data/train-* ---
distilled-one-sec-cv12-each-chunk-uniq/chunk_16	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1243273188.0 num_examples: 242259 download_size: 1270215878 dataset_size: 1243273188.0 --- # Dataset Card for "chunk_16" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
distilled-from-one-sec-cv12/chunk_140	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1147381768 num_examples: 223574 download_size: 1170376996 dataset_size: 1147381768 --- # Dataset Card for "chunk_140" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
roydcarlson/dirt_teff2	--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 6436424.0 num_examples: 7 download_size: 6352411 dataset_size: 6436424.0 --- # Dataset Card for "dirt_teff2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tollefj/big-bang-theory-splits-removal	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: en dtype: string - name: 'no' dtype: string splits: - name: train num_bytes: 8082 num_examples: 60 - name: test num_bytes: 3499 num_examples: 27 download_size: 12705 dataset_size: 11581 --- # Dataset Card for "big-bang-theory-splits-removal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
irds/mr-tydi_th_test	--- pretty_name: '`mr-tydi/th/test`' viewer: false source_datasets: ['irds/mr-tydi_th'] task_categories: - text-retrieval --- # Dataset Card for `mr-tydi/th/test` The `mr-tydi/th/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/th/test). # Data This dataset provides: - `queries` (i.e., topics); count=1,190 - `qrels`: (relevance assessments); count=1,368 - For `docs`, use [`irds/mr-tydi_th`](https://huggingface.co/datasets/irds/mr-tydi_th) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/mr-tydi_th_test', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/mr-tydi_th_test', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Zhang2021MrTyDi, title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin}, year={2021}, journal={arXiv:2108.08787}, } @article{Clark2020TyDiQa, title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages}, author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}, year={2020}, journal={Transactions of the Association for Computational Linguistics} } ```
LambdaTests/VQAv2_sample_validation_benchmarks_partition_global_7_loca_7	--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 13 num_examples: 1 download_size: 0 dataset_size: 13 --- # Dataset Card for "VQAv2_sample_validation_benchmarks_partition_global_7_loca_7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/1cc7040b	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 178 num_examples: 10 download_size: 1340 dataset_size: 178 --- # Dataset Card for "1cc7040b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SahilSN/DataSet_v3	--- license: unknown dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11291 num_examples: 50 download_size: 6939 dataset_size: 11291 configs: - config_name: default data_files: - split: train path: data/train-* ---
banloada/ban	--- license: other ---
OzoneAsai/factorExpander	--- license: wtfpl task_categories: - conversational language: - ja --- # Polynomial Expansion and Factoring Dataset This dataset contains problem and solution pairs for polynomial expansion and factoring. Each problem is a result of expanding and factoring the `(x + n)^2` form expression, where `n` takes values from -1000 to 1000. ## Dataset Structure - `factorized_dataset.csv`: CSV file containing the dataset. - `README.md`: This file that provides an overview and usage instructions for the dataset. ## Data Format The CSV file of the dataset includes the following columns: - `Instruction`: A description of the problem. It indicates the expression that should be expanded or factored. - `Output`: The answer expression. ## Sample Data \| Instruction \| Output \| \|-----------------------------------\|--------------------\| \| 式を展開せよ: (x - 1000)2 \| x2 - 2000x + 1000000 \| ... ## Dataset Information - Number of samples: 2002 (due to the range of n from -1000 to 1000) - Dataset format: CSV file # 数式の展開と因数分解データセットこのデータセットは、数式の展開と因数分解の問題と解答のペアを含んでいます。各問題は、`sympy`ライブラリを使用して、`(x + n)^2`形式の数式を展開し、因数分解した結果です。 ## データセットの構造 - `factorized_dataset.csv`: データセットが格納されたCSVファイルです。 - `README.md`: このファイルで、データセットの概要と使用方法を説明します。 ## データのフォーマットデータセットのCSVファイルには、以下のカラムが含まれています： - `Instruction`: 問題の説明文です。展開または因数分解を行うべき数式が記載されています。 - `Output`: 解答の数式が記載されています。 ## データのサンプル \| Instruction \| Output \| \|-----------------------------------\|--------------------\| \| 式を展開せよ: (x - 1000)2 \| x2 - 2000x + 1000000 \| ... ## データセットの情報 - データのサンプル数: 2002 (nの値を-1000から1000まで総当たりしたため) - データセットのフォーマット: CSVファイル
ingTikna/Prolog_Dataset	--- license: mit ---
SF-Corpus/EF_Supersense_Tags	--- language: - en pretty_name: sf-nexus-ef-supsersense-tags --- # Dataset Card for SF Nexus Extracted Features: Named Entities ## Dataset Description - Homepage: https://sfnexus.io/ - Repository: https://github.com/SF-Nexus/extracted-features-notebooks - Point of Contact: Alex Wermer-Colan ### Dataset Summary The SF Nexus EF Supersense Tags dataset contains supersense tags generated from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection. After digitization, the books were cleaned using Abbyy FineReader. The dataframes in this repository were generated using BookNLP and contain information about the "supersense tags" in the texts. ### About the SF Nexus Corpus The Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980). The digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's [Temple page](https://babel.hathitrust.org/cgi/ls?field1=ocr;q1=%2A;a=srchls;facet=htsource%3A%22Temple%20University%22;pn=4) for non-consumptive research. For more information on the project to digitize and curate a corpus of "New Wave" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, ["Building a New Wave Science Fiction Corpus."](https://sites.temple.edu/tudsc/2017/12/20/building-new-wave-science-fiction-corpus/). ### Languages English ## Dataset Structure This dataset contains 403 csv files containing information about the named entities in each text in the SF corpus. For example: ``` First line of dataframe: 1908_HODGSON_THEHOUSEONTHEBORDERLAND.txt.supersense.csv {'start_token': 4, 'end_token': 4 'supersense_category': noun.location 'text': 'Borderland', } ``` ### Data Fields - start_token: int The start token of entity name - end_token: int The end token of the entity name; same as the start token for one-word entites; increase by one for each additional word that is part of the token - supersense_category: str The part of speech and category to which the text belongs - text: str The text corresponding to the supersense tag ### Loading the Dataset Use the following code to load the dataset in a Python environment (note: does not work with repo set to private) ``` from datasets import load_dataset # If the dataset is gated/private, make sure you have run huggingface-cli login dataset = load_dataset("SF-Corpus/EF_Supersense_Tags") ``` Or just clone the dataset repo ``` git lfs install git clone https://huggingface.co/datasets/SF-Corpus/EF_Supersense_Tags # if you want to clone without large files – just their pointers # prepend your git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1 ``` ## Dataset Creation ### Curation Rationale For an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022) ### Source Data The Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well. ## Considerations for Using the Data This data card only exhibits extracted features for copyrighted fiction; no copyrighted work is being made available for consumption. These digitized files are made accessible for purposes of education and research. Temple University Libraries have given attribution to rights holders when possible. If you hold the rights to materials in our digitized collections that are unattributed, please let us know so that we may maintain accurate information about these materials. If you are a rights holder and are concerned that you have found material on this website for which you have not granted permission (or is not covered by a copyright exception under US copyright laws), you may request the removal of the material from our site by writing to digitalscholarship@temple.edu. For more information on non-consumptive research, check out HathiTrust Research Center’s Non-Consumptive Use Research Policy. ## Additional Information ### Dataset Curators For a full list of conributors to the SF Nexus project, visit [https://sfnexus.io/people/](https://sfnexus.io/people/).
carnival13/massive_eng_DA_tokenized	--- dataset_info: features: - name: pass_label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 97244320 num_examples: 138200 download_size: 22020759 dataset_size: 97244320 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "massive_eng_DA_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xl_mode_A_ns_6149	--- 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: 2470439 num_examples: 6149 download_size: 269782 dataset_size: 2470439 --- # Dataset Card for "OxfordFlowers_test_google_flan_t5_xl_mode_A_ns_6149" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Vikhrmodels/habr_qa_sbs	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: best dtype: string - name: bad dtype: string splits: - name: train num_bytes: 119263751 num_examples: 102558 download_size: 66726288 dataset_size: 119263751 license: apache-2.0 task_categories: - question-answering - text-generation language: - ru tags: - code - finance pretty_name: habr_qa_sbs size_categories: - 10K<n<100K --- # Habr sbs qa Датасет основан на сайте habr qa, лучший ответ - тот на котором есть лайки, худший - тот на котором меньше всего лайков. Датасет собран [Love.Death.Transformers.](https://t.me/lovedeathtransformers) и [Дата-Утренник](https://t.me/data_morning) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_wnli_indef_one	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 5997 num_examples: 29 - name: test num_bytes: 27482 num_examples: 94 - name: train num_bytes: 40847 num_examples: 195 download_size: 32547 dataset_size: 74326 --- # Dataset Card for "MULTI_VALUE_wnli_indef_one" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2	--- pretty_name: Evaluation run of Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2](https://huggingface.co/Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-04T20:26:53.463273](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2/blob/main/results_2024-02-04T20-26-53.463273.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\": 0.2605954222902013,\n\ \ \"acc_stderr\": 0.030887287206153434,\n \"acc_norm\": 0.2609822344299048,\n\ \ \"acc_norm_stderr\": 0.031636108991043924,\n \"mc1\": 0.22643818849449204,\n\ \ \"mc1_stderr\": 0.014651337324602574,\n \"mc2\": 0.372644846918848,\n\ \ \"mc2_stderr\": 0.014009270688888235\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.29436860068259385,\n \"acc_stderr\": 0.013318528460539422,\n\ \ \"acc_norm\": 0.32764505119453924,\n \"acc_norm_stderr\": 0.013715847940719346\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4347739494124676,\n\ \ \"acc_stderr\": 0.004947141797384123,\n \"acc_norm\": 0.5791674965146385,\n\ \ \"acc_norm_stderr\": 0.004926837572202166\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\": 0.24444444444444444,\n\ \ \"acc_stderr\": 0.03712537833614866,\n \"acc_norm\": 0.24444444444444444,\n\ \ \"acc_norm_stderr\": 0.03712537833614866\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.03317672787533157,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.03317672787533157\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.33,\n\ \ \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \ \ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.2679245283018868,\n \"acc_stderr\": 0.027257260322494845,\n\ \ \"acc_norm\": 0.2679245283018868,\n \"acc_norm_stderr\": 0.027257260322494845\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2152777777777778,\n\ \ \"acc_stderr\": 0.03437079344106134,\n \"acc_norm\": 0.2152777777777778,\n\ \ \"acc_norm_stderr\": 0.03437079344106134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\"\ : 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.23699421965317918,\n\ \ \"acc_stderr\": 0.03242414757483099,\n \"acc_norm\": 0.23699421965317918,\n\ \ \"acc_norm_stderr\": 0.03242414757483099\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.3191489361702128,\n \"acc_stderr\": 0.030472973363380045,\n\ \ \"acc_norm\": 0.3191489361702128,\n \"acc_norm_stderr\": 0.030472973363380045\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2543859649122807,\n\ \ \"acc_stderr\": 0.04096985139843672,\n \"acc_norm\": 0.2543859649122807,\n\ \ \"acc_norm_stderr\": 0.04096985139843672\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n\ \ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"\ acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.20634920634920634,\n\ \ \"acc_stderr\": 0.036196045241242515,\n \"acc_norm\": 0.20634920634920634,\n\ \ \"acc_norm_stderr\": 0.036196045241242515\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.24838709677419354,\n\ \ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.24838709677419354,\n\ \ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.2660098522167488,\n \"acc_stderr\": 0.03108982600293753,\n\ \ \"acc_norm\": 0.2660098522167488,\n \"acc_norm_stderr\": 0.03108982600293753\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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_european_history\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.21717171717171718,\n \"acc_stderr\": 0.029376616484945637,\n \"\ acc_norm\": 0.21717171717171718,\n \"acc_norm_stderr\": 0.029376616484945637\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.24870466321243523,\n \"acc_stderr\": 0.03119584087770031,\n\ \ \"acc_norm\": 0.24870466321243523,\n \"acc_norm_stderr\": 0.03119584087770031\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.2846153846153846,\n \"acc_stderr\": 0.022878322799706287,\n\ \ \"acc_norm\": 0.2846153846153846,\n \"acc_norm_stderr\": 0.022878322799706287\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275805,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275805\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.24789915966386555,\n \"acc_stderr\": 0.028047967224176896,\n\ \ \"acc_norm\": 0.24789915966386555,\n \"acc_norm_stderr\": 0.028047967224176896\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2052980132450331,\n \"acc_stderr\": 0.03297986648473834,\n \"\ acc_norm\": 0.2052980132450331,\n \"acc_norm_stderr\": 0.03297986648473834\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.23302752293577983,\n \"acc_stderr\": 0.0181256691808615,\n \"\ acc_norm\": 0.23302752293577983,\n \"acc_norm_stderr\": 0.0181256691808615\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.3425925925925926,\n \"acc_stderr\": 0.03236585252602158,\n \"\ acc_norm\": 0.3425925925925926,\n \"acc_norm_stderr\": 0.03236585252602158\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.2911392405063291,\n \"acc_stderr\": 0.029571601065753374,\n\ \ \"acc_norm\": 0.2911392405063291,\n \"acc_norm_stderr\": 0.029571601065753374\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.37668161434977576,\n\ \ \"acc_stderr\": 0.032521134899291884,\n \"acc_norm\": 0.37668161434977576,\n\ \ \"acc_norm_stderr\": 0.032521134899291884\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.21374045801526717,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.21374045801526717,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.23140495867768596,\n \"acc_stderr\": 0.03849856098794088,\n \"\ acc_norm\": 0.23140495867768596,\n \"acc_norm_stderr\": 0.03849856098794088\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.23148148148148148,\n\ \ \"acc_stderr\": 0.04077494709252628,\n \"acc_norm\": 0.23148148148148148,\n\ \ \"acc_norm_stderr\": 0.04077494709252628\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.3312883435582822,\n \"acc_stderr\": 0.03697983910025588,\n\ \ \"acc_norm\": 0.3312883435582822,\n \"acc_norm_stderr\": 0.03697983910025588\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.22321428571428573,\n\ \ \"acc_stderr\": 0.039523019677025116,\n \"acc_norm\": 0.22321428571428573,\n\ \ \"acc_norm_stderr\": 0.039523019677025116\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n\ \ \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.24358974358974358,\n\ \ \"acc_stderr\": 0.02812096650391441,\n \"acc_norm\": 0.24358974358974358,\n\ \ \"acc_norm_stderr\": 0.02812096650391441\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.2835249042145594,\n\ \ \"acc_stderr\": 0.016117318166832283,\n \"acc_norm\": 0.2835249042145594,\n\ \ \"acc_norm_stderr\": 0.016117318166832283\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n\ \ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.24183006535947713,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.24183006535947713,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2861736334405145,\n\ \ \"acc_stderr\": 0.025670259242188947,\n \"acc_norm\": 0.2861736334405145,\n\ \ \"acc_norm_stderr\": 0.025670259242188947\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.2654320987654321,\n \"acc_stderr\": 0.024569223600460845,\n\ \ \"acc_norm\": 0.2654320987654321,\n \"acc_norm_stderr\": 0.024569223600460845\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.23049645390070922,\n \"acc_stderr\": 0.025123739226872395,\n \ \ \"acc_norm\": 0.23049645390070922,\n \"acc_norm_stderr\": 0.025123739226872395\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2438070404172099,\n\ \ \"acc_stderr\": 0.010966507972178475,\n \"acc_norm\": 0.2438070404172099,\n\ \ \"acc_norm_stderr\": 0.010966507972178475\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.025767252010855963,\n\ \ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.025767252010855963\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness\|hendrycksTest-public_relations\|5\"\ : {\n \"acc\": 0.32727272727272727,\n \"acc_stderr\": 0.044942908662520896,\n\ \ \"acc_norm\": 0.32727272727272727,\n \"acc_norm_stderr\": 0.044942908662520896\n\ \ },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.17959183673469387,\n\ \ \"acc_stderr\": 0.024573293589585637,\n \"acc_norm\": 0.17959183673469387,\n\ \ \"acc_norm_stderr\": 0.024573293589585637\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.21393034825870647,\n \"acc_stderr\": 0.028996909693328927,\n\ \ \"acc_norm\": 0.21393034825870647,\n \"acc_norm_stderr\": 0.028996909693328927\n\ \ },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness\|hendrycksTest-virology\|5\"\ : {\n \"acc\": 0.3253012048192771,\n \"acc_stderr\": 0.03647168523683227,\n\ \ \"acc_norm\": 0.3253012048192771,\n \"acc_norm_stderr\": 0.03647168523683227\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.21052631578947367,\n\ \ \"acc_stderr\": 0.031267817146631786,\n \"acc_norm\": 0.21052631578947367,\n\ \ \"acc_norm_stderr\": 0.031267817146631786\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 0.22643818849449204,\n \"mc1_stderr\": 0.014651337324602574,\n\ \ \"mc2\": 0.372644846918848,\n \"mc2_stderr\": 0.014009270688888235\n\ \ },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.6479873717442778,\n\ \ \"acc_stderr\": 0.013422874824929714\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.028051554207733132,\n \"acc_stderr\": 0.004548229533836337\n\ \ }\n}\n```" repo_url: https://huggingface.co/Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2 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_04T18_17_11.697806 path: - '/details_harness\|arc:challenge\|25_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|arc:challenge\|25_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|arc:challenge\|25_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-04T20-26-53.463273.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|gsm8k\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|gsm8k\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|gsm8k\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hellaswag\|10_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hellaswag\|10_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hellaswag\|10_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T18-17-11.697806.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T18-52-11.664162.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T20-26-53.463273.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-04T20-26-53.463273.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '/details_harness\|winogrande\|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '/details_harness\|winogrande\|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '/details_harness\|winogrande\|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-04T20-26-53.463273.parquet' - config_name: results data_files: - split: 2024_02_04T18_17_11.697806 path: - results_2024-02-04T18-17-11.697806.parquet - split: 2024_02_04T18_52_11.664162 path: - results_2024-02-04T18-52-11.664162.parquet - split: 2024_02_04T20_26_53.463273 path: - results_2024-02-04T20-26-53.463273.parquet - split: latest path: - results_2024-02-04T20-26-53.463273.parquet --- # Dataset Card for Evaluation run of Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2](https://huggingface.co/Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-04T20:26:53.463273](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2/blob/main/results_2024-02-04T20-26-53.463273.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": 0.2605954222902013, "acc_stderr": 0.030887287206153434, "acc_norm": 0.2609822344299048, "acc_norm_stderr": 0.031636108991043924, "mc1": 0.22643818849449204, "mc1_stderr": 0.014651337324602574, "mc2": 0.372644846918848, "mc2_stderr": 0.014009270688888235 }, "harness\|arc:challenge\|25": { "acc": 0.29436860068259385, "acc_stderr": 0.013318528460539422, "acc_norm": 0.32764505119453924, "acc_norm_stderr": 0.013715847940719346 }, "harness\|hellaswag\|10": { "acc": 0.4347739494124676, "acc_stderr": 0.004947141797384123, "acc_norm": 0.5791674965146385, "acc_norm_stderr": 0.004926837572202166 }, "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": 0.24444444444444444, "acc_stderr": 0.03712537833614866, "acc_norm": 0.24444444444444444, "acc_norm_stderr": 0.03712537833614866 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03317672787533157, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03317672787533157 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2679245283018868, "acc_stderr": 0.027257260322494845, "acc_norm": 0.2679245283018868, "acc_norm_stderr": 0.027257260322494845 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2152777777777778, "acc_stderr": 0.03437079344106134, "acc_norm": 0.2152777777777778, "acc_norm_stderr": 0.03437079344106134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.23699421965317918, "acc_stderr": 0.03242414757483099, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483099 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179961, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179961 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.3191489361702128, "acc_stderr": 0.030472973363380045, "acc_norm": 0.3191489361702128, "acc_norm_stderr": 0.030472973363380045 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2543859649122807, "acc_stderr": 0.04096985139843672, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.04096985139843672 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2689655172413793, "acc_stderr": 0.03695183311650232, "acc_norm": 0.2689655172413793, "acc_norm_stderr": 0.03695183311650232 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2566137566137566, "acc_stderr": 0.022494510767503154, "acc_norm": 0.2566137566137566, "acc_norm_stderr": 0.022494510767503154 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.20634920634920634, "acc_stderr": 0.036196045241242515, "acc_norm": 0.20634920634920634, "acc_norm_stderr": 0.036196045241242515 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.18, "acc_stderr": 0.03861229196653695, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653695 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.24838709677419354, "acc_stderr": 0.024580028921481003, "acc_norm": 0.24838709677419354, "acc_norm_stderr": 0.024580028921481003 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2660098522167488, "acc_stderr": 0.03108982600293753, "acc_norm": 0.2660098522167488, "acc_norm_stderr": 0.03108982600293753 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.21717171717171718, "acc_stderr": 0.029376616484945637, "acc_norm": 0.21717171717171718, "acc_norm_stderr": 0.029376616484945637 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.24870466321243523, "acc_stderr": 0.03119584087770031, "acc_norm": 0.24870466321243523, "acc_norm_stderr": 0.03119584087770031 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2846153846153846, "acc_stderr": 0.022878322799706287, "acc_norm": 0.2846153846153846, "acc_norm_stderr": 0.022878322799706287 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275805, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275805 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.24789915966386555, "acc_stderr": 0.028047967224176896, "acc_norm": 0.24789915966386555, "acc_norm_stderr": 0.028047967224176896 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2052980132450331, "acc_stderr": 0.03297986648473834, "acc_norm": 0.2052980132450331, "acc_norm_stderr": 0.03297986648473834 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.23302752293577983, "acc_stderr": 0.0181256691808615, "acc_norm": 0.23302752293577983, "acc_norm_stderr": 0.0181256691808615 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.3425925925925926, "acc_stderr": 0.03236585252602158, "acc_norm": 0.3425925925925926, "acc_norm_stderr": 0.03236585252602158 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.2911392405063291, "acc_stderr": 0.029571601065753374, "acc_norm": 0.2911392405063291, "acc_norm_stderr": 0.029571601065753374 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.37668161434977576, "acc_stderr": 0.032521134899291884, "acc_norm": 0.37668161434977576, "acc_norm_stderr": 0.032521134899291884 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.21374045801526717, "acc_stderr": 0.0359546161177469, "acc_norm": 0.21374045801526717, "acc_norm_stderr": 0.0359546161177469 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.23140495867768596, "acc_stderr": 0.03849856098794088, "acc_norm": 0.23140495867768596, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.23148148148148148, "acc_stderr": 0.04077494709252628, "acc_norm": 0.23148148148148148, "acc_norm_stderr": 0.04077494709252628 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.3312883435582822, "acc_stderr": 0.03697983910025588, "acc_norm": 0.3312883435582822, "acc_norm_stderr": 0.03697983910025588 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.22321428571428573, "acc_stderr": 0.039523019677025116, "acc_norm": 0.22321428571428573, "acc_norm_stderr": 0.039523019677025116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.2524271844660194, "acc_stderr": 0.04301250399690877, "acc_norm": 0.2524271844660194, "acc_norm_stderr": 0.04301250399690877 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.24358974358974358, "acc_stderr": 0.02812096650391441, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.02812096650391441 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2835249042145594, "acc_stderr": 0.016117318166832283, "acc_norm": 0.2835249042145594, "acc_norm_stderr": 0.016117318166832283 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24566473988439305, "acc_stderr": 0.02317629820399201, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.02317629820399201 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.24183006535947713, "acc_stderr": 0.024518195641879334, "acc_norm": 0.24183006535947713, "acc_norm_stderr": 0.024518195641879334 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2861736334405145, "acc_stderr": 0.025670259242188947, "acc_norm": 0.2861736334405145, "acc_norm_stderr": 0.025670259242188947 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2654320987654321, "acc_stderr": 0.024569223600460845, "acc_norm": 0.2654320987654321, "acc_norm_stderr": 0.024569223600460845 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.23049645390070922, "acc_stderr": 0.025123739226872395, "acc_norm": 0.23049645390070922, "acc_norm_stderr": 0.025123739226872395 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2438070404172099, "acc_stderr": 0.010966507972178475, "acc_norm": 0.2438070404172099, "acc_norm_stderr": 0.010966507972178475 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.23529411764705882, "acc_stderr": 0.025767252010855963, "acc_norm": 0.23529411764705882, "acc_norm_stderr": 0.025767252010855963 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.32727272727272727, "acc_stderr": 0.044942908662520896, "acc_norm": 0.32727272727272727, "acc_norm_stderr": 0.044942908662520896 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.17959183673469387, "acc_stderr": 0.024573293589585637, "acc_norm": 0.17959183673469387, "acc_norm_stderr": 0.024573293589585637 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.21393034825870647, "acc_stderr": 0.028996909693328927, "acc_norm": 0.21393034825870647, "acc_norm_stderr": 0.028996909693328927 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3253012048192771, "acc_stderr": 0.03647168523683227, "acc_norm": 0.3253012048192771, "acc_norm_stderr": 0.03647168523683227 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.21052631578947367, "acc_stderr": 0.031267817146631786, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.031267817146631786 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.22643818849449204, "mc1_stderr": 0.014651337324602574, "mc2": 0.372644846918848, "mc2_stderr": 0.014009270688888235 }, "harness\|winogrande\|5": { "acc": 0.6479873717442778, "acc_stderr": 0.013422874824929714 }, "harness\|gsm8k\|5": { "acc": 0.028051554207733132, "acc_stderr": 0.004548229533836337 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, 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Samburskoy/TT3	--- license: openrail ---

open-llm-leaderboard/details_NLUHOPOE__Mistral-7B-random-100000

--- pretty_name: Evaluation run of NLUHOPOE/Mistral-7B-random-100000 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [NLUHOPOE/Mistral-7B-random-100000](https://huggingface.co/NLUHOPOE/Mistral-7B-random-100000)\ \ 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_NLUHOPOE__Mistral-7B-random-100000\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-26T09:57:50.433664](https://huggingface.co/datasets/open-llm-leaderboard/details_NLUHOPOE__Mistral-7B-random-100000/blob/main/results_2024-01-26T09-57-50.433664.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\": 0.5312649011937841,\n\ \ \"acc_stderr\": 0.03412886748292059,\n \"acc_norm\": 0.5384137413136626,\n\ \ \"acc_norm_stderr\": 0.03490969986024582,\n \"mc1\": 0.2864137086903305,\n\ \ \"mc1_stderr\": 0.015826142439502356,\n \"mc2\": 0.43163352782122394,\n\ \ \"mc2_stderr\": 0.014658079708747593\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4948805460750853,\n \"acc_stderr\": 0.014610624890309157,\n\ \ \"acc_norm\": 0.537542662116041,\n \"acc_norm_stderr\": 0.014570144495075576\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5836486755626369,\n\ \ \"acc_stderr\": 0.004919457850104236,\n \"acc_norm\": 0.7859988050189205,\n\ \ \"acc_norm_stderr\": 0.004092894578418981\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\": 0.4888888888888889,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309172,\n\ \ \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309172\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6037735849056604,\n \"acc_stderr\": 0.030102793781791194,\n\ \ \"acc_norm\": 0.6037735849056604,\n \"acc_norm_stderr\": 0.030102793781791194\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5833333333333334,\n\ \ \"acc_stderr\": 0.04122728707651281,\n \"acc_norm\": 0.5833333333333334,\n\ \ \"acc_norm_stderr\": 0.04122728707651281\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.43,\n\ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5317919075144508,\n\ \ \"acc_stderr\": 0.03804749744364764,\n \"acc_norm\": 0.5317919075144508,\n\ \ \"acc_norm_stderr\": 0.03804749744364764\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.4765957446808511,\n \"acc_stderr\": 0.03265019475033582,\n\ \ \"acc_norm\": 0.4765957446808511,\n \"acc_norm_stderr\": 0.03265019475033582\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3684210526315789,\n\ \ \"acc_stderr\": 0.04537815354939392,\n \"acc_norm\": 0.3684210526315789,\n\ \ \"acc_norm_stderr\": 0.04537815354939392\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\"\ : 0.3306878306878307,\n \"acc_stderr\": 0.024229965298425086,\n \"\ acc_norm\": 0.3306878306878307,\n \"acc_norm_stderr\": 0.024229965298425086\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.5806451612903226,\n \"acc_stderr\": 0.028071588901091838,\n \"\ acc_norm\": 0.5806451612903226,\n \"acc_norm_stderr\": 0.028071588901091838\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.3793103448275862,\n \"acc_stderr\": 0.034139638059062345,\n \"\ acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.034139638059062345\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.03567969772268049,\n\ \ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.03567969772268049\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.702020202020202,\n \"acc_stderr\": 0.03258630383836556,\n \"acc_norm\"\ : 0.702020202020202,\n \"acc_norm_stderr\": 0.03258630383836556\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.7668393782383419,\n \"acc_stderr\": 0.03051611137147602,\n\ \ \"acc_norm\": 0.7668393782383419,\n \"acc_norm_stderr\": 0.03051611137147602\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5282051282051282,\n \"acc_stderr\": 0.025310639254933886,\n\ \ \"acc_norm\": 0.5282051282051282,\n \"acc_norm_stderr\": 0.025310639254933886\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683515,\n \ \ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683515\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5504201680672269,\n \"acc_stderr\": 0.03231293497137707,\n \ \ \"acc_norm\": 0.5504201680672269,\n \"acc_norm_stderr\": 0.03231293497137707\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7155963302752294,\n \"acc_stderr\": 0.01934203658770258,\n \"\ acc_norm\": 0.7155963302752294,\n \"acc_norm_stderr\": 0.01934203658770258\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502325,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502325\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.696078431372549,\n \"acc_stderr\": 0.032282103870378935,\n \"\ acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.032282103870378935\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7257383966244726,\n \"acc_stderr\": 0.029041333510598035,\n \ \ \"acc_norm\": 0.7257383966244726,\n \"acc_norm_stderr\": 0.029041333510598035\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6233183856502242,\n\ \ \"acc_stderr\": 0.03252113489929188,\n \"acc_norm\": 0.6233183856502242,\n\ \ \"acc_norm_stderr\": 0.03252113489929188\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.04118438565806298,\n\ \ \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.04118438565806298\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6528925619834711,\n \"acc_stderr\": 0.043457245702925335,\n \"\ acc_norm\": 0.6528925619834711,\n \"acc_norm_stderr\": 0.043457245702925335\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.047500773411999854,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.047500773411999854\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6257668711656442,\n \"acc_stderr\": 0.03802068102899615,\n\ \ \"acc_norm\": 0.6257668711656442,\n \"acc_norm_stderr\": 0.03802068102899615\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\ \ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \ \ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8076923076923077,\n\ \ \"acc_stderr\": 0.025819233256483717,\n \"acc_norm\": 0.8076923076923077,\n\ \ \"acc_norm_stderr\": 0.025819233256483717\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7381864623243933,\n\ \ \"acc_stderr\": 0.015720838678445266,\n \"acc_norm\": 0.7381864623243933,\n\ \ \"acc_norm_stderr\": 0.015720838678445266\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5260115606936416,\n \"acc_stderr\": 0.02688264343402289,\n\ \ \"acc_norm\": 0.5260115606936416,\n \"acc_norm_stderr\": 0.02688264343402289\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2681564245810056,\n\ \ \"acc_stderr\": 0.014816119635317012,\n \"acc_norm\": 0.2681564245810056,\n\ \ \"acc_norm_stderr\": 0.014816119635317012\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5915032679738562,\n \"acc_stderr\": 0.028146405993096358,\n\ \ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.028146405993096358\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6109324758842444,\n\ \ \"acc_stderr\": 0.027690337536485372,\n \"acc_norm\": 0.6109324758842444,\n\ \ \"acc_norm_stderr\": 0.027690337536485372\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6080246913580247,\n \"acc_stderr\": 0.027163686038271146,\n\ \ \"acc_norm\": 0.6080246913580247,\n \"acc_norm_stderr\": 0.027163686038271146\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3900709219858156,\n \"acc_stderr\": 0.02909767559946393,\n \ \ \"acc_norm\": 0.3900709219858156,\n \"acc_norm_stderr\": 0.02909767559946393\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3754889178617992,\n\ \ \"acc_stderr\": 0.012367945396728213,\n \"acc_norm\": 0.3754889178617992,\n\ \ \"acc_norm_stderr\": 0.012367945396728213\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904612,\n\ \ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904612\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5147058823529411,\n \"acc_stderr\": 0.020219083895133924,\n \ \ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.020219083895133924\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n\ \ \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n\ \ \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.030635655150387638,\n\ \ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.030635655150387638\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6716417910447762,\n\ \ \"acc_stderr\": 0.033206858897443244,\n \"acc_norm\": 0.6716417910447762,\n\ \ \"acc_norm_stderr\": 0.033206858897443244\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4397590361445783,\n\ \ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\ \ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7251461988304093,\n \"acc_stderr\": 0.03424042924691583,\n\ \ \"acc_norm\": 0.7251461988304093,\n \"acc_norm_stderr\": 0.03424042924691583\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2864137086903305,\n\ \ \"mc1_stderr\": 0.015826142439502356,\n \"mc2\": 0.43163352782122394,\n\ \ \"mc2_stderr\": 0.014658079708747593\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7561168113654302,\n \"acc_stderr\": 0.012068923278908189\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12964366944655042,\n \ \ \"acc_stderr\": 0.00925265775782556\n }\n}\n```" repo_url: https://huggingface.co/NLUHOPOE/Mistral-7B-random-100000 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|arc:challenge|25_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-26T09-57-50.433664.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|gsm8k|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hellaswag|10_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-26T09-57-50.433664.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-management|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T09-57-50.433664.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|truthfulqa:mc|0_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-26T09-57-50.433664.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_26T09_57_50.433664 path: - '**/details_harness|winogrande|5_2024-01-26T09-57-50.433664.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-26T09-57-50.433664.parquet' - config_name: results data_files: - split: 2024_01_26T09_57_50.433664 path: - results_2024-01-26T09-57-50.433664.parquet - split: latest path: - results_2024-01-26T09-57-50.433664.parquet --- # Dataset Card for Evaluation run of NLUHOPOE/Mistral-7B-random-100000  Dataset automatically created during the evaluation run of model [NLUHOPOE/Mistral-7B-random-100000](https://huggingface.co/NLUHOPOE/Mistral-7B-random-100000) 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_NLUHOPOE__Mistral-7B-random-100000", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-26T09:57:50.433664](https://huggingface.co/datasets/open-llm-leaderboard/details_NLUHOPOE__Mistral-7B-random-100000/blob/main/results_2024-01-26T09-57-50.433664.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": 0.5312649011937841, "acc_stderr": 0.03412886748292059, "acc_norm": 0.5384137413136626, "acc_norm_stderr": 0.03490969986024582, "mc1": 0.2864137086903305, "mc1_stderr": 0.015826142439502356, "mc2": 0.43163352782122394, "mc2_stderr": 0.014658079708747593 }, "harness|arc:challenge|25": { "acc": 0.4948805460750853, "acc_stderr": 0.014610624890309157, "acc_norm": 0.537542662116041, "acc_norm_stderr": 0.014570144495075576 }, "harness|hellaswag|10": { "acc": 0.5836486755626369, "acc_stderr": 0.004919457850104236, "acc_norm": 0.7859988050189205, "acc_norm_stderr": 0.004092894578418981 }, "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": 0.4888888888888889, "acc_stderr": 0.04318275491977976, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.04318275491977976 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5131578947368421, "acc_stderr": 0.04067533136309172, "acc_norm": 0.5131578947368421, "acc_norm_stderr": 0.04067533136309172 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6037735849056604, "acc_stderr": 0.030102793781791194, "acc_norm": 0.6037735849056604, "acc_norm_stderr": 0.030102793781791194 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5833333333333334, "acc_stderr": 0.04122728707651281, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.04122728707651281 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5317919075144508, "acc_stderr": 0.03804749744364764, "acc_norm": 0.5317919075144508, "acc_norm_stderr": 0.03804749744364764 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4765957446808511, "acc_stderr": 0.03265019475033582, "acc_norm": 0.4765957446808511, "acc_norm_stderr": 0.03265019475033582 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3684210526315789, "acc_stderr": 0.04537815354939392, "acc_norm": 0.3684210526315789, "acc_norm_stderr": 0.04537815354939392 }, "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": 0.3306878306878307, "acc_stderr": 0.024229965298425086, "acc_norm": 0.3306878306878307, "acc_norm_stderr": 0.024229965298425086 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5806451612903226, "acc_stderr": 0.028071588901091838, "acc_norm": 0.5806451612903226, "acc_norm_stderr": 0.028071588901091838 }, 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"acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7381864623243933, "acc_stderr": 0.015720838678445266, "acc_norm": 0.7381864623243933, "acc_norm_stderr": 0.015720838678445266 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5260115606936416, "acc_stderr": 0.02688264343402289, "acc_norm": 0.5260115606936416, "acc_norm_stderr": 0.02688264343402289 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2681564245810056, "acc_stderr": 0.014816119635317012, "acc_norm": 0.2681564245810056, "acc_norm_stderr": 0.014816119635317012 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5915032679738562, "acc_stderr": 0.028146405993096358, "acc_norm": 0.5915032679738562, "acc_norm_stderr": 0.028146405993096358 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6109324758842444, "acc_stderr": 0.027690337536485372, "acc_norm": 0.6109324758842444, "acc_norm_stderr": 0.027690337536485372 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6080246913580247, "acc_stderr": 0.027163686038271146, "acc_norm": 0.6080246913580247, "acc_norm_stderr": 0.027163686038271146 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3900709219858156, "acc_stderr": 0.02909767559946393, "acc_norm": 0.3900709219858156, "acc_norm_stderr": 0.02909767559946393 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3754889178617992, "acc_stderr": 0.012367945396728213, "acc_norm": 0.3754889178617992, "acc_norm_stderr": 0.012367945396728213 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5147058823529411, "acc_stderr": 0.03035969707904612, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.03035969707904612 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5147058823529411, "acc_stderr": 0.020219083895133924, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.020219083895133924 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5818181818181818, "acc_stderr": 0.04724577405731572, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.04724577405731572 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6448979591836734, "acc_stderr": 0.030635655150387638, "acc_norm": 0.6448979591836734, "acc_norm_stderr": 0.030635655150387638 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6716417910447762, "acc_stderr": 0.033206858897443244, "acc_norm": 0.6716417910447762, "acc_norm_stderr": 0.033206858897443244 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-virology|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7251461988304093, "acc_stderr": 0.03424042924691583, "acc_norm": 0.7251461988304093, "acc_norm_stderr": 0.03424042924691583 }, "harness|truthfulqa:mc|0": { "mc1": 0.2864137086903305, "mc1_stderr": 0.015826142439502356, "mc2": 0.43163352782122394, "mc2_stderr": 0.014658079708747593 }, "harness|winogrande|5": { "acc": 0.7561168113654302, "acc_stderr": 0.012068923278908189 }, "harness|gsm8k|5": { "acc": 0.12964366944655042, "acc_stderr": 0.00925265775782556 } } ``` ## 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]

randomwalksky/cup

--- license: openrail ---

imperialwarrior/open-australian-legal-qa-paraphrased-hard-gemini

--- dataset_info: features: - name: index dtype: 'null' - name: pipeline_1_result dtype: string - name: pipeline_1_result_embeddings dtype: string - name: pipeline_2_context dtype: string - name: pipeline_2_result dtype: string - name: pipeline_2_result_embeddings dtype: string - name: pipeline_3_context dtype: string - name: pipeline_3_result dtype: string - name: pipeline_3_result_embeddings dtype: string - name: pipeline_4_context dtype: string - name: pipeline_4_result dtype: string - name: pipeline_4_result_embeddings dtype: string - name: pipeline_5_context dtype: string - name: pipeline_5_result dtype: string - name: pipeline_5_result_embeddings dtype: string - name: pipeline_6_context dtype: string - name: pipeline_6_result dtype: string - name: pipeline_6_result_embeddings dtype: string - name: pipeline_7_context dtype: string - name: pipeline_7_result dtype: string - name: pipeline_7_result_embeddings dtype: string - name: referenced_question dtype: string - name: answer dtype: string - name: question dtype: string - name: question_non_retrieval_embeddings dtype: string - name: answer_non_retrieval_embeddings dtype: string - name: question_retrieval_embeddings dtype: string - name: answer_retrieval_embeddings dtype: string - name: __index_level_0__ dtype: float64 - name: case_index dtype: float64 - name: pipeline_6_case_indexes sequence: int64 - name: pipeline_7_case_indexes sequence: int64 splits: - name: train num_bytes: 40967131 num_examples: 203 download_size: 14378490 dataset_size: 40967131 configs: - config_name: default data_files: - split: train path: data/train-* ---

zolak/twitter_dataset_79_1713085240

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2654461 num_examples: 6615 download_size: 1346680 dataset_size: 2654461 configs: - config_name: default data_files: - split: train path: data/train-* ---

botp/TigerResearch-pretrain_zh

--- dataset_info: features: - name: dataType dtype: string - name: title dtype: string - name: content dtype: string - name: uniqueKey dtype: string - name: titleUkey dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 58043923125 num_examples: 16905023 download_size: 25662051889 dataset_size: 58043923125 duplicated_from: TigerResearch/pretrain_zh --- # Dataset Card for "pretrain_zh" [Tigerbot](https://github.com/TigerResearch/TigerBot) pretrain数据的中文部分。包含(未压缩前) 中文书籍zh-books 12G, 中文互联网zh-webtext 25G, 中文百科zh-wiki 19G 更多语料请关注开源模型及持续更新 [https://github.com/TigerResearch/TigerBot](https://github.com/TigerResearch/TigerBot) <p align="center" width="40%"> </p> ## Usage ```python import datasets ds_sft = datasets.load_dataset('TigerResearch/pretrain_zh') ```

sam-mosaic/evesix-llama-fmt

--- dataset_info: features: - name: id dtype: string - name: prompt dtype: string - name: language dtype: string - name: response dtype: string splits: - name: train num_bytes: 775938568 num_examples: 486455 download_size: 0 dataset_size: 775938568 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "evesix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

freddyaboulton/gradio-image-urls

--- license: mit ---

severo/doc-image-4

--- size_categories: - n<1K --- # [doc] image dataset 4 This dataset contains 4 jpg image files in the /data directory, with a CSV metadata file providing another data column.

cj-mills/cvat-keypoint-toy-dataset

--- license: mit ---

ovior/twitter_dataset_1713091046

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2394653 num_examples: 6954 download_size: 1377260 dataset_size: 2394653 configs: - config_name: default data_files: - split: train path: data/train-* ---

jxie/qg-tagging

--- dataset_info: features: - name: inputs sequence: sequence: float64 - name: label dtype: int64 splits: - name: train num_bytes: 6944726400 num_examples: 1600000 - name: val num_bytes: 868957000 num_examples: 200000 - name: test num_bytes: 868286700 num_examples: 200000 download_size: 3812296127 dataset_size: 8681970100 --- # Dataset Card for "qg-tagging" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

WinterSchool/ROCO

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: conversations struct: - name: data list: - name: answer dtype: string - name: question dtype: string splits: - name: train num_bytes: 403971903.769 num_examples: 2101 download_size: 403037177 dataset_size: 403971903.769 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - en tags: - medical size_categories: - 1K<n<10K --- This dataset is made using raw data from [ROCO(Radiology Objects in COntext)]( https://www.semanticscholar.org/paper/Radiology-Objects-in-COntext-(ROCO)%3A-A-Multimodal-Pelka-Koitka/a564fabf130ff6e2742cfba90c7a4018937d764d), a multimodal image dataset, with the aim of detecting the interplay between visual elements and semantic relations present in radiology images. For each image in the original raw dataset we used the associated caption to generate a simulated conversation about the image between a user and a chatbot.

zyxleo/cord_donut_multitask

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: task dtype: string - name: image_path dtype: string - name: ground_truth dtype: string - name: labels sequence: int64 - name: input_ids sequence: int64 splits: - name: train num_bytes: 1260759 num_examples: 800 - name: test num_bytes: 93059 num_examples: 100 - name: validation num_bytes: 86619 num_examples: 100 download_size: 299877 dataset_size: 1440437 --- # Dataset Card for "cord_donut_multitask" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

phanvancongthanh/data_part02

--- dataset_info: features: - name: smiles dtype: string splits: - name: train num_bytes: 5777591178 num_examples: 138701675 download_size: 3034948930 dataset_size: 5777591178 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "data_part02" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

loubnabnl/rmarkdown_checks

--- dataset_info: features: - name: entities list: - name: context dtype: string - name: end dtype: int64 - name: score dtype: float32 - name: start dtype: int64 - name: tag dtype: string - name: value dtype: string - name: max_stars_repo_path dtype: string - name: max_stars_repo_name dtype: string - name: max_stars_count dtype: int64 - name: content dtype: string - name: id dtype: string - name: new_content dtype: string - name: modified dtype: bool - name: references dtype: string splits: - name: train num_bytes: 113015277.61771259 num_examples: 3493 download_size: 62607907 dataset_size: 113015277.61771259 --- # Dataset Card for "rmarkdown_checks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/kanroji_mitsuri_demonslayer

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kanroji Mitsuri (Demon Slayer) This is the dataset of Kanroji Mitsuri (Demon Slayer), containing 101 images and their tags. The core tags of this character are `pink_hair, long_hair, multicolored_hair, green_hair, braid, mole, mole_under_eye, gradient_hair, green_eyes, twin_braids, breasts, bangs`, 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 | 101 | 73.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 101 | 57.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 189 | 109.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 101 | 73.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 189 | 133.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanroji_mitsuri_demonslayer/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/kanroji_mitsuri_demonslayer', 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 | 12 | ![](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, closed_mouth, demon_slayer_uniform, portrait, eyelashes, smile, looking_at_viewer | | 1 | 23 | ![](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, long_sleeves, demon_slayer_uniform, solo, haori, black_skirt, holding_sword, cleavage, looking_at_viewer, open_clothes, jacket, miniskirt, pleated_skirt, two-tone_hair, katana, partially_unbuttoned, closed_mouth, wide_sleeves, green_thighhighs, ribbed_legwear, belt | | 2 | 5 | ![](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, demon_slayer_uniform, holding_sword, solo, closed_mouth, katana, from_side, portrait, profile | | 3 | 8 | ![](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, kimono, solo, looking_at_viewer, cleavage, collarbone, haori, upper_body, blush, floral_print, open_mouth, smile, tears | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | closed_mouth | demon_slayer_uniform | portrait | eyelashes | smile | looking_at_viewer | long_sleeves | haori | black_skirt | holding_sword | cleavage | open_clothes | jacket | miniskirt | pleated_skirt | two-tone_hair | katana | partially_unbuttoned | wide_sleeves | green_thighhighs | ribbed_legwear | belt | from_side | profile | kimono | collarbone | upper_body | blush | floral_print | open_mouth | tears | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:-----------------------|:-----------|:------------|:--------|:--------------------|:---------------|:--------|:--------------|:----------------|:-----------|:---------------|:---------|:------------|:----------------|:----------------|:---------|:-----------------------|:---------------|:-------------------|:-----------------|:-------|:------------|:----------|:---------|:-------------|:-------------|:--------|:---------------|:-------------|:--------| | 0 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 23 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | | | | | | | X | | | | | | | X | | | | | | X | X | | | | | | | | | 3 | 8 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | | | | X | X | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X |

zhengxuanzenwu/fair_glue_qnli

--- dataset_info: features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment - name: idx dtype: int32 splits: - name: train num_bytes: 25612443 num_examples: 104743 - name: validation num_bytes: 250467.5086948563 num_examples: 1000 - name: test num_bytes: 1368304 num_examples: 5463 download_size: 18562140 dataset_size: 27231214.508694857 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

miguelinc/oratorialab

--- license: cc-by-sa-4.0 task_categories: - image-classification --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### 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]

HaawkeNeural/latent_diffusion

--- license: other ---

CesarLeblanc/geoplantbert_fill_mask_dataset

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 881264679 num_examples: 900042 - name: test num_bytes: 97851633 num_examples: 99958 download_size: 388245641 dataset_size: 979116312 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

coref-data/litbank_indiscrim

--- dataset_info: - config_name: split_0 features: - name: sentences list: - name: id dtype: int64 - name: misc struct: - name: parse_tree dtype: string - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: deprel dtype: string - name: end_char dtype: int64 - name: feats dtype: string - name: head dtype: int64 - name: id dtype: int64 - name: lemma dtype: string - name: misc dtype: string - name: start_char dtype: int64 - name: text dtype: string - name: upos dtype: string - name: xpos dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 66722053 num_examples: 80 - name: validation num_bytes: 9538946 num_examples: 10 - name: test num_bytes: 10206291 num_examples: 10 download_size: 44024474 dataset_size: 86467290 - config_name: split_1 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 51521261 num_examples: 80 - name: validation num_bytes: 8300522 num_examples: 10 - name: test num_bytes: 7127546 num_examples: 10 download_size: 40296693 dataset_size: 66949329 - config_name: split_2 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 51695718 num_examples: 80 - name: validation num_bytes: 7127546 num_examples: 10 - name: test num_bytes: 8126065 num_examples: 10 download_size: 40287905 dataset_size: 66949329 - config_name: split_3 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 52504381 num_examples: 80 - name: validation num_bytes: 8126065 num_examples: 10 - name: test num_bytes: 6318883 num_examples: 10 download_size: 40292412 dataset_size: 66949329 - config_name: split_4 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 54684836 num_examples: 80 - name: validation num_bytes: 6318883 num_examples: 10 - name: test num_bytes: 5945610 num_examples: 10 download_size: 40283365 dataset_size: 66949329 - config_name: split_5 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 53798360 num_examples: 80 - name: validation num_bytes: 5945610 num_examples: 10 - name: test num_bytes: 7205359 num_examples: 10 download_size: 40284379 dataset_size: 66949329 - config_name: split_6 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 53481831 num_examples: 80 - name: validation num_bytes: 7205359 num_examples: 10 - name: test num_bytes: 6262139 num_examples: 10 download_size: 40294155 dataset_size: 66949329 - config_name: split_7 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 54849391 num_examples: 80 - name: validation num_bytes: 6262139 num_examples: 10 - name: test num_bytes: 5837799 num_examples: 10 download_size: 40294847 dataset_size: 66949329 - config_name: split_8 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 56921350 num_examples: 80 - name: validation num_bytes: 5837799 num_examples: 10 - name: test num_bytes: 4190180 num_examples: 10 download_size: 40292974 dataset_size: 66949329 - config_name: split_9 features: - name: sentences list: - name: id dtype: int64 - name: speaker dtype: 'null' - name: text dtype: string - name: tokens list: - name: id dtype: int64 - name: text dtype: string - name: coref_chains sequence: sequence: sequence: int64 - name: id dtype: string - name: text dtype: string - name: genre dtype: string - name: meta_data struct: - name: author dtype: string - name: comment dtype: string - name: date dtype: string - name: gutenberg_id dtype: string - name: title dtype: string splits: - name: train num_bytes: 55123923 num_examples: 80 - name: validation num_bytes: 4190180 num_examples: 10 - name: test num_bytes: 7635226 num_examples: 10 download_size: 40294593 dataset_size: 66949329 configs: - config_name: split_0 data_files: - split: train path: split_0/train-* - split: validation path: split_0/validation-* - split: test path: split_0/test-* - config_name: split_1 data_files: - split: train path: split_1/train-* - split: validation path: split_1/validation-* - split: test path: split_1/test-* - config_name: split_2 data_files: - split: train path: split_2/train-* - split: validation path: split_2/validation-* - split: test path: split_2/test-* - config_name: split_3 data_files: - split: train path: split_3/train-* - split: validation path: split_3/validation-* - split: test path: split_3/test-* - config_name: split_4 data_files: - split: train path: split_4/train-* - split: validation path: split_4/validation-* - split: test path: split_4/test-* - config_name: split_5 data_files: - split: train path: split_5/train-* - split: validation path: split_5/validation-* - split: test path: split_5/test-* - config_name: split_6 data_files: - split: train path: split_6/train-* - split: validation path: split_6/validation-* - split: test path: split_6/test-* - config_name: split_7 data_files: - split: train path: split_7/train-* - split: validation path: split_7/validation-* - split: test path: split_7/test-* - config_name: split_8 data_files: - split: train path: split_8/train-* - split: validation path: split_8/validation-* - split: test path: split_8/test-* - config_name: split_9 data_files: - split: train path: split_9/train-* - split: validation path: split_9/validation-* - split: test path: split_9/test-* --- This dataset was generated by reformatting [`coref-data/litbank_raw`](https://huggingface.co/datasets/coref-data/litbank_raw) into the indiscrim coreference format. See that repo for dataset details. See [ianporada/coref-data](https://github.com/ianporada/coref-data) for additional conversion details and the conversion script. Please create an issue in the repo above or in this dataset repo for any questions.

CyberHarem/akutsu_ruri_ahogirl

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Akutsu Ruri This is the dataset of Akutsu Ruri, containing 109 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 | 109 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 224 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 109 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 109 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 109 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 109 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 109 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 224 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 224 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 224 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

Innominate/LargeConvo2048

--- dataset_info: features: - name: input dtype: string splits: - name: train num_bytes: 1793023981 num_examples: 984989 download_size: 974905351 dataset_size: 1793023981 task_categories: - text-generation --- A large dataset to train Churro. Every element is under 2048 tokens, when tokenized using the LLaMA Tokenizer.

KenBars/item_rec

--- license: mit ---

qq371/11111

--- license: epl-2.0 ---

FaalSa/f1

--- 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: 79710 num_examples: 1 - name: validation num_bytes: 80190 num_examples: 1 - name: test num_bytes: 80670 num_examples: 1 download_size: 69501 dataset_size: 240570 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

satyanshu404/MS-Marco-Prompt-generation

--- license: unknown ---

andersonbcdefg/dolly-ai-filtered

--- dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string splits: - name: train num_bytes: 2939273 num_examples: 5444 download_size: 0 dataset_size: 2939273 --- # Dataset Card for "dolly-ai-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

JovialValley/phoneme_totalMapped0

--- dataset_info: features: - name: input_values sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 108844668 num_examples: 389 - name: test num_bytes: 27494376 num_examples: 98 download_size: 137098876 dataset_size: 136339044 --- # Dataset Card for "phoneme_totalMapped0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

julienmercier/mobile-eye-tracking-dataset-v3

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

huggingartists/joni-mitchell

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/joni-mitchell" ## 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:** 0.703544 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/ed9a330b2539058076e0c48398599b09.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/joni-mitchell"> <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">Joni Mitchell</div> <a href="https://genius.com/artists/joni-mitchell"> <div style="text-align: center; font-size: 14px;">@joni-mitchell</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/joni-mitchell). ### 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/joni-mitchell") ``` ## 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| |------:|---------:|---:| |559| -| -| '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/joni-mitchell") 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)

KPrashanth/articles_dataset

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 13354657 num_examples: 3188 - name: validation num_bytes: 3257643 num_examples: 798 - name: test num_bytes: 4221414 num_examples: 997 download_size: 9383756 dataset_size: 20833714 task_categories: - text-generation - summarization - text2text-generation - text-classification --- # Dataset Card for "articles_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_fblgit__una-cybertron-7b-v3-OMA

--- pretty_name: Evaluation run of fblgit/una-cybertron-7b-v3-OMA dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [fblgit/una-cybertron-7b-v3-OMA](https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA)\ \ 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_fblgit__una-cybertron-7b-v3-OMA\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T14:22:11.823260](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v3-OMA/blob/main/results_2023-12-16T14-22-11.823260.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\": 0.6407887213707157,\n\ \ \"acc_stderr\": 0.032306194957506966,\n \"acc_norm\": 0.6401991329877219,\n\ \ \"acc_norm_stderr\": 0.03297436021123899,\n \"mc1\": 0.5801713586291309,\n\ \ \"mc1_stderr\": 0.017277030301775766,\n \"mc2\": 0.6984571807866093,\n\ \ \"mc2_stderr\": 0.01516400593831668\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7098976109215017,\n \"acc_stderr\": 0.013261573677520766,\n\ \ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869155\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7183827922724557,\n\ \ \"acc_stderr\": 0.0044886843979795015,\n \"acc_norm\": 0.8794064927305317,\n\ \ \"acc_norm_stderr\": 0.0032498873947065044\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-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\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\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\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\"\ : 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3968253968253968,\n \"acc_stderr\": 0.02519710107424649,\n \"\ acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.02519710107424649\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\ \ \"acc_stderr\": 0.04451807959055328,\n \"acc_norm\": 0.4523809523809524,\n\ \ \"acc_norm_stderr\": 0.04451807959055328\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\": 0.8,\n\ \ \"acc_stderr\": 0.022755204959542943,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.022755204959542943\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592154,\n\ \ \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592154\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\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479047,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479047\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\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602354,\n\ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602354\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\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \ \ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650155,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650155\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8137254901960784,\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\": 0.7890295358649789,\n \"acc_stderr\": 0.02655837250266192,\n \ \ \"acc_norm\": 0.7890295358649789,\n \"acc_norm_stderr\": 0.02655837250266192\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7557251908396947,\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\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516301,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516301\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371802\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577612,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577612\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.45251396648044695,\n\ \ \"acc_stderr\": 0.016646914804438775,\n \"acc_norm\": 0.45251396648044695,\n\ \ \"acc_norm_stderr\": 0.016646914804438775\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n\ \ \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\ \ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\ \ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.0286619962023353,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.0286619962023353\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6584967320261438,\n \"acc_stderr\": 0.019184639328092487,\n \ \ \"acc_norm\": 0.6584967320261438,\n \"acc_norm_stderr\": 0.019184639328092487\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\ \ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.025196929874827075,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.025196929874827075\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685517,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685517\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\": 0.5801713586291309,\n\ \ \"mc1_stderr\": 0.017277030301775766,\n \"mc2\": 0.6984571807866093,\n\ \ \"mc2_stderr\": 0.01516400593831668\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047987\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6770280515542078,\n \ \ \"acc_stderr\": 0.012880360794851815\n }\n}\n```" repo_url: https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|arc:challenge|25_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-16T14-22-11.823260.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|gsm8k|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hellaswag|10_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-22-11.823260.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-22-11.823260.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T14-22-11.823260.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T14_22_11.823260 path: - '**/details_harness|winogrande|5_2023-12-16T14-22-11.823260.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-16T14-22-11.823260.parquet' - config_name: results data_files: - split: 2023_12_16T14_22_11.823260 path: - results_2023-12-16T14-22-11.823260.parquet - split: latest path: - results_2023-12-16T14-22-11.823260.parquet --- # Dataset Card for Evaluation run of fblgit/una-cybertron-7b-v3-OMA  Dataset automatically created during the evaluation run of model [fblgit/una-cybertron-7b-v3-OMA](https://huggingface.co/fblgit/una-cybertron-7b-v3-OMA) 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_fblgit__una-cybertron-7b-v3-OMA", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T14:22:11.823260](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v3-OMA/blob/main/results_2023-12-16T14-22-11.823260.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": 0.6407887213707157, "acc_stderr": 0.032306194957506966, "acc_norm": 0.6401991329877219, "acc_norm_stderr": 0.03297436021123899, "mc1": 0.5801713586291309, "mc1_stderr": 0.017277030301775766, "mc2": 0.6984571807866093, "mc2_stderr": 0.01516400593831668 }, "harness|arc:challenge|25": { "acc": 0.7098976109215017, "acc_stderr": 0.013261573677520766, "acc_norm": 0.7303754266211604, "acc_norm_stderr": 0.012968040686869155 }, "harness|hellaswag|10": { "acc": 0.7183827922724557, "acc_stderr": 0.0044886843979795015, "acc_norm": 0.8794064927305317, "acc_norm_stderr": 0.0032498873947065044 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "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": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566017, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566017 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6473988439306358, "acc_stderr": 0.03643037168958548, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.03643037168958548 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.048108401480826346, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.048108401480826346 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3968253968253968, "acc_stderr": 0.02519710107424649, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.02519710107424649 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4523809523809524, "acc_stderr": 0.04451807959055328, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.04451807959055328 }, "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": 0.8, "acc_stderr": 0.022755204959542943, "acc_norm": 0.8, "acc_norm_stderr": 0.022755204959542943 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.46798029556650245, "acc_stderr": 0.035107665979592154, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.035107665979592154 }, "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": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479047, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479047 }, "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": 0.6410256410256411, "acc_stderr": 0.024321738484602354, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.024321738484602354 }, "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": 0.634453781512605, "acc_stderr": 0.031282177063684614, "acc_norm": 0.634453781512605, "acc_norm_stderr": 0.031282177063684614 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8366972477064221, "acc_stderr": 0.01584825580650155, "acc_norm": 0.8366972477064221, "acc_norm_stderr": 0.01584825580650155 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7890295358649789, "acc_stderr": 0.02655837250266192, "acc_norm": 0.7890295358649789, "acc_norm_stderr": 0.02655837250266192 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "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": 0.7520661157024794, "acc_stderr": 0.03941897526516301, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.03941897526516301 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371802, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371802 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577612, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577612 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.45251396648044695, "acc_stderr": 0.016646914804438775, "acc_norm": 0.45251396648044695, "acc_norm_stderr": 0.016646914804438775 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6993464052287581, "acc_stderr": 0.02625605383571896, "acc_norm": 0.6993464052287581, "acc_norm_stderr": 0.02625605383571896 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7041800643086816, "acc_stderr": 0.025922371788818763, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.025922371788818763 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4645390070921986, "acc_stderr": 0.029752389657427047, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.029752389657427047 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303957, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303957 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.0286619962023353, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.0286619962023353 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6584967320261438, "acc_stderr": 0.019184639328092487, "acc_norm": 0.6584967320261438, "acc_norm_stderr": 0.019184639328092487 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7306122448979592, "acc_stderr": 0.02840125202902294, "acc_norm": 0.7306122448979592, "acc_norm_stderr": 0.02840125202902294 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.025196929874827075, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.025196929874827075 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness|hendrycksTest-virology|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685517, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685517 }, "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": 0.5801713586291309, "mc1_stderr": 0.017277030301775766, "mc2": 0.6984571807866093, "mc2_stderr": 0.01516400593831668 }, "harness|winogrande|5": { "acc": 0.8208366219415943, "acc_stderr": 0.010777949156047987 }, "harness|gsm8k|5": { "acc": 0.6770280515542078, "acc_stderr": 0.012880360794851815 } } ``` ## 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]

hippocrates/MedQA_one_shot_test

--- dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: train num_bytes: 2491204 num_examples: 1273 - name: valid num_bytes: 2491204 num_examples: 1273 - name: test num_bytes: 2491204 num_examples: 1273 download_size: 2482272 dataset_size: 7473612 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---

Tristan/olm-wikipedia-20221220-1-percent-tokenized-766

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 300178944 num_examples: 65143 download_size: 93964466 dataset_size: 300178944 --- # Dataset Card for "olm-wikipedia-20221220-1-percent-tokenized-766" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/imai_midori_shirobako

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Imai Midori This is the dataset of Imai Midori, containing 276 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 | 276 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 611 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 276 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 276 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 276 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 276 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 276 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 611 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 611 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 611 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

open-llm-leaderboard/details_Aryanne__sheared-plus-westlake-50_75p

--- pretty_name: Evaluation run of Aryanne/sheared-plus-westlake-50_75p dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aryanne/sheared-plus-westlake-50_75p](https://huggingface.co/Aryanne/sheared-plus-westlake-50_75p)\ \ 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_Aryanne__sheared-plus-westlake-50_75p\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-23T22:04:31.166175](https://huggingface.co/datasets/open-llm-leaderboard/details_Aryanne__sheared-plus-westlake-50_75p/blob/main/results_2024-01-23T22-04-31.166175.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\": 0.2672140448144015,\n\ \ \"acc_stderr\": 0.03127543112931,\n \"acc_norm\": 0.26909676356851875,\n\ \ \"acc_norm_stderr\": 0.03210076459110669,\n \"mc1\": 0.2668298653610771,\n\ \ \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.42638955634632064,\n\ \ \"mc2_stderr\": 0.014788435851867392\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3310580204778157,\n \"acc_stderr\": 0.013752062419817836,\n\ \ \"acc_norm\": 0.34044368600682595,\n \"acc_norm_stderr\": 0.013847460518892983\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4441346345349532,\n\ \ \"acc_stderr\": 0.004958537988993581,\n \"acc_norm\": 0.5804620593507269,\n\ \ \"acc_norm_stderr\": 0.004924748500639335\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.32592592592592595,\n\ \ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.32592592592592595,\n\ \ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.27631578947368424,\n \"acc_stderr\": 0.03639057569952924,\n\ \ \"acc_norm\": 0.27631578947368424,\n \"acc_norm_stderr\": 0.03639057569952924\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.32,\n\ \ \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \ \ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.24150943396226415,\n \"acc_stderr\": 0.026341480371118355,\n\ \ \"acc_norm\": 0.24150943396226415,\n \"acc_norm_stderr\": 0.026341480371118355\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\"\ : 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.23699421965317918,\n\ \ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n\ \ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n\ \ \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.20425531914893616,\n \"acc_stderr\": 0.026355158413349417,\n\ \ \"acc_norm\": 0.20425531914893616,\n \"acc_norm_stderr\": 0.026355158413349417\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\ \ \"acc_stderr\": 0.04096985139843671,\n \"acc_norm\": 0.2543859649122807,\n\ \ \"acc_norm_stderr\": 0.04096985139843671\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.22758620689655173,\n \"acc_stderr\": 0.03493950380131184,\n\ \ \"acc_norm\": 0.22758620689655173,\n \"acc_norm_stderr\": 0.03493950380131184\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25925925925925924,\n \"acc_stderr\": 0.02256989707491841,\n \"\ acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.02256989707491841\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1984126984126984,\n\ \ \"acc_stderr\": 0.03567016675276863,\n \"acc_norm\": 0.1984126984126984,\n\ \ \"acc_norm_stderr\": 0.03567016675276863\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.24516129032258063,\n \"acc_stderr\": 0.02447224384089553,\n \"\ acc_norm\": 0.24516129032258063,\n \"acc_norm_stderr\": 0.02447224384089553\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114468,\n \"\ acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114468\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.28484848484848485,\n \"acc_stderr\": 0.035243908445117836,\n\ \ \"acc_norm\": 0.28484848484848485,\n \"acc_norm_stderr\": 0.035243908445117836\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.21212121212121213,\n \"acc_stderr\": 0.029126522834586818,\n \"\ acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.029126522834586818\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.27461139896373055,\n \"acc_stderr\": 0.03221024508041156,\n\ \ \"acc_norm\": 0.27461139896373055,\n \"acc_norm_stderr\": 0.03221024508041156\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2692307692307692,\n \"acc_stderr\": 0.022489389793654824,\n\ \ \"acc_norm\": 0.2692307692307692,\n \"acc_norm_stderr\": 0.022489389793654824\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2777777777777778,\n \"acc_stderr\": 0.02730914058823018,\n \ \ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.02730914058823018\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.19747899159663865,\n \"acc_stderr\": 0.025859164122051463,\n\ \ \"acc_norm\": 0.19747899159663865,\n \"acc_norm_stderr\": 0.025859164122051463\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.20733944954128442,\n \"acc_stderr\": 0.017381415563608664,\n \"\ acc_norm\": 0.20733944954128442,\n \"acc_norm_stderr\": 0.017381415563608664\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.3287037037037037,\n \"acc_stderr\": 0.03203614084670058,\n \"\ acc_norm\": 0.3287037037037037,\n \"acc_norm_stderr\": 0.03203614084670058\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604246,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604246\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.26582278481012656,\n \"acc_stderr\": 0.02875679962965834,\n \ \ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.02875679962965834\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2809917355371901,\n \"acc_stderr\": 0.04103203830514512,\n \"\ acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.04103203830514512\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3055555555555556,\n\ \ \"acc_stderr\": 0.044531975073749834,\n \"acc_norm\": 0.3055555555555556,\n\ \ \"acc_norm_stderr\": 0.044531975073749834\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.2822085889570552,\n \"acc_stderr\": 0.03536117886664742,\n\ \ \"acc_norm\": 0.2822085889570552,\n \"acc_norm_stderr\": 0.03536117886664742\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3482142857142857,\n\ \ \"acc_stderr\": 0.04521829902833585,\n \"acc_norm\": 0.3482142857142857,\n\ \ \"acc_norm_stderr\": 0.04521829902833585\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.1650485436893204,\n \"acc_stderr\": 0.036756688322331886,\n\ \ \"acc_norm\": 0.1650485436893204,\n \"acc_norm_stderr\": 0.036756688322331886\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2264957264957265,\n\ \ \"acc_stderr\": 0.02742100729539294,\n \"acc_norm\": 0.2264957264957265,\n\ \ \"acc_norm_stderr\": 0.02742100729539294\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23499361430395913,\n\ \ \"acc_stderr\": 0.01516202415227844,\n \"acc_norm\": 0.23499361430395913,\n\ \ \"acc_norm_stderr\": 0.01516202415227844\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.2976878612716763,\n \"acc_stderr\": 0.024617055388677003,\n\ \ \"acc_norm\": 0.2976878612716763,\n \"acc_norm_stderr\": 0.024617055388677003\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24916201117318434,\n\ \ \"acc_stderr\": 0.014465893829859926,\n \"acc_norm\": 0.24916201117318434,\n\ \ \"acc_norm_stderr\": 0.014465893829859926\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.23202614379084968,\n \"acc_stderr\": 0.02417084087934101,\n\ \ \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.02417084087934101\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.29260450160771706,\n\ \ \"acc_stderr\": 0.025839898334877983,\n \"acc_norm\": 0.29260450160771706,\n\ \ \"acc_norm_stderr\": 0.025839898334877983\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2191358024691358,\n \"acc_stderr\": 0.023016705640262192,\n\ \ \"acc_norm\": 0.2191358024691358,\n \"acc_norm_stderr\": 0.023016705640262192\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2198581560283688,\n \"acc_stderr\": 0.024706141070705474,\n \ \ \"acc_norm\": 0.2198581560283688,\n \"acc_norm_stderr\": 0.024706141070705474\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2379400260756193,\n\ \ \"acc_stderr\": 0.010875700787694228,\n \"acc_norm\": 0.2379400260756193,\n\ \ \"acc_norm_stderr\": 0.010875700787694228\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.35661764705882354,\n \"acc_stderr\": 0.029097209568411955,\n\ \ \"acc_norm\": 0.35661764705882354,\n \"acc_norm_stderr\": 0.029097209568411955\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25980392156862747,\n \"acc_stderr\": 0.017740899509177795,\n \ \ \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.017740899509177795\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.20909090909090908,\n\ \ \"acc_stderr\": 0.038950910157241364,\n \"acc_norm\": 0.20909090909090908,\n\ \ \"acc_norm_stderr\": 0.038950910157241364\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.20408163265306123,\n \"acc_stderr\": 0.025801283475090506,\n\ \ \"acc_norm\": 0.20408163265306123,\n \"acc_norm_stderr\": 0.025801283475090506\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.25870646766169153,\n\ \ \"acc_stderr\": 0.030965903123573012,\n \"acc_norm\": 0.25870646766169153,\n\ \ \"acc_norm_stderr\": 0.030965903123573012\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.2710843373493976,\n\ \ \"acc_stderr\": 0.03460579907553027,\n \"acc_norm\": 0.2710843373493976,\n\ \ \"acc_norm_stderr\": 0.03460579907553027\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.28654970760233917,\n \"acc_stderr\": 0.03467826685703826,\n\ \ \"acc_norm\": 0.28654970760233917,\n \"acc_norm_stderr\": 0.03467826685703826\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2668298653610771,\n\ \ \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.42638955634632064,\n\ \ \"mc2_stderr\": 0.014788435851867392\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.569060773480663,\n \"acc_stderr\": 0.013917796623335964\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/Aryanne/sheared-plus-westlake-50_75p leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|arc:challenge|25_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-23T22-04-31.166175.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|gsm8k|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hellaswag|10_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-23T22-04-31.166175.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-management|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T22-04-31.166175.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|truthfulqa:mc|0_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-23T22-04-31.166175.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_23T22_04_31.166175 path: - '**/details_harness|winogrande|5_2024-01-23T22-04-31.166175.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-23T22-04-31.166175.parquet' - config_name: results data_files: - split: 2024_01_23T22_04_31.166175 path: - results_2024-01-23T22-04-31.166175.parquet - split: latest path: - results_2024-01-23T22-04-31.166175.parquet --- # Dataset Card for Evaluation run of Aryanne/sheared-plus-westlake-50_75p  Dataset automatically created during the evaluation run of model [Aryanne/sheared-plus-westlake-50_75p](https://huggingface.co/Aryanne/sheared-plus-westlake-50_75p) 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_Aryanne__sheared-plus-westlake-50_75p", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-23T22:04:31.166175](https://huggingface.co/datasets/open-llm-leaderboard/details_Aryanne__sheared-plus-westlake-50_75p/blob/main/results_2024-01-23T22-04-31.166175.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": 0.2672140448144015, "acc_stderr": 0.03127543112931, "acc_norm": 0.26909676356851875, "acc_norm_stderr": 0.03210076459110669, "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.42638955634632064, "mc2_stderr": 0.014788435851867392 }, "harness|arc:challenge|25": { "acc": 0.3310580204778157, "acc_stderr": 0.013752062419817836, "acc_norm": 0.34044368600682595, "acc_norm_stderr": 0.013847460518892983 }, "harness|hellaswag|10": { "acc": 0.4441346345349532, "acc_stderr": 0.004958537988993581, "acc_norm": 0.5804620593507269, "acc_norm_stderr": 0.004924748500639335 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.32592592592592595, "acc_stderr": 0.040491220417025055, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.040491220417025055 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.27631578947368424, "acc_stderr": 0.03639057569952924, "acc_norm": 0.27631578947368424, "acc_norm_stderr": 0.03639057569952924 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.24150943396226415, "acc_stderr": 0.026341480371118355, "acc_norm": 0.24150943396226415, "acc_norm_stderr": 0.026341480371118355 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.28, "acc_stderr": 0.04512608598542129, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542129 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.23699421965317918, "acc_stderr": 0.03242414757483098, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483098 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237655, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237655 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.20425531914893616, "acc_stderr": 0.026355158413349417, "acc_norm": 0.20425531914893616, "acc_norm_stderr": 0.026355158413349417 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2543859649122807, "acc_stderr": 0.04096985139843671, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.04096985139843671 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.22758620689655173, "acc_stderr": 0.03493950380131184, "acc_norm": 0.22758620689655173, "acc_norm_stderr": 0.03493950380131184 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25925925925925924, "acc_stderr": 0.02256989707491841, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.02256989707491841 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.1984126984126984, "acc_stderr": 0.03567016675276863, "acc_norm": 0.1984126984126984, "acc_norm_stderr": 0.03567016675276863 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24516129032258063, "acc_stderr": 0.02447224384089553, "acc_norm": 0.24516129032258063, "acc_norm_stderr": 0.02447224384089553 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.26108374384236455, "acc_stderr": 0.030903796952114468, "acc_norm": 0.26108374384236455, "acc_norm_stderr": 0.030903796952114468 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.28484848484848485, "acc_stderr": 0.035243908445117836, "acc_norm": 0.28484848484848485, "acc_norm_stderr": 0.035243908445117836 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.21212121212121213, "acc_stderr": 0.029126522834586818, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.029126522834586818 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.27461139896373055, "acc_stderr": 0.03221024508041156, "acc_norm": 0.27461139896373055, "acc_norm_stderr": 0.03221024508041156 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2692307692307692, "acc_stderr": 0.022489389793654824, "acc_norm": 0.2692307692307692, "acc_norm_stderr": 0.022489389793654824 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2777777777777778, "acc_stderr": 0.02730914058823018, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.02730914058823018 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.19747899159663865, "acc_stderr": 0.025859164122051463, "acc_norm": 0.19747899159663865, "acc_norm_stderr": 0.025859164122051463 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763743, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763743 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.20733944954128442, "acc_stderr": 0.017381415563608664, "acc_norm": 0.20733944954128442, "acc_norm_stderr": 0.017381415563608664 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3287037037037037, "acc_stderr": 0.03203614084670058, "acc_norm": 0.3287037037037037, "acc_norm_stderr": 0.03203614084670058 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604246, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604246 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.26582278481012656, "acc_stderr": 0.02875679962965834, "acc_norm": 0.26582278481012656, "acc_norm_stderr": 0.02875679962965834 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.31390134529147984, "acc_stderr": 0.031146796482972465, "acc_norm": 0.31390134529147984, "acc_norm_stderr": 0.031146796482972465 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2809917355371901, "acc_stderr": 0.04103203830514512, "acc_norm": 0.2809917355371901, "acc_norm_stderr": 0.04103203830514512 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.3055555555555556, "acc_stderr": 0.044531975073749834, "acc_norm": 0.3055555555555556, "acc_norm_stderr": 0.044531975073749834 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.2822085889570552, "acc_stderr": 0.03536117886664742, "acc_norm": 0.2822085889570552, "acc_norm_stderr": 0.03536117886664742 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3482142857142857, "acc_stderr": 0.04521829902833585, "acc_norm": 0.3482142857142857, "acc_norm_stderr": 0.04521829902833585 }, "harness|hendrycksTest-management|5": { "acc": 0.1650485436893204, "acc_stderr": 0.036756688322331886, "acc_norm": 0.1650485436893204, "acc_norm_stderr": 0.036756688322331886 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2264957264957265, "acc_stderr": 0.02742100729539294, "acc_norm": 0.2264957264957265, "acc_norm_stderr": 0.02742100729539294 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.23499361430395913, "acc_stderr": 0.01516202415227844, "acc_norm": 0.23499361430395913, "acc_norm_stderr": 0.01516202415227844 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2976878612716763, "acc_stderr": 0.024617055388677003, "acc_norm": 0.2976878612716763, "acc_norm_stderr": 0.024617055388677003 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24916201117318434, "acc_stderr": 0.014465893829859926, "acc_norm": 0.24916201117318434, "acc_norm_stderr": 0.014465893829859926 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.23202614379084968, "acc_stderr": 0.02417084087934101, "acc_norm": 0.23202614379084968, "acc_norm_stderr": 0.02417084087934101 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.29260450160771706, "acc_stderr": 0.025839898334877983, "acc_norm": 0.29260450160771706, "acc_norm_stderr": 0.025839898334877983 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2191358024691358, "acc_stderr": 0.023016705640262192, "acc_norm": 0.2191358024691358, "acc_norm_stderr": 0.023016705640262192 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2198581560283688, "acc_stderr": 0.024706141070705474, "acc_norm": 0.2198581560283688, "acc_norm_stderr": 0.024706141070705474 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2379400260756193, "acc_stderr": 0.010875700787694228, "acc_norm": 0.2379400260756193, "acc_norm_stderr": 0.010875700787694228 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.35661764705882354, "acc_stderr": 0.029097209568411955, "acc_norm": 0.35661764705882354, "acc_norm_stderr": 0.029097209568411955 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.25980392156862747, "acc_stderr": 0.017740899509177795, "acc_norm": 0.25980392156862747, "acc_norm_stderr": 0.017740899509177795 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.20909090909090908, "acc_stderr": 0.038950910157241364, "acc_norm": 0.20909090909090908, "acc_norm_stderr": 0.038950910157241364 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.20408163265306123, "acc_stderr": 0.025801283475090506, "acc_norm": 0.20408163265306123, "acc_norm_stderr": 0.025801283475090506 }, "harness|hendrycksTest-sociology|5": { "acc": 0.25870646766169153, "acc_stderr": 0.030965903123573012, "acc_norm": 0.25870646766169153, "acc_norm_stderr": 0.030965903123573012 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-virology|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553027, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553027 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.28654970760233917, "acc_stderr": 0.03467826685703826, "acc_norm": 0.28654970760233917, "acc_norm_stderr": 0.03467826685703826 }, "harness|truthfulqa:mc|0": { "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.42638955634632064, "mc2_stderr": 0.014788435851867392 }, "harness|winogrande|5": { "acc": 0.569060773480663, "acc_stderr": 0.013917796623335964 }, "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] 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open-llm-leaderboard/details_kekmodel__StopCarbon-10.7B-v3

--- pretty_name: Evaluation run of kekmodel/StopCarbon-10.7B-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kekmodel/StopCarbon-10.7B-v3](https://huggingface.co/kekmodel/StopCarbon-10.7B-v3)\ \ 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_kekmodel__StopCarbon-10.7B-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-30T10:15:50.941228](https://huggingface.co/datasets/open-llm-leaderboard/details_kekmodel__StopCarbon-10.7B-v3/blob/main/results_2023-12-30T10-15-50.941228.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\": 0.6649827029825734,\n\ \ \"acc_stderr\": 0.03166471620730208,\n \"acc_norm\": 0.6659533597079996,\n\ \ \"acc_norm_stderr\": 0.03230745700615819,\n \"mc1\": 0.572827417380661,\n\ \ \"mc1_stderr\": 0.017316834410963926,\n \"mc2\": 0.7193506614125464,\n\ \ \"mc2_stderr\": 0.014949525122441177\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6860068259385665,\n \"acc_stderr\": 0.013562691224726293,\n\ \ \"acc_norm\": 0.7098976109215017,\n \"acc_norm_stderr\": 0.013261573677520764\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7180840470025891,\n\ \ \"acc_stderr\": 0.004490130691020433,\n \"acc_norm\": 0.8856801433977295,\n\ \ \"acc_norm_stderr\": 0.003175490413694419\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-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\": 0.743421052631579,\n \"acc_stderr\": 0.0355418036802569,\n\ \ \"acc_norm\": 0.743421052631579,\n \"acc_norm_stderr\": 0.0355418036802569\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.72,\n\ \ \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.5,\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\": 0.34,\n\ \ \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \ \ \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\"\ : {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n\ \ \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n\ \ \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n\ \ \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768077,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768077\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6212765957446809,\n\ \ \"acc_stderr\": 0.03170995606040655,\n \"acc_norm\": 0.6212765957446809,\n\ \ \"acc_norm_stderr\": 0.03170995606040655\n },\n \"harness|hendrycksTest-econometrics|5\"\ : {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n\ \ \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n\ \ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\ : 0.6206896551724138,\n \"acc_stderr\": 0.040434618619167466,\n \"\ acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.040434618619167466\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4947089947089947,\n \"acc_stderr\": 0.02574986828855657,\n \"\ acc_norm\": 0.4947089947089947,\n \"acc_norm_stderr\": 0.02574986828855657\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8161290322580645,\n \"acc_stderr\": 0.022037217340267822,\n \"\ acc_norm\": 0.8161290322580645,\n \"acc_norm_stderr\": 0.022037217340267822\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"\ acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\ \ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857406,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857406\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634332,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634332\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242741,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242741\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669235,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669235\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5648148148148148,\n \"acc_stderr\": 0.03381200005643527,\n \"\ acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.03381200005643527\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8529411764705882,\n \"acc_stderr\": 0.02485747808025046,\n \"\ acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02485747808025046\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8565400843881856,\n \"acc_stderr\": 0.022818291821017012,\n \ \ \"acc_norm\": 0.8565400843881856,\n \"acc_norm_stderr\": 0.022818291821017012\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.03160295143776678,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.03160295143776678\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\": 0.03749492448709696,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709696\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\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\": 0.8543689320388349,\n \"acc_stderr\": 0.03492606476623791,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.03492606476623791\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\ \ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\ \ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.80970625798212,\n\ \ \"acc_stderr\": 0.014036945850381396,\n \"acc_norm\": 0.80970625798212,\n\ \ \"acc_norm_stderr\": 0.014036945850381396\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4201117318435754,\n\ \ \"acc_stderr\": 0.016507671073256402,\n \"acc_norm\": 0.4201117318435754,\n\ \ \"acc_norm_stderr\": 0.016507671073256402\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.02463004897982478,\n\ \ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.02463004897982478\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n\ \ \"acc_stderr\": 0.025311765975426122,\n \"acc_norm\": 0.7266881028938906,\n\ \ \"acc_norm_stderr\": 0.025311765975426122\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7746913580246914,\n \"acc_stderr\": 0.023246202647819753,\n\ \ \"acc_norm\": 0.7746913580246914,\n \"acc_norm_stderr\": 0.023246202647819753\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4908735332464146,\n\ \ \"acc_stderr\": 0.01276810860164001,\n \"acc_norm\": 0.4908735332464146,\n\ \ \"acc_norm_stderr\": 0.01276810860164001\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7463235294117647,\n \"acc_stderr\": 0.026431329870789527,\n\ \ \"acc_norm\": 0.7463235294117647,\n \"acc_norm_stderr\": 0.026431329870789527\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \ \ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\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\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\ \ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.02553843336857834,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.02553843336857834\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.032180937956023566,\n\ \ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.032180937956023566\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.572827417380661,\n\ \ \"mc1_stderr\": 0.017316834410963926,\n \"mc2\": 0.7193506614125464,\n\ \ \"mc2_stderr\": 0.014949525122441177\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8318863456985004,\n \"acc_stderr\": 0.010510336954166732\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6322971948445792,\n \ \ \"acc_stderr\": 0.013281630503395475\n }\n}\n```" repo_url: https://huggingface.co/kekmodel/StopCarbon-10.7B-v3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|arc:challenge|25_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-30T10-15-50.941228.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|gsm8k|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hellaswag|10_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-30T10-15-50.941228.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-management|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T10-15-50.941228.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|truthfulqa:mc|0_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-30T10-15-50.941228.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_30T10_15_50.941228 path: - '**/details_harness|winogrande|5_2023-12-30T10-15-50.941228.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-30T10-15-50.941228.parquet' - config_name: results data_files: - split: 2023_12_30T10_15_50.941228 path: - results_2023-12-30T10-15-50.941228.parquet - split: latest path: - results_2023-12-30T10-15-50.941228.parquet --- # Dataset Card for Evaluation run of kekmodel/StopCarbon-10.7B-v3  Dataset automatically created during the evaluation run of model [kekmodel/StopCarbon-10.7B-v3](https://huggingface.co/kekmodel/StopCarbon-10.7B-v3) 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_kekmodel__StopCarbon-10.7B-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-30T10:15:50.941228](https://huggingface.co/datasets/open-llm-leaderboard/details_kekmodel__StopCarbon-10.7B-v3/blob/main/results_2023-12-30T10-15-50.941228.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": 0.6649827029825734, "acc_stderr": 0.03166471620730208, "acc_norm": 0.6659533597079996, "acc_norm_stderr": 0.03230745700615819, "mc1": 0.572827417380661, "mc1_stderr": 0.017316834410963926, "mc2": 0.7193506614125464, "mc2_stderr": 0.014949525122441177 }, "harness|arc:challenge|25": { "acc": 0.6860068259385665, "acc_stderr": 0.013562691224726293, "acc_norm": 0.7098976109215017, "acc_norm_stderr": 0.013261573677520764 }, "harness|hellaswag|10": { "acc": 0.7180840470025891, "acc_stderr": 0.004490130691020433, "acc_norm": 0.8856801433977295, "acc_norm_stderr": 0.003175490413694419 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "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": 0.743421052631579, "acc_stderr": 0.0355418036802569, "acc_norm": 0.743421052631579, "acc_norm_stderr": 0.0355418036802569 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.690566037735849, "acc_stderr": 0.028450154794118637, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118637 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768077, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768077 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6212765957446809, "acc_stderr": 0.03170995606040655, "acc_norm": 0.6212765957446809, "acc_norm_stderr": 0.03170995606040655 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6206896551724138, "acc_stderr": 0.040434618619167466, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.040434618619167466 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4947089947089947, "acc_stderr": 0.02574986828855657, "acc_norm": 0.4947089947089947, "acc_norm_stderr": 0.02574986828855657 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8161290322580645, "acc_stderr": 0.022037217340267822, "acc_norm": 0.8161290322580645, "acc_norm_stderr": 0.022037217340267822 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768763, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768763 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35555555555555557, "acc_stderr": 0.029185714949857406, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.029185714949857406 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7142857142857143, "acc_stderr": 0.029344572500634332, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.029344572500634332 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242741, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242741 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669235, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669235 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5648148148148148, "acc_stderr": 0.03381200005643527, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.03381200005643527 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8529411764705882, "acc_stderr": 0.02485747808025046, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.02485747808025046 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8565400843881856, "acc_stderr": 0.022818291821017012, "acc_norm": 0.8565400843881856, "acc_norm_stderr": 0.022818291821017012 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6681614349775785, "acc_stderr": 0.03160295143776678, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.03160295143776678 }, "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": 0.03749492448709696, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.03749492448709696 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037182, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037182 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "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": 0.8543689320388349, "acc_stderr": 0.03492606476623791, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.03492606476623791 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.80970625798212, "acc_stderr": 0.014036945850381396, "acc_norm": 0.80970625798212, "acc_norm_stderr": 0.014036945850381396 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7514450867052023, "acc_stderr": 0.023267528432100174, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.023267528432100174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4201117318435754, "acc_stderr": 0.016507671073256402, "acc_norm": 0.4201117318435754, "acc_norm_stderr": 0.016507671073256402 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7549019607843137, "acc_stderr": 0.02463004897982478, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.02463004897982478 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7266881028938906, "acc_stderr": 0.025311765975426122, "acc_norm": 0.7266881028938906, "acc_norm_stderr": 0.025311765975426122 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7746913580246914, "acc_stderr": 0.023246202647819753, "acc_norm": 0.7746913580246914, "acc_norm_stderr": 0.023246202647819753 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4908735332464146, "acc_stderr": 0.01276810860164001, "acc_norm": 0.4908735332464146, "acc_norm_stderr": 0.01276810860164001 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7463235294117647, "acc_stderr": 0.026431329870789527, "acc_norm": 0.7463235294117647, "acc_norm_stderr": 0.026431329870789527 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6781045751633987, "acc_stderr": 0.018901015322093092, "acc_norm": 0.6781045751633987, "acc_norm_stderr": 0.018901015322093092 }, "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": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.02553843336857834, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.02553843336857834 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7719298245614035, "acc_stderr": 0.032180937956023566, "acc_norm": 0.7719298245614035, "acc_norm_stderr": 0.032180937956023566 }, "harness|truthfulqa:mc|0": { "mc1": 0.572827417380661, "mc1_stderr": 0.017316834410963926, "mc2": 0.7193506614125464, "mc2_stderr": 0.014949525122441177 }, "harness|winogrande|5": { "acc": 0.8318863456985004, "acc_stderr": 0.010510336954166732 }, "harness|gsm8k|5": { "acc": 0.6322971948445792, "acc_stderr": 0.013281630503395475 } } ``` ## 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 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[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_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1

--- pretty_name: Evaluation run of Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1](https://huggingface.co/Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1)\ \ 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_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-01T21:44:48.551629](https://huggingface.co/datasets/open-llm-leaderboard/details_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1/blob/main/results_2024-02-01T21-44-48.551629.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\": 0.235616909983485,\n\ \ \"acc_stderr\": 0.030096196864815804,\n \"acc_norm\": 0.23617812988980863,\n\ \ \"acc_norm_stderr\": 0.030895352600212644,\n \"mc1\": 0.2460220318237454,\n\ \ \"mc1_stderr\": 0.015077219200662578,\n \"mc2\": 0.49848823283731625,\n\ \ \"mc2_stderr\": 0.016449164481650215\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.2090443686006826,\n \"acc_stderr\": 0.011882746987406458,\n\ \ \"acc_norm\": 0.2645051194539249,\n \"acc_norm_stderr\": 0.012889272949313366\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.25632344154550885,\n\ \ \"acc_stderr\": 0.004357101984278612,\n \"acc_norm\": 0.2568213503286198,\n\ \ \"acc_norm_stderr\": 0.00435987151963954\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n\ \ \"acc_stderr\": 0.03355677216313141,\n \"acc_norm\": 0.18518518518518517,\n\ \ \"acc_norm_stderr\": 0.03355677216313141\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.24150943396226415,\n \"acc_stderr\": 0.026341480371118376,\n\ \ \"acc_norm\": 0.24150943396226415,\n \"acc_norm_stderr\": 0.026341480371118376\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816507,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816507\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2023121387283237,\n\ \ \"acc_stderr\": 0.03063114553919882,\n \"acc_norm\": 0.2023121387283237,\n\ \ \"acc_norm_stderr\": 0.03063114553919882\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\ \ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21052631578947367,\n\ \ \"acc_stderr\": 0.038351539543994194,\n \"acc_norm\": 0.21052631578947367,\n\ \ \"acc_norm_stderr\": 0.038351539543994194\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\ acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036625,\n \ \ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036625\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.1774193548387097,\n\ \ \"acc_stderr\": 0.02173254068932927,\n \"acc_norm\": 0.1774193548387097,\n\ \ \"acc_norm_stderr\": 0.02173254068932927\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.18719211822660098,\n \"acc_stderr\": 0.027444924966882618,\n\ \ \"acc_norm\": 0.18719211822660098,\n \"acc_norm_stderr\": 0.027444924966882618\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2222222222222222,\n \"acc_stderr\": 0.029620227874790482,\n \"\ acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.029620227874790482\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2,\n \"acc_stderr\": 0.020280805062535722,\n \"acc_norm\"\ : 0.2,\n \"acc_norm_stderr\": 0.020280805062535722\n },\n \"harness|hendrycksTest-high_school_mathematics|5\"\ : {\n \"acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n\ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.026265024608275882,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.026265024608275882\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.19205298013245034,\n \"acc_stderr\": 0.032162984205936135,\n \"\ acc_norm\": 0.19205298013245034,\n \"acc_norm_stderr\": 0.032162984205936135\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.1889908256880734,\n \"acc_stderr\": 0.016785481159203634,\n \"\ acc_norm\": 0.1889908256880734,\n \"acc_norm_stderr\": 0.016785481159203634\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.14814814814814814,\n \"acc_stderr\": 0.024227629273728356,\n \"\ acc_norm\": 0.14814814814814814,\n \"acc_norm_stderr\": 0.024227629273728356\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3004484304932735,\n\ \ \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.3004484304932735,\n\ \ \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.22137404580152673,\n \"acc_stderr\": 0.0364129708131373,\n\ \ \"acc_norm\": 0.22137404580152673,\n \"acc_norm_stderr\": 0.0364129708131373\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\ \ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\ \ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.24393358876117496,\n\ \ \"acc_stderr\": 0.015357212665829489,\n \"acc_norm\": 0.24393358876117496,\n\ \ \"acc_norm_stderr\": 0.015357212665829489\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.2581699346405229,\n \"acc_stderr\": 0.025058503316958157,\n\ \ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.025058503316958157\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\ \ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\ \ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.02841820861940679,\n\ \ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.02841820861940679\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n\ \ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\ \ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\ \ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.2710843373493976,\n \"acc_stderr\": 0.034605799075530276,\n\ \ \"acc_norm\": 0.2710843373493976,\n \"acc_norm_stderr\": 0.034605799075530276\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.32748538011695905,\n\ \ \"acc_stderr\": 0.035993357714560276,\n \"acc_norm\": 0.32748538011695905,\n\ \ \"acc_norm_stderr\": 0.035993357714560276\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.2460220318237454,\n \"mc1_stderr\": 0.015077219200662578,\n\ \ \"mc2\": 0.49848823283731625,\n \"mc2_stderr\": 0.016449164481650215\n\ \ },\n \"harness|winogrande|5\": {\n \"acc\": 0.4940805051302289,\n\ \ \"acc_stderr\": 0.014051500838485807\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1 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_01T21_44_48.551629 path: - '**/details_harness|arc:challenge|25_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-01T21-44-48.551629.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|gsm8k|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hellaswag|10_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-01T21-44-48.551629.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-management|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-01T21-44-48.551629.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|truthfulqa:mc|0_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-01T21-44-48.551629.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_01T21_44_48.551629 path: - '**/details_harness|winogrande|5_2024-02-01T21-44-48.551629.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-01T21-44-48.551629.parquet' - config_name: results data_files: - split: 2024_02_01T21_44_48.551629 path: - results_2024-02-01T21-44-48.551629.parquet - split: latest path: - results_2024-02-01T21-44-48.551629.parquet --- # Dataset Card for Evaluation run of Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1  Dataset automatically created during the evaluation run of model [Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1](https://huggingface.co/Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1) 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_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-01T21:44:48.551629](https://huggingface.co/datasets/open-llm-leaderboard/details_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1/blob/main/results_2024-02-01T21-44-48.551629.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": 0.235616909983485, "acc_stderr": 0.030096196864815804, "acc_norm": 0.23617812988980863, "acc_norm_stderr": 0.030895352600212644, "mc1": 0.2460220318237454, "mc1_stderr": 0.015077219200662578, "mc2": 0.49848823283731625, "mc2_stderr": 0.016449164481650215 }, "harness|arc:challenge|25": { "acc": 0.2090443686006826, "acc_stderr": 0.011882746987406458, "acc_norm": 0.2645051194539249, "acc_norm_stderr": 0.012889272949313366 }, "harness|hellaswag|10": { "acc": 0.25632344154550885, "acc_stderr": 0.004357101984278612, "acc_norm": 0.2568213503286198, "acc_norm_stderr": 0.00435987151963954 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03355677216313141, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03355677216313141 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.24150943396226415, "acc_stderr": 0.026341480371118376, "acc_norm": 0.24150943396226415, "acc_norm_stderr": 0.026341480371118376 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.25, "acc_stderr": 0.03621034121889507, "acc_norm": 0.25, "acc_norm_stderr": 0.03621034121889507 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.23, "acc_stderr": 0.04229525846816507, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816507 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2023121387283237, "acc_stderr": 0.03063114553919882, "acc_norm": 0.2023121387283237, "acc_norm_stderr": 0.03063114553919882 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.21052631578947367, "acc_stderr": 0.038351539543994194, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.038351539543994194 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.20899470899470898, "acc_stderr": 0.02094048156533486, "acc_norm": 0.20899470899470898, "acc_norm_stderr": 0.02094048156533486 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04040610178208841, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04040610178208841 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.19, "acc_stderr": 0.03942772444036625, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036625 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.18719211822660098, "acc_stderr": 0.027444924966882618, "acc_norm": 0.18719211822660098, "acc_norm_stderr": 0.027444924966882618 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2222222222222222, "acc_stderr": 0.029620227874790482, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.029620227874790482 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2, "acc_stderr": 0.020280805062535722, "acc_norm": 0.2, "acc_norm_stderr": 0.020280805062535722 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2111111111111111, "acc_stderr": 0.024882116857655075, "acc_norm": 0.2111111111111111, "acc_norm_stderr": 0.024882116857655075 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.20588235294117646, "acc_stderr": 0.026265024608275882, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.026265024608275882 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.19205298013245034, "acc_stderr": 0.032162984205936135, "acc_norm": 0.19205298013245034, "acc_norm_stderr": 0.032162984205936135 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.1889908256880734, "acc_stderr": 0.016785481159203634, "acc_norm": 0.1889908256880734, "acc_norm_stderr": 0.016785481159203634 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.14814814814814814, "acc_stderr": 0.024227629273728356, "acc_norm": 0.14814814814814814, "acc_norm_stderr": 0.024227629273728356 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.270042194092827, "acc_stderr": 0.028900721906293426, "acc_norm": 0.270042194092827, "acc_norm_stderr": 0.028900721906293426 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.3004484304932735, "acc_stderr": 0.030769352008229143, "acc_norm": 0.3004484304932735, "acc_norm_stderr": 0.030769352008229143 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.22137404580152673, "acc_stderr": 0.0364129708131373, "acc_norm": 0.22137404580152673, "acc_norm_stderr": 0.0364129708131373 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.25925925925925924, "acc_stderr": 0.042365112580946336, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.042365112580946336 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness|hendrycksTest-management|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness|hendrycksTest-marketing|5": { "acc": 0.2905982905982906, "acc_stderr": 0.02974504857267404, "acc_norm": 0.2905982905982906, "acc_norm_stderr": 0.02974504857267404 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.24393358876117496, "acc_stderr": 0.015357212665829489, "acc_norm": 0.24393358876117496, "acc_norm_stderr": 0.015357212665829489 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2581699346405229, "acc_stderr": 0.025058503316958157, "acc_norm": 0.2581699346405229, "acc_norm_stderr": 0.025058503316958157 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.1864951768488746, "acc_stderr": 0.02212243977248077, "acc_norm": 0.1864951768488746, "acc_norm_stderr": 0.02212243977248077 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432417, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432417 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.3235294117647059, "acc_stderr": 0.02841820861940679, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.02841820861940679 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-virology|5": { "acc": 0.2710843373493976, "acc_stderr": 0.034605799075530276, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.034605799075530276 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.32748538011695905, "acc_stderr": 0.035993357714560276, "acc_norm": 0.32748538011695905, "acc_norm_stderr": 0.035993357714560276 }, "harness|truthfulqa:mc|0": { "mc1": 0.2460220318237454, "mc1_stderr": 0.015077219200662578, "mc2": 0.49848823283731625, "mc2_stderr": 0.016449164481650215 }, "harness|winogrande|5": { "acc": 0.4940805051302289, "acc_stderr": 0.014051500838485807 }, "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]

senhorsapo/yor

--- license: openrail ---

ibm/Wish-QA-ASQA-Falcon

--- dataset_info: features: - name: id dtype: string - name: old_question dtype: string - name: old_answer dtype: string - name: passage_1 dtype: string - name: passage_2 dtype: string - name: passage_3 dtype: string - name: text dtype: string - name: qa dtype: string - name: question dtype: string - name: answer dtype: string - name: doc_score dtype: float64 - name: score_qa dtype: float64 - name: ans_num_words dtype: int64 - name: text_num_words dtype: int64 - name: text_longer_1.5 dtype: int64 - name: input dtype: string - name: output 0 answer dtype: string splits: - name: train num_bytes: 23433520 num_examples: 4354 download_size: 14082055 dataset_size: 23433520 --- # Dataset Card for "Wish-QA-ASQA-Falcon" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yekta/banchan

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 6899876.0 num_examples: 13 download_size: 6901557 dataset_size: 6899876.0 --- # Dataset Card for "banchan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Deojoandco/dialogturns_not_generated_train

--- dataset_info: features: - name: url dtype: string - name: id dtype: string - name: num_comments dtype: int64 - name: name dtype: string - name: title dtype: string - name: body dtype: string - name: score dtype: int64 - name: upvote_ratio dtype: float64 - name: distinguished dtype: string - name: over_18 dtype: bool - name: created_utc dtype: int64 - name: comments list: - name: body dtype: string - name: created_utc dtype: float64 - name: distinguished dtype: string - name: id dtype: string - name: permalink dtype: string - name: score dtype: int64 - name: best_num_comments dtype: int64 - name: query dtype: string - name: dialog dtype: string - name: annotation_success dtype: bool - name: annotation_text dtype: string - name: turns_generated dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 7511834 num_examples: 284 download_size: 4057449 dataset_size: 7511834 --- # Dataset Card for "dialogturns_not_generated_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nexdata/Handwriting_OCR_Data_of_Japanese_and_Korean

--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Handwriting_OCR_Data_of_Japanese_and_Korean ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.nexdata.ai/datasets/127?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary 100 People - Handwriting OCR Data of Japanese and Korean,. This dadaset was collected from 100 subjects including 50 Japanese, 49 Koreans and 1 Afghan. For different subjects, the corpus are different. The data diversity includes multiple cellphone models and different corpus. This dataset can be used for tasks, such as handwriting OCR data of Japanese and Korean. For more details, please refer to the link: https://www.nexdata.ai/datasets/127?source=Huggingface ### Supported Tasks and Leaderboards image-to-text, computer-vision: The dataset can be used to train a model for image-to-text. ### Languages Japanese, Korean ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions

arieg/bw_spec_cls_80_11

--- 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': '27855' '1': '27856' '2': '27866' '3': '27945' '4': '27953' '5': '27975' '6': '27978' '7': '27981' '8': '27987' '9': '28241' '10': '28260' '11': '28477' '12': '28478' '13': '28479' '14': '28480' '15': '28481' '16': '28482' '17': '28483' '18': '28484' '19': '28485' '20': '28546' '21': '28548' '22': '28553' '23': '28571' '24': '28608' '25': '29045' '26': '29128' '27': '29180' '28': '29243' '29': '29245' '30': '29255' '31': '29271' '32': '29272' '33': '29355' '34': '29465' '35': '29480' '36': '29587' '37': '29602' '38': '29673' '39': '29718' '40': '29719' '41': '29720' '42': '29721' '43': '29738' '44': '29739' '45': '29740' '46': '29741' '47': '29742' '48': '29744' '49': '29745' '50': '29746' '51': '29747' '52': '29750' '53': '29752' '54': '29807' '55': '29813' '56': '29816' '57': '29961' '58': '29971' '59': '30041' '60': '30043' '61': '30050' '62': '30056' '63': '30058' '64': '30059' '65': '30090' '66': '30095' '67': '30120' '68': '30196' '69': '30198' '70': '30230' '71': '30486' '72': '30487' '73': '30488' '74': '30519' '75': '30520' '76': '30521' '77': '30522' '78': '30636' '79': '30690' splits: - name: train num_bytes: 89109867.2 num_examples: 1600 download_size: 88188426 dataset_size: 89109867.2 --- # Dataset Card for "bw_spec_cls_80_11" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-wmt14-de-en-fbedb0-67643145604

--- type: predictions tags: - autotrain - evaluation datasets: - wmt14 eval_info: task: translation model: leukas/byt5-large-wmt14-deen metrics: ['bleu'] dataset_name: wmt14 dataset_config: de-en dataset_split: test col_mapping: source: translation.de target: translation.en --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Translation * Model: leukas/byt5-large-wmt14-deen * Dataset: wmt14 * Config: de-en * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@seeed](https://huggingface.co/seeed) for evaluating this model.

Carlisle/msmacro-passage-non-abs-small

--- license: mit ---

vgaraujov/fapesp

--- language: - en - es - pt license: - cc-by-2.0 task_categories: - translation dataset_info: - config_name: en-pt features: - name: translation dtype: translation: languages: - en - pt splits: - name: train num_bytes: 47417503 num_examples: 160975 - name: validation num_bytes: 405055 num_examples: 1375 - name: test num_bytes: 407579 num_examples: 1447 download_size: 29615550 dataset_size: 48230137 - config_name: es-pt features: - name: translation dtype: translation: languages: - es - pt splits: - name: train num_bytes: 47480897 num_examples: 158197 - name: validation num_bytes: 377101 num_examples: 1302 - name: test num_bytes: 400915 num_examples: 1379 download_size: 29829573 dataset_size: 48258913 configs: - config_name: en-pt data_files: - split: train path: en-pt/train-* - split: validation path: en-pt/validation-* - split: test path: en-pt/test-* - config_name: es-pt data_files: - split: train path: es-pt/train-* - split: validation path: es-pt/validation-* - split: test path: es-pt/test-* ---

ibranze/araproje_arc_tr_f2

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices sequence: - name: text dtype: string - name: label dtype: string - name: answerKey dtype: string splits: - name: validation num_bytes: 86423.0 num_examples: 250 download_size: 46973 dataset_size: 86423.0 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "araproje_arc_tr_f2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ywan111/macbook-dataset-b4

--- license: apache-2.0 ---

MLNavigator/russian-retrieval

--- license: mit --- Based on Sberquad - Answer converted to human affordable answer. - Context augmented with some pices of texts from wiki accordant to text on tematic and keywords. - This dataset cold be used for training retrieval LLM models or modificators for ability of LLM to retrieve target information from collection of tematic related texts. - Dataset has version with SOURCE data for generating answer with specifing source document for right answer. See file retrieval_dataset_src.jsonl Dataset consists of 45278 examples in russian language of format: { 'text': 'text with correct answer', 'q': 'question text', 'a': 'correct answer text', 'context': 'text of 4-10 text chunks, one with right answer and others relevant with text and question on tematic and keywords' } Length of one example of context + question + answer is less than 7000 symbols. It should be less than 2048 tokens of rugpt tokenizer. File retrieval_dataset_src.jsonl has additionally SOURCE data for every text chunk in context, also SOURCE of right answer is set in answer. This variant of dataset is useful if you need extract answer with specifing source of the right answer. { 'text': 'text with correct answer', 'q': 'question text', 'a': 'correct answer text with SOURCE data of text', 'context': 'text of 4-10 text chunks, one with right answer and others relevant with text and question on tematic and keywords. Each of text chunks has it's own SOURCE data' } All SOURCE data are sintetic generated and not real.

taesiri/imagenet_hard_review_data

--- license: mit ---

arthurmluz/GPTextSum_data-wiki_1024_results

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: summary dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 - name: moverScore dtype: float64 splits: - name: validation num_bytes: 25941 num_examples: 20 download_size: 32992 dataset_size: 25941 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "GPTextSum_data-wiki_1024_results" rouge= {'rouge1': 0.20436494957206813, 'rouge2': 0.06669792477248418, 'rougeL': 0.1645584797463879, 'rougeLsum': 0.1645584797463879} bert= {'precision': 0.7313757807016372, 'recall': 0.6589481264352799, 'f1': 0.6928485721349716}

Jessiecs/llama-2-7b-a3-self-curated

--- dataset_info: features: - name: instruction_generated dtype: string - name: response dtype: string - name: rating_score dtype: string - name: is_high_quality dtype: bool splits: - name: train num_bytes: 231166 num_examples: 128 download_size: 152410 dataset_size: 231166 configs: - config_name: default data_files: - split: train path: data/train-* ---

gianma/eurlexsum_ita_cleaned_8192_232

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: is_camera dtype: bool - name: reference dtype: string - name: summary dtype: string - name: tokenized_len_total dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4119487 num_examples: 228 - name: validation num_bytes: 231666 num_examples: 13 - name: test num_bytes: 253451 num_examples: 13 download_size: 0 dataset_size: 4604604 --- # Dataset Card for "eurlexsum_ita_cleaned_8192_232" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jonathanli/winston-ai-luka-dataset

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: model dtype: string splits: - name: train num_bytes: 29522548 num_examples: 10000 download_size: 16797973 dataset_size: 29522548 configs: - config_name: default data_files: - split: train path: data/train-* ---

sherelyn912/finnews_en_2wk_qa

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 310555.2191464821 num_examples: 1387 - name: test num_bytes: 77694.78085351788 num_examples: 347 download_size: 182095 dataset_size: 388250.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- extracted content from various website articles using code from [FinNLP](https://github.com/AI4Finance-Foundation/FinNLP) and generated questions and answers with OpenAI API **gpt3.5-turbo-16k**

David-Egea/Creditcard-fraud-detection

--- license: mit --- # Credit Card Fraud Detection This dataset was downloaded from https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud/data adn uploaded for educational purposes.

CronosGhost/wikipedia_fr_snippets

--- license: mit dataset_info: features: - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 8126467952 num_examples: 12138717 download_size: 4271960527 dataset_size: 8126467952 configs: - config_name: default data_files: - split: train path: data/train-* ---

mwong/climate-evidence-related

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-sa-3.0 - gpl-3.0 multilinguality: - monolingual paperswithcode_id: climate-fever pretty_name: climate-fever size_categories: - 100K<n<1M source_datasets: - extended|climate_fever task_categories: - text-classification task_ids: - fact-checking --- ### Dataset Summary This dataset is extracted from Climate Fever dataset (https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html), pre-processed and ready to train and evaluate. The training objective is a text classification task - given a claim and evidence, predict if evidence is related to claim.

gagan3012/FSR

--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: answer dtype: string - name: query dtype: string - name: choices sequence: string - name: gold dtype: int64 splits: - name: test num_bytes: 5874352 num_examples: 3931 download_size: 1688819 dataset_size: 5874352 configs: - config_name: default data_files: - split: test path: data/test-* ---

liuyanchen1015/MULTI_VALUE_mrpc_a_ing

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 194382 num_examples: 670 - name: train num_bytes: 412800 num_examples: 1423 - name: validation num_bytes: 45079 num_examples: 150 download_size: 437103 dataset_size: 652261 --- # Dataset Card for "MULTI_VALUE_mrpc_a_ing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

automated-research-group/llama2_7b_chat-agieval-results

--- dataset_info: config_name: '{''do_sample''=False, ''beams''=1}' features: - name: id dtype: string - name: prediction dtype: string - name: agieval_accuracy dtype: bool splits: - name: train num_bytes: 85792 num_examples: 254 download_size: 46709 dataset_size: 85792 configs: - config_name: '{''do_sample''=False, ''beams''=1}' data_files: - split: train path: '{''do_sample''=False, ''beams''=1}/train-*' ---

coref-data/niv2_winogrande_raw

--- license: apache-2.0 --- # Natural Instructions v2 Winogrande Tasks - Project: https://github.com/allenai/natural-instructions - Data source: [DataProvenanceInitiative/niv2_submix_original](https://huggingface.co/datasets/DataProvenanceInitiative/niv2_submix_original) ## Details This dataset contains all Winogrande examples that were included in the [Flan 2022 collection](https://github.com/google-research/FLAN/tree/main/flan/v2) which were orignally published in Super-Natural-Instructions. The data is copied from the preprocessed Natural Instructions v2 dataset at [DataProvenanceInitiative/niv2_submix_original](https://huggingface.co/datasets/DataProvenanceInitiative/niv2_submix_original). These tasks are: 1. 'task029_winogrande_full_object': Creating a pair of fill in the blank question-answer pairs on objects. 2. 'task030_winogrande_full_person': Creating a pair of fill in the blank questions on persons. 3. 'task031_winogrande_question_generation_object': Writing a fill in the blank question on objects. 4. 'task032_winogrande_question_generation_person': Writing a fill in the blank question on persons. 5. 'task033_winogrande_answer_generation': Answering a fill in the blank question on objects. 6. 'task034_winogrande_question_modification_object': Modifying a fill in the blank question on objects. 7. 'task035_winogrande_question_modification_person': Modifying a fill in the blank question on persons. 8. 'task1391_winogrande_easy_answer_generation': Answering a fill in the blank question on objects. ### Fields - `inputs`: a `string` feature. - `targets`: a `string` feature. - `task_source`: a `string` feature. - `task_name`: a `string` feature. - `template_type`: a `string` feature. ## Citation ``` @inproceedings{wang-etal-2022-super, title = "Super-{N}atural{I}nstructions: Generalization via Declarative Instructions on 1600+ {NLP} Tasks", author = "Wang, Yizhong and Mishra, Swaroop and Alipoormolabashi, Pegah and Kordi, Yeganeh and Mirzaei, Amirreza and Naik, Atharva and Ashok, Arjun and Dhanasekaran, Arut Selvan and Arunkumar, Anjana and Stap, David and Pathak, Eshaan and Karamanolakis, Giannis and Lai, Haizhi and Purohit, Ishan and Mondal, Ishani and Anderson, Jacob and Kuznia, Kirby and Doshi, Krima and Pal, Kuntal Kumar and Patel, Maitreya and Moradshahi, Mehrad and Parmar, Mihir and Purohit, Mirali and Varshney, Neeraj and Kaza, Phani Rohitha and Verma, Pulkit and Puri, Ravsehaj Singh and Karia, Rushang and Doshi, Savan and Sampat, Shailaja Keyur and Mishra, Siddhartha and Reddy A, Sujan and Patro, Sumanta and Dixit, Tanay and Shen, Xudong", editor = "Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.340", doi = "10.18653/v1/2022.emnlp-main.340", pages = "5085--5109", abstract = "How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions{---}training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9{\%} on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.", } ```

Zarakun/audiobooks_ua_test

--- tags: - "audio" configs: - config_name: default data_files: - split: train path: "data/train.parquet" --- ### About dataset It is a dataset of ukrainian audiobooks Each sample contain an approximately 8 seconds od ukrainian speech ### Loading script ``` >>> load_dataset("Zarakun/audiobooks_ua_test") ``` ### Dataset structure **Every example has the following: **audio** - the waveform **rate** - the sampling rate of the waveform **file_id** - the id of the speaker **duration** - the duration of the video in seconds **sentence** - the transcript of the video

Shivanshyadav/wrinkled_to_ironed_clothes

--- license: apache-2.0 dataset_info: features: - name: input_image dtype: image - name: instruct_prompt dtype: string - name: output_image dtype: image splits: - name: train num_bytes: 20133465629.31 num_examples: 3022 download_size: 19529267242 dataset_size: 20133465629.31 configs: - config_name: default data_files: - split: train path: data/train-* ---

neerajaabhyankar/hindustani-raag-small

--- license: cc-by-4.0 task_categories: - audio-classification tags: - music - hindustani - raag - raga - raaga pretty_name: Hindustani Raag Identification (Small) size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: train path: '**/train_*.mp3' - split: test path: '**/test_*.mp3' ---

open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat

--- pretty_name: Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kyujinpy/PlatYi-34B-Llama-Q-FastChat](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat)\ \ 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_kyujinpy__PlatYi-34B-Llama-Q-FastChat\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-10T05:55:07.023442](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat/blob/main/results_2023-12-10T05-55-07.023442.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\": 0.7741514926490987,\n\ \ \"acc_stderr\": 0.027646135380835733,\n \"acc_norm\": 0.7828326159595959,\n\ \ \"acc_norm_stderr\": 0.02814394317924737,\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5362104216200869,\n\ \ \"mc2_stderr\": 0.01504184962981019\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042194,\n\ \ \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6533559051981677,\n\ \ \"acc_stderr\": 0.004749286071559569,\n \"acc_norm\": 0.8525194184425413,\n\ \ \"acc_norm_stderr\": 0.003538596773704832\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\"\ : {\n \"acc\": 0.7555555555555555,\n \"acc_stderr\": 0.03712537833614866,\n\ \ \"acc_norm\": 0.7555555555555555,\n \"acc_norm_stderr\": 0.03712537833614866\n\ \ },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.881578947368421,\n\ \ \"acc_stderr\": 0.026293995855474938,\n \"acc_norm\": 0.881578947368421,\n\ \ \"acc_norm_stderr\": 0.026293995855474938\n },\n \"harness|hendrycksTest-business_ethics|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\n \ \ },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\":\ \ 0.8075471698113208,\n \"acc_stderr\": 0.024262979839372277,\n \"\ acc_norm\": 0.8075471698113208,\n \"acc_norm_stderr\": 0.024262979839372277\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9027777777777778,\n\ \ \"acc_stderr\": 0.024774516250440182,\n \"acc_norm\": 0.9027777777777778,\n\ \ \"acc_norm_stderr\": 0.024774516250440182\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.67,\n\ \ \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956913,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956913\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7283236994219653,\n\ \ \"acc_stderr\": 0.03391750322321659,\n \"acc_norm\": 0.7283236994219653,\n\ \ \"acc_norm_stderr\": 0.03391750322321659\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.04951218252396262,\n\ \ \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.04951218252396262\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.7872340425531915,\n \"acc_stderr\": 0.02675439134803976,\n\ \ \"acc_norm\": 0.7872340425531915,\n \"acc_norm_stderr\": 0.02675439134803976\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5789473684210527,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7724137931034483,\n \"acc_stderr\": 0.03493950380131184,\n\ \ \"acc_norm\": 0.7724137931034483,\n \"acc_norm_stderr\": 0.03493950380131184\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.753968253968254,\n \"acc_stderr\": 0.022182037202948365,\n \"\ acc_norm\": 0.753968253968254,\n \"acc_norm_stderr\": 0.022182037202948365\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.6031746031746031,\n\ \ \"acc_stderr\": 0.043758884927270585,\n \"acc_norm\": 0.6031746031746031,\n\ \ \"acc_norm_stderr\": 0.043758884927270585\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.9258064516129032,\n\ \ \"acc_stderr\": 0.01490952930054621,\n \"acc_norm\": 0.9258064516129032,\n\ \ \"acc_norm_stderr\": 0.01490952930054621\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6847290640394089,\n \"acc_stderr\": 0.03269080871970186,\n\ \ \"acc_norm\": 0.6847290640394089,\n \"acc_norm_stderr\": 0.03269080871970186\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \"acc_norm\"\ : 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.0188526702349931,\n \"acc_norm\"\ : 0.9242424242424242,\n \"acc_norm_stderr\": 0.0188526702349931\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.823076923076923,\n \"acc_stderr\": 0.01934807017439698,\n \ \ \"acc_norm\": 0.823076923076923,\n \"acc_norm_stderr\": 0.01934807017439698\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.4925925925925926,\n \"acc_stderr\": 0.0304821923951915,\n \ \ \"acc_norm\": 0.4925925925925926,\n \"acc_norm_stderr\": 0.0304821923951915\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8697478991596639,\n \"acc_stderr\": 0.02186325849485212,\n \ \ \"acc_norm\": 0.8697478991596639,\n \"acc_norm_stderr\": 0.02186325849485212\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5496688741721855,\n \"acc_stderr\": 0.04062290018683775,\n \"\ acc_norm\": 0.5496688741721855,\n \"acc_norm_stderr\": 0.04062290018683775\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9302752293577982,\n \"acc_stderr\": 0.010919426411848607,\n \"\ acc_norm\": 0.9302752293577982,\n \"acc_norm_stderr\": 0.010919426411848607\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.7222222222222222,\n \"acc_stderr\": 0.0305467452649532,\n \"acc_norm\"\ : 0.7222222222222222,\n \"acc_norm_stderr\": 0.0305467452649532\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9215686274509803,\n\ \ \"acc_stderr\": 0.018869514646658935,\n \"acc_norm\": 0.9215686274509803,\n\ \ \"acc_norm_stderr\": 0.018869514646658935\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.919831223628692,\n \"acc_stderr\": 0.017676679991891632,\n\ \ \"acc_norm\": 0.919831223628692,\n \"acc_norm_stderr\": 0.017676679991891632\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951538,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951538\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.9338842975206612,\n \"acc_stderr\": 0.022683403691723312,\n \"\ acc_norm\": 0.9338842975206612,\n \"acc_norm_stderr\": 0.022683403691723312\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.03038159675665167,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.03038159675665167\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n\ \ \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6339285714285714,\n\ \ \"acc_stderr\": 0.04572372358737431,\n \"acc_norm\": 0.6339285714285714,\n\ \ \"acc_norm_stderr\": 0.04572372358737431\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.883495145631068,\n \"acc_stderr\": 0.031766839486404054,\n\ \ \"acc_norm\": 0.883495145631068,\n \"acc_norm_stderr\": 0.031766839486404054\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9487179487179487,\n\ \ \"acc_stderr\": 0.014450181176872736,\n \"acc_norm\": 0.9487179487179487,\n\ \ \"acc_norm_stderr\": 0.014450181176872736\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9080459770114943,\n\ \ \"acc_stderr\": 0.010333225570778518,\n \"acc_norm\": 0.9080459770114943,\n\ \ \"acc_norm_stderr\": 0.010333225570778518\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8352601156069365,\n \"acc_stderr\": 0.019971040982442265,\n\ \ \"acc_norm\": 0.8352601156069365,\n \"acc_norm_stderr\": 0.019971040982442265\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.788826815642458,\n\ \ \"acc_stderr\": 0.013650276794312199,\n \"acc_norm\": 0.788826815642458,\n\ \ \"acc_norm_stderr\": 0.013650276794312199\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8660130718954249,\n \"acc_stderr\": 0.019504890618464815,\n\ \ \"acc_norm\": 0.8660130718954249,\n \"acc_norm_stderr\": 0.019504890618464815\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8456591639871383,\n\ \ \"acc_stderr\": 0.020519050342084726,\n \"acc_norm\": 0.8456591639871383,\n\ \ \"acc_norm_stderr\": 0.020519050342084726\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8580246913580247,\n \"acc_stderr\": 0.019420260109438293,\n\ \ \"acc_norm\": 0.8580246913580247,\n \"acc_norm_stderr\": 0.019420260109438293\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6631205673758865,\n \"acc_stderr\": 0.02819553487396673,\n \ \ \"acc_norm\": 0.6631205673758865,\n \"acc_norm_stderr\": 0.02819553487396673\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6186440677966102,\n\ \ \"acc_stderr\": 0.01240550940188812,\n \"acc_norm\": 0.6186440677966102,\n\ \ \"acc_norm_stderr\": 0.01240550940188812\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8272058823529411,\n \"acc_stderr\": 0.022966067585581767,\n\ \ \"acc_norm\": 0.8272058823529411,\n \"acc_norm_stderr\": 0.022966067585581767\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8316993464052288,\n \"acc_stderr\": 0.01513580333869338,\n \ \ \"acc_norm\": 0.8316993464052288,\n \"acc_norm_stderr\": 0.01513580333869338\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8367346938775511,\n \"acc_stderr\": 0.023661699177098615,\n\ \ \"acc_norm\": 0.8367346938775511,\n \"acc_norm_stderr\": 0.023661699177098615\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8706467661691543,\n\ \ \"acc_stderr\": 0.02372983088101853,\n \"acc_norm\": 0.8706467661691543,\n\ \ \"acc_norm_stderr\": 0.02372983088101853\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.93,\n \"acc_stderr\": 0.025643239997624294,\n \ \ \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.025643239997624294\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015578,\n\ \ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015578\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5362104216200869,\n\ \ \"mc2_stderr\": 0.01504184962981019\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8216258879242304,\n \"acc_stderr\": 0.010759352014855944\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.44351781652767247,\n \ \ \"acc_stderr\": 0.013684327592606165\n }\n}\n```" repo_url: https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|arc:challenge|25_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-10T05-55-07.023442.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|gsm8k|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hellaswag|10_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-55-07.023442.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-management|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T05-55-07.023442.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|truthfulqa:mc|0_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-10T05-55-07.023442.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_10T05_55_07.023442 path: - '**/details_harness|winogrande|5_2023-12-10T05-55-07.023442.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-10T05-55-07.023442.parquet' - config_name: results data_files: - split: 2023_12_10T05_55_07.023442 path: - results_2023-12-10T05-55-07.023442.parquet - split: latest path: - results_2023-12-10T05-55-07.023442.parquet --- # Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-FastChat ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat - **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 [kyujinpy/PlatYi-34B-Llama-Q-FastChat](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-FastChat) 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_kyujinpy__PlatYi-34B-Llama-Q-FastChat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T05:55:07.023442](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-FastChat/blob/main/results_2023-12-10T05-55-07.023442.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": 0.7741514926490987, "acc_stderr": 0.027646135380835733, "acc_norm": 0.7828326159595959, "acc_norm_stderr": 0.02814394317924737, "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5362104216200869, "mc2_stderr": 0.01504184962981019 }, "harness|arc:challenge|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042194, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.013830568927974332 }, "harness|hellaswag|10": { "acc": 0.6533559051981677, "acc_stderr": 0.004749286071559569, "acc_norm": 0.8525194184425413, "acc_norm_stderr": 0.003538596773704832 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7555555555555555, "acc_stderr": 0.03712537833614866, "acc_norm": 0.7555555555555555, "acc_norm_stderr": 0.03712537833614866 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.881578947368421, "acc_stderr": 0.026293995855474938, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.026293995855474938 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.82, "acc_stderr": 0.038612291966536955, "acc_norm": 0.82, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8075471698113208, "acc_stderr": 0.024262979839372277, "acc_norm": 0.8075471698113208, "acc_norm_stderr": 0.024262979839372277 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9027777777777778, "acc_stderr": 0.024774516250440182, "acc_norm": 0.9027777777777778, "acc_norm_stderr": 0.024774516250440182 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252606, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.49, "acc_stderr": 0.05024183937956913, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956913 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7283236994219653, "acc_stderr": 0.03391750322321659, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.03391750322321659 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5490196078431373, "acc_stderr": 0.04951218252396262, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.04951218252396262 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7872340425531915, "acc_stderr": 0.02675439134803976, "acc_norm": 0.7872340425531915, "acc_norm_stderr": 0.02675439134803976 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5789473684210527, "acc_stderr": 0.046446020912223177, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7724137931034483, "acc_stderr": 0.03493950380131184, "acc_norm": 0.7724137931034483, "acc_norm_stderr": 0.03493950380131184 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.753968253968254, "acc_stderr": 0.022182037202948365, "acc_norm": 0.753968253968254, "acc_norm_stderr": 0.022182037202948365 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.6031746031746031, "acc_stderr": 0.043758884927270585, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.043758884927270585 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9258064516129032, "acc_stderr": 0.01490952930054621, "acc_norm": 0.9258064516129032, "acc_norm_stderr": 0.01490952930054621 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6847290640394089, "acc_stderr": 0.03269080871970186, "acc_norm": 0.6847290640394089, "acc_norm_stderr": 0.03269080871970186 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.87, "acc_stderr": 0.03379976689896309, "acc_norm": 0.87, "acc_norm_stderr": 0.03379976689896309 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9242424242424242, "acc_stderr": 0.0188526702349931, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.0188526702349931 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527033, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527033 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.823076923076923, "acc_stderr": 0.01934807017439698, "acc_norm": 0.823076923076923, "acc_norm_stderr": 0.01934807017439698 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.4925925925925926, "acc_stderr": 0.0304821923951915, "acc_norm": 0.4925925925925926, "acc_norm_stderr": 0.0304821923951915 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8697478991596639, "acc_stderr": 0.02186325849485212, "acc_norm": 0.8697478991596639, "acc_norm_stderr": 0.02186325849485212 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5496688741721855, "acc_stderr": 0.04062290018683775, "acc_norm": 0.5496688741721855, "acc_norm_stderr": 0.04062290018683775 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9302752293577982, "acc_stderr": 0.010919426411848607, "acc_norm": 0.9302752293577982, "acc_norm_stderr": 0.010919426411848607 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.7222222222222222, "acc_stderr": 0.0305467452649532, 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"harness|hendrycksTest-jurisprudence|5": { "acc": 0.8888888888888888, "acc_stderr": 0.03038159675665167, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.03038159675665167 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6339285714285714, "acc_stderr": 0.04572372358737431, "acc_norm": 0.6339285714285714, "acc_norm_stderr": 0.04572372358737431 }, "harness|hendrycksTest-management|5": { "acc": 0.883495145631068, "acc_stderr": 0.031766839486404054, "acc_norm": 0.883495145631068, "acc_norm_stderr": 0.031766839486404054 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9487179487179487, "acc_stderr": 0.014450181176872736, "acc_norm": 0.9487179487179487, "acc_norm_stderr": 0.014450181176872736 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, 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"acc": 0.8580246913580247, "acc_stderr": 0.019420260109438293, "acc_norm": 0.8580246913580247, "acc_norm_stderr": 0.019420260109438293 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6631205673758865, "acc_stderr": 0.02819553487396673, "acc_norm": 0.6631205673758865, "acc_norm_stderr": 0.02819553487396673 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6186440677966102, "acc_stderr": 0.01240550940188812, "acc_norm": 0.6186440677966102, "acc_norm_stderr": 0.01240550940188812 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8272058823529411, "acc_stderr": 0.022966067585581767, "acc_norm": 0.8272058823529411, "acc_norm_stderr": 0.022966067585581767 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8316993464052288, "acc_stderr": 0.01513580333869338, "acc_norm": 0.8316993464052288, "acc_norm_stderr": 0.01513580333869338 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7181818181818181, "acc_stderr": 0.04309118709946458, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.04309118709946458 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8367346938775511, "acc_stderr": 0.023661699177098615, "acc_norm": 0.8367346938775511, "acc_norm_stderr": 0.023661699177098615 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8706467661691543, "acc_stderr": 0.02372983088101853, "acc_norm": 0.8706467661691543, "acc_norm_stderr": 0.02372983088101853 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.93, "acc_stderr": 0.025643239997624294, "acc_norm": 0.93, "acc_norm_stderr": 0.025643239997624294 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598053, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598053 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015578, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015578 }, "harness|truthfulqa:mc|0": { "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5362104216200869, "mc2_stderr": 0.01504184962981019 }, "harness|winogrande|5": { "acc": 0.8216258879242304, "acc_stderr": 0.010759352014855944 }, "harness|gsm8k|5": { "acc": 0.44351781652767247, "acc_stderr": 0.013684327592606165 } } ``` ### 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]

matlok/python-copilot-training-on-ai-research-repos

--- license: - other pretty_name: >- python copilot ai research coding dataset dataset_info: - config_name: view_schema splits: - name: view_schema configs: - config_name: view_schema data_files: - split: view_schema path: files/lok-python-code-ai-core-v1_00000002.parquet size_categories: - 100K<n<1M tags: - python-copilot - python-coding - fine-tuning - training - alpaca - text - coding # supported task_categories # text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, conversational, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, other task_categories: - text-generation # supported task_ids # acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering task_ids: - parsing --- ## Python Copilot AI Research Coding Dataset This dataset is a subset of the matlok python copilot datasets. Please refer to the [Multimodal Python Copilot Training Overview](https://huggingface.co/datasets/matlok/multimodal-python-copilot-training-overview) for more details on how to use this dataset. ### Details Each row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the code), and more. - Rows: 514430 - Size: 674 MB - Data type: text - Format: Extracted code using python AST ### Schema ```json { "args": "string", "class_bases": "string", "class_docstr": "string", "class_docstr_tok": "string", "class_name": "string", "code": "string", "code_tok": "string", "docstr": "string", "docstr_tok": "string", "file_path": "string", "filename": "string", "imports": "string", "is_member": "bool", "label_desc": "string", "label_desc_len": "int64", "label_id": "string", "lend": "int64", "lstart": "int64", "name": "string", "num_all_bases": "float64", "num_bases": "float64", "num_classes": "float64", "num_functions": "int64", "num_imports": "int64", "num_methods": "float64", "raises": "string", "returns": "string", "total_objects": "int64" } ``` ### How to use the dataset ```python from datasets import load_dataset ds = load_dataset("matlok/python-copilot-training-on-ai-research-repos", data_dir="files") ```

Falah/real_military_machinery_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 39077069 num_examples: 100000 download_size: 4335632 dataset_size: 39077069 --- # Dataset Card for "real_military_machinery_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

umm-maybe/ai_images

--- 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': train_dataset - name: text dtype: string splits: - name: train num_bytes: 540439882.0 num_examples: 304 download_size: 540208895 dataset_size: 540439882.0 --- # Dataset Card for "ai_images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/uomi_chihiro_seitokaiyakuindomo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Uomi Chihiro (Seitokai Yakuindomo) This is the dataset of Uomi Chihiro (Seitokai Yakuindomo), containing 208 images and their tags. The core tags of this character are `black_hair, twintails, long_hair, low_twintails, black_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 208 | 93.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 208 | 80.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 421 | 158.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 208 | 93.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 421 | 177.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/uomi_chihiro_seitokaiyakuindomo/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/uomi_chihiro_seitokaiyakuindomo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, school_uniform, solo, upper_body, cardigan, purple_eyes, white_shirt, smile, closed_mouth, red_necktie, collared_shirt, jacket, bangs | | 1 | 21 | ![](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, necktie, school_uniform, solo, cardigan, skirt | | 2 | 5 | ![](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, profile, school_uniform, solo, collared_shirt, from_side, blazer, closed_mouth, outdoors, red_necktie, upper_body, white_shirt, bangs, blue_eyes, blurry_background, depth_of_field | | 3 | 13 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, solo, twin_braids, anime_coloring, hair_over_shoulder, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | school_uniform | solo | upper_body | cardigan | purple_eyes | white_shirt | smile | closed_mouth | red_necktie | collared_shirt | jacket | bangs | necktie | skirt | profile | from_side | blazer | outdoors | blue_eyes | blurry_background | depth_of_field | twin_braids | anime_coloring | hair_over_shoulder | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------|:-------------|:-----------|:--------------|:--------------|:--------|:---------------|:--------------|:-----------------|:---------|:--------|:----------|:--------|:----------|:------------|:---------|:-----------|:------------|:--------------------|:-----------------|:--------------|:-----------------|:---------------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 1 | 21 | ![](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 | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | | X | | X | X | X | | X | | | X | X | X | X | X | X | X | | | | | 3 | 13 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | X | | | | | | | | | | | | | | | | | | | X | X | X |

Beluuuuuuga/Japanese-Instruction-Linux-Command-169

--- license: cc-by-nc-4.0 task_categories: - question-answering language: - ja size_categories: - n<1K ---

argilla/news-fakenews

--- language: - en size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification dataset_info: features: - name: text dtype: string - name: inputs struct: - name: text dtype: string - name: prediction list: - name: label dtype: string - name: score dtype: float64 - name: prediction_agent dtype: string - name: annotation dtype: 'null' - name: annotation_agent dtype: 'null' - name: multi_label dtype: bool - name: explanation dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: text_length dtype: int64 splits: - name: train num_bytes: 227222498 num_examples: 44898 download_size: 138350597 dataset_size: 227222498 --- # Dataset Card for "news-fakenews" ## Dataset Description - **Homepage:** Kaggle Challenge - **Repository:** https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset?select=True.csv - **Paper:** N.A. - **Leaderboard:** N.A. - **Point of Contact:** N.A. ### Dataset Summary Can you use this data set to make an algorithm able to determine if an article is fake news or not ? ### Languages english ### Citation Information Acknowledgements Ahmed H, Traore I, Saad S. “Detecting opinion spams and fake news using text classification”, Journal of Security and Privacy, Volume 1, Issue 1, Wiley, January/February 2018. Ahmed H, Traore I, Saad S. (2017) “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore I., Woungang I., Awad A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science, vol 10618. Springer, Cham (pp. 127-138). ### Contributions Thanks to [@davidberenstein1957](https://github.com/davidberenstein1957) for adding this dataset.

roman_urdu_hate_speech

--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - ur license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: roman_urdu_hate_speech tags: - binary classification dataset_info: - config_name: Coarse_Grained features: - name: tweet dtype: string - name: label dtype: class_label: names: '0': Abusive/Offensive '1': Normal splits: - name: train num_bytes: 725719 num_examples: 7208 - name: test num_bytes: 218087 num_examples: 2002 - name: validation num_bytes: 79759 num_examples: 800 download_size: 927937 dataset_size: 1023565 - config_name: Fine_Grained features: - name: tweet dtype: string - name: label dtype: class_label: names: '0': Abusive/Offensive '1': Normal '2': Religious Hate '3': Sexism '4': Profane/Untargeted splits: - name: train num_bytes: 723670 num_examples: 7208 - name: test num_bytes: 219359 num_examples: 2002 - name: validation num_bytes: 723670 num_examples: 7208 download_size: 1519423 dataset_size: 1666699 --- # Dataset Card for roman_urdu_hate_speech ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [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:** [roman_urdu_hate_speech homepage](https://aclanthology.org/2020.emnlp-main.197/) - **Repository:** [roman_urdu_hate_speech repository](https://github.com/haroonshakeel/roman_urdu_hate_speech) - **Paper:** [Hate-Speech and Offensive Language Detection in Roman Urdu](https://aclanthology.org/2020.emnlp-main.197.pdf) - **Leaderboard:** [N/A] - **Point of Contact:** [M. Haroon Shakeel](mailto:m.shakeel@lums.edu.pk) ### Dataset Summary The Roman Urdu Hate-Speech and Offensive Language Detection (RUHSOLD) dataset is a Roman Urdu dataset of tweets annotated by experts in the relevant language. The authors develop the gold-standard for two sub-tasks. First sub-task is based on binary labels of Hate-Offensive content and Normal content (i.e., inoffensive language). These labels are self-explanatory. The authors refer to this sub-task as coarse-grained classification. Second sub-task defines Hate-Offensive content with four labels at a granular level. These labels are the most relevant for the demographic of users who converse in RU and are defined in related literature. The authors refer to this sub-task as fine-grained classification. The objective behind creating two gold-standards is to enable the researchers to evaluate the hate speech detection approaches on both easier (coarse-grained) and challenging (fine-grained) scenarios. ### Supported Tasks and Leaderboards - 'multi-class-classification', 'text-classification-other-binary classification': The dataset can be used for both multi class classification as well as for binary classification as it contains both coarse grained and fine grained labels. ### Languages The text of this dataset is Roman Urdu. The associated BCP-47 code is 'ur'. ## Dataset Structure ### Data Instances The dataset consists of two parts divided as a set of two types, Coarse grained examples and Fine Grained examples. The difference is that in the coarse grained example the tweets are labelled as abusive or normal whereas in the fine grained version there are several classes of hate associated with a tweet. For the Coarse grained segment of the dataset the label mapping is:- Task 1: Coarse-grained Classification Labels 0: Abusive/Offensive 1: Normal Whereas for the Fine Grained segment of the dataset the label mapping is:- Task 2: Fine-grained Classification Labels 0: Abusive/Offensive 1: Normal 2: Religious Hate 3: Sexism 4: Profane/Untargeted An example from Roman Urdu Hate Speech looks as follows: ``` { 'tweet': 'there are some yahodi daboo like imran chore zakat khore' 'label': 0 } ``` ### Data Fields -tweet:a string denoting the tweet which has been selected by using a random sampling from a tweet base of 50000 tweets to select 10000 tweets and annotated for the dataset. -label:An annotation manually labeled by three independent annotators, during the annotation process, all conflicts are resolved by a majority vote among three annotators. ### Data Splits The data of each of the segments, Coarse Grained and Fine Grained is further split into training, validation and test set. The data is split in train, test, and validation sets with 70,20,10 split ratio using stratification based on fine-grained labels. The use of stratified sampling is deemed necessary to preserve the same labels ratio across all splits. The Final split sizes are as follows: Train Valid Test 7209 2003 801 ## 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 The dataset was created by Hammad Rizwan, Muhammad Haroon Shakeel, Asim Karim during work done at Department of Computer Science, Lahore University of Management Sciences (LUMS), Lahore, Pakistan. ### Licensing Information The licensing status of the dataset hinges on the legal status of the [Roman Urdu Hate Speech Dataset Repository](https://github.com/haroonshakeel/roman_urdu_hate_speech) which is under MIT License. ### Citation Information ```bibtex @inproceedings{rizwan2020hate, title={Hate-speech and offensive language detection in roman Urdu}, author={Rizwan, Hammad and Shakeel, Muhammad Haroon and Karim, Asim}, booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, pages={2512--2522}, year={2020} } ``` ### Contributions Thanks to [@bp-high](https://github.com/bp-high), for adding this dataset.

emaeon/train7

--- dataset_info: features: - name: code1 dtype: string - name: code2 dtype: string - name: similar dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 9013855023 num_examples: 5000000 download_size: 4017642295 dataset_size: 9013855023 --- # Dataset Card for "train7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Nexdata/British_English_Average_Tone_Speech_Synthesis_Corpus

--- task_categories: - text-to-speech language: - en --- # Dataset Card for Nexdata/British_English_Average_Tone_Speech_Synthesis_Corpus ## Description 10 People - British English Average Tone Speech Synthesis Corpus. It is recorded by British English native speakers, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis. For more details, please refer to the link: https://www.nexdata.ai/datasets/1309?source=Huggingface # Specifications ## Format 48,000Hz, 24bit, uncompressed wav, mono channel; ## Recording environment professional recording studio; ## Recording content general narrative sentences, interrogative sentences, etc; ## Speaker british native speaker, 5 male and 5 female, 2 hours per person; ## Device microphone; ## Language British English; ## Annotation word and phoneme transcription, four-level prosodic boundary annotation; ## Application scenarios speech synthesis. # Licensing Information Commercial License

irds/wikir_en59k

--- pretty_name: '`wikir/en59k`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `wikir/en59k` The `wikir/en59k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/wikir#wikir/en59k). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=2,454,785 ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/wikir_en59k', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Frej2020Wikir, title={WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={LREC}, year={2020} } @inproceedings{Frej2020MlWikir, title={MLWIKIR: A Python Toolkit for Building Large-scale Wikipedia-based Information Retrieval Datasets in Chinese, English, French, Italian, Japanese, Spanish and More}, author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet}, booktitle={CIRCLE}, year={2020} } ```

aarnow/auditory-skills-test

--- language: - en license: mit dataset_info: features: - name: label dtype: string - name: text dtype: string splits: - name: train num_bytes: 16536 num_examples: 178 download_size: 7410 dataset_size: 16536 configs: - config_name: default data_files: - split: train path: data/train-* --- The purpose of this project is to build a dataset and model to enable an AI powered diagnostic tool that assesses a child's auditory skills and recommends resources and therapies that can bring them to the next stage. The primary user base of this tool is intended to be the parents of a child with hearing loss however it is the hope of the creators of this tool that speech and language pathologists (SLPs) and other early intervention and pediatric practitioners can find use. The model uses a natural language processing (NLP) model for text-classification and converts free text inputted by the parent of a child with hearing loss into 1 of 4 clinical categories: DETECTION, DISCRIMINATION, IDENTIFICATION, CLASSIFICATION. Based on the classification of the child against a given skill a recommendation is made for therapies that can be used to improve the child's competency against a given skill. The value of this approach is that each child is challenged to build upon existing skills while not being given any task too difficult that will result in discouragement.

Ga88/Clovis8

--- license: openrail ---

reglab/land-app-trial

--- license: cc-by-4.0 task_categories: - object-detection language: - en tags: - agriculture - environment size_categories: - 1K<n<10K --- # Land application field trial data ### Intro This dataset is a repository of results from our Land Application Detection Model trial with two organizations. Land application is the process of disposing of agricultural animal waste by spraying it onto fields. [We developed a model](https://github.com/reglab/land-application-detection?tab=readme-ov-file) to detect these practices. This dataset represents the results of a real world trial to verify and label these detected spreads. ### Data description #### Structured data - sent_to_wdnr.csv - Each row is a detected spread that we forwarded to our partners at WDNR - sent_to_elpc.csv - Each row is a detected spread that we forwarded to our partners at ELPC - wdnr_responses.csv - Each row is a response to a detection from sent_to_wdnr.csv which contains a preliminary determination by WDNR staff as to whether the image looks like a spread and if it was determined to be likely spreading, the results of an investigation into said spread. - elpc_responses_raw.csv - Each row is a response to a detection from sent_to_elpc.csv which is the results of the ELPC investigation into that detection through the use of citizen volunteers verifiying in person. - elpc_responses_clean.csv - Same as the raw file but with corrected detection ids to deal with a data entry error. #### Image data - images/ - This directory contains .jpeg images of satellite data fed into the model that were sent to either of the partners. Images were captured by [Planet](https://www.planet.com/) using the PlanetScope sensor, visual spectrum 3m images. ## Citation `@misc {stanford_regulation,_evaluation,_and_governance_lab_2024, author = { {Stanford Regulation, Evaluation, and Governance Lab} }, title = { land-app-trial (Revision b3d0e11) }, year = 2024, url = { https://huggingface.co/datasets/reglab/land-app-trial }, doi = { 10.57967/hf/1733 }, publisher = { Hugging Face } }`

zolak/twitter_dataset_79_1713209820

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 1387710 num_examples: 3432 download_size: 680273 dataset_size: 1387710 configs: - config_name: default data_files: - split: train path: data/train-* ---

distilled-one-sec-cv12-each-chunk-uniq/chunk_16

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1243273188.0 num_examples: 242259 download_size: 1270215878 dataset_size: 1243273188.0 --- # Dataset Card for "chunk_16" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

distilled-from-one-sec-cv12/chunk_140

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1147381768 num_examples: 223574 download_size: 1170376996 dataset_size: 1147381768 --- # Dataset Card for "chunk_140" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

roydcarlson/dirt_teff2

--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 6436424.0 num_examples: 7 download_size: 6352411 dataset_size: 6436424.0 --- # Dataset Card for "dirt_teff2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tollefj/big-bang-theory-splits-removal

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: en dtype: string - name: 'no' dtype: string splits: - name: train num_bytes: 8082 num_examples: 60 - name: test num_bytes: 3499 num_examples: 27 download_size: 12705 dataset_size: 11581 --- # Dataset Card for "big-bang-theory-splits-removal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

irds/mr-tydi_th_test

--- pretty_name: '`mr-tydi/th/test`' viewer: false source_datasets: ['irds/mr-tydi_th'] task_categories: - text-retrieval --- # Dataset Card for `mr-tydi/th/test` The `mr-tydi/th/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/th/test). # Data This dataset provides: - `queries` (i.e., topics); count=1,190 - `qrels`: (relevance assessments); count=1,368 - For `docs`, use [`irds/mr-tydi_th`](https://huggingface.co/datasets/irds/mr-tydi_th) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/mr-tydi_th_test', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/mr-tydi_th_test', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Zhang2021MrTyDi, title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval}, author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin}, year={2021}, journal={arXiv:2108.08787}, } @article{Clark2020TyDiQa, title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages}, author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}, year={2020}, journal={Transactions of the Association for Computational Linguistics} } ```

LambdaTests/VQAv2_sample_validation_benchmarks_partition_global_7_loca_7

--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 13 num_examples: 1 download_size: 0 dataset_size: 13 --- # Dataset Card for "VQAv2_sample_validation_benchmarks_partition_global_7_loca_7" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/1cc7040b

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 178 num_examples: 10 download_size: 1340 dataset_size: 178 --- # Dataset Card for "1cc7040b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

SahilSN/DataSet_v3

--- license: unknown dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11291 num_examples: 50 download_size: 6939 dataset_size: 11291 configs: - config_name: default data_files: - split: train path: data/train-* ---

banloada/ban

--- license: other ---

OzoneAsai/factorExpander

--- license: wtfpl task_categories: - conversational language: - ja --- # Polynomial Expansion and Factoring Dataset This dataset contains problem and solution pairs for polynomial expansion and factoring. Each problem is a result of expanding and factoring the `(x + n)^2` form expression, where `n` takes values from -1000 to 1000. ## Dataset Structure - `factorized_dataset.csv`: CSV file containing the dataset. - `README.md`: This file that provides an overview and usage instructions for the dataset. ## Data Format The CSV file of the dataset includes the following columns: - `Instruction`: A description of the problem. It indicates the expression that should be expanded or factored. - `Output`: The answer expression. ## Sample Data | Instruction | Output | |-----------------------------------|--------------------| | 式を展開せよ: (x - 1000)**2 | x**2 - 2000*x + 1000000 | ... ## Dataset Information - Number of samples: 2002 (due to the range of n from -1000 to 1000) - Dataset format: CSV file # 数式の展開と因数分解データセットこのデータセットは、数式の展開と因数分解の問題と解答のペアを含んでいます。各問題は、`sympy`ライブラリを使用して、`(x + n)^2`形式の数式を展開し、因数分解した結果です。 ## データセットの構造 - `factorized_dataset.csv`: データセットが格納されたCSVファイルです。 - `README.md`: このファイルで、データセットの概要と使用方法を説明します。 ## データのフォーマットデータセットのCSVファイルには、以下のカラムが含まれています： - `Instruction`: 問題の説明文です。展開または因数分解を行うべき数式が記載されています。 - `Output`: 解答の数式が記載されています。 ## データのサンプル | Instruction | Output | |-----------------------------------|--------------------| | 式を展開せよ: (x - 1000)**2 | x**2 - 2000*x + 1000000 | ... ## データセットの情報 - データのサンプル数: 2002 (nの値を-1000から1000まで総当たりしたため) - データセットのフォーマット: CSVファイル

ingTikna/Prolog_Dataset

--- license: mit ---

SF-Corpus/EF_Supersense_Tags

--- language: - en pretty_name: sf-nexus-ef-supsersense-tags --- # Dataset Card for SF Nexus Extracted Features: Named Entities ## Dataset Description - **Homepage: https://sfnexus.io/** - **Repository: https://github.com/SF-Nexus/extracted-features-notebooks** - **Point of Contact: Alex Wermer-Colan** ### Dataset Summary The SF Nexus EF Supersense Tags dataset contains supersense tags generated from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection. After digitization, the books were cleaned using Abbyy FineReader. The dataframes in this repository were generated using BookNLP and contain information about the "supersense tags" in the texts. ### About the SF Nexus Corpus The Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980). The digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's [Temple page](https://babel.hathitrust.org/cgi/ls?field1=ocr;q1=%2A;a=srchls;facet=htsource%3A%22Temple%20University%22;pn=4) for non-consumptive research. For more information on the project to digitize and curate a corpus of "New Wave" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, ["Building a New Wave Science Fiction Corpus."](https://sites.temple.edu/tudsc/2017/12/20/building-new-wave-science-fiction-corpus/). ### Languages English ## Dataset Structure This dataset contains 403 csv files containing information about the named entities in each text in the SF corpus. For example: ``` First line of dataframe: 1908_HODGSON_THEHOUSEONTHEBORDERLAND.txt.supersense.csv {'start_token': 4, 'end_token': 4 'supersense_category': noun.location 'text': 'Borderland', } ``` ### Data Fields - **start_token: int** The start token of entity name - **end_token: int** The end token of the entity name; same as the start token for one-word entites; increase by one for each additional word that is part of the token - **supersense_category: str** The part of speech and category to which the text belongs - **text: str** The text corresponding to the supersense tag ### Loading the Dataset Use the following code to load the dataset in a Python environment (note: does not work with repo set to private) ``` from datasets import load_dataset # If the dataset is gated/private, make sure you have run huggingface-cli login dataset = load_dataset("SF-Corpus/EF_Supersense_Tags") ``` Or just clone the dataset repo ``` git lfs install git clone https://huggingface.co/datasets/SF-Corpus/EF_Supersense_Tags # if you want to clone without large files – just their pointers # prepend your git clone with the following env var: GIT_LFS_SKIP_SMUDGE=1 ``` ## Dataset Creation ### Curation Rationale For an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022) ### Source Data The Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well. ## Considerations for Using the Data This data card only exhibits extracted features for copyrighted fiction; no copyrighted work is being made available for consumption. These digitized files are made accessible for purposes of education and research. Temple University Libraries have given attribution to rights holders when possible. If you hold the rights to materials in our digitized collections that are unattributed, please let us know so that we may maintain accurate information about these materials. If you are a rights holder and are concerned that you have found material on this website for which you have not granted permission (or is not covered by a copyright exception under US copyright laws), you may request the removal of the material from our site by writing to digitalscholarship@temple.edu. For more information on non-consumptive research, check out HathiTrust Research Center’s Non-Consumptive Use Research Policy. ## Additional Information ### Dataset Curators For a full list of conributors to the SF Nexus project, visit [https://sfnexus.io/people/](https://sfnexus.io/people/).

carnival13/massive_eng_DA_tokenized

--- dataset_info: features: - name: pass_label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 97244320 num_examples: 138200 download_size: 22020759 dataset_size: 97244320 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "massive_eng_DA_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xl_mode_A_ns_6149

--- 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: 2470439 num_examples: 6149 download_size: 269782 dataset_size: 2470439 --- # Dataset Card for "OxfordFlowers_test_google_flan_t5_xl_mode_A_ns_6149" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Vikhrmodels/habr_qa_sbs

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: best dtype: string - name: bad dtype: string splits: - name: train num_bytes: 119263751 num_examples: 102558 download_size: 66726288 dataset_size: 119263751 license: apache-2.0 task_categories: - question-answering - text-generation language: - ru tags: - code - finance pretty_name: habr_qa_sbs size_categories: - 10K<n<100K --- # Habr sbs qa Датасет основан на сайте habr qa, лучший ответ - тот на котором есть лайки, худший - тот на котором меньше всего лайков. Датасет собран [Love.Death.Transformers.](https://t.me/lovedeathtransformers) и [Дата-Утренник](https://t.me/data_morning) [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_wnli_indef_one

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 5997 num_examples: 29 - name: test num_bytes: 27482 num_examples: 94 - name: train num_bytes: 40847 num_examples: 195 download_size: 32547 dataset_size: 74326 --- # Dataset Card for "MULTI_VALUE_wnli_indef_one" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2

--- pretty_name: Evaluation run of Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2](https://huggingface.co/Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-04T20:26:53.463273](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2/blob/main/results_2024-02-04T20-26-53.463273.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\": 0.2605954222902013,\n\ \ \"acc_stderr\": 0.030887287206153434,\n \"acc_norm\": 0.2609822344299048,\n\ \ \"acc_norm_stderr\": 0.031636108991043924,\n \"mc1\": 0.22643818849449204,\n\ \ \"mc1_stderr\": 0.014651337324602574,\n \"mc2\": 0.372644846918848,\n\ \ \"mc2_stderr\": 0.014009270688888235\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.29436860068259385,\n \"acc_stderr\": 0.013318528460539422,\n\ \ \"acc_norm\": 0.32764505119453924,\n \"acc_norm_stderr\": 0.013715847940719346\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4347739494124676,\n\ \ \"acc_stderr\": 0.004947141797384123,\n \"acc_norm\": 0.5791674965146385,\n\ \ \"acc_norm_stderr\": 0.004926837572202166\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\": 0.24444444444444444,\n\ \ \"acc_stderr\": 0.03712537833614866,\n \"acc_norm\": 0.24444444444444444,\n\ \ \"acc_norm_stderr\": 0.03712537833614866\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.03317672787533157,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.03317672787533157\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.33,\n\ \ \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \ \ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2679245283018868,\n \"acc_stderr\": 0.027257260322494845,\n\ \ \"acc_norm\": 0.2679245283018868,\n \"acc_norm_stderr\": 0.027257260322494845\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2152777777777778,\n\ \ \"acc_stderr\": 0.03437079344106134,\n \"acc_norm\": 0.2152777777777778,\n\ \ \"acc_norm_stderr\": 0.03437079344106134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\"\ : 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.23699421965317918,\n\ \ \"acc_stderr\": 0.03242414757483099,\n \"acc_norm\": 0.23699421965317918,\n\ \ \"acc_norm_stderr\": 0.03242414757483099\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.3191489361702128,\n \"acc_stderr\": 0.030472973363380045,\n\ \ \"acc_norm\": 0.3191489361702128,\n \"acc_norm_stderr\": 0.030472973363380045\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\ \ \"acc_stderr\": 0.04096985139843672,\n \"acc_norm\": 0.2543859649122807,\n\ \ \"acc_norm_stderr\": 0.04096985139843672\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n\ \ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"\ acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.20634920634920634,\n\ \ \"acc_stderr\": 0.036196045241242515,\n \"acc_norm\": 0.20634920634920634,\n\ \ \"acc_norm_stderr\": 0.036196045241242515\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24838709677419354,\n\ \ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.24838709677419354,\n\ \ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.2660098522167488,\n \"acc_stderr\": 0.03108982600293753,\n\ \ \"acc_norm\": 0.2660098522167488,\n \"acc_norm_stderr\": 0.03108982600293753\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.21717171717171718,\n \"acc_stderr\": 0.029376616484945637,\n \"\ acc_norm\": 0.21717171717171718,\n \"acc_norm_stderr\": 0.029376616484945637\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.24870466321243523,\n \"acc_stderr\": 0.03119584087770031,\n\ \ \"acc_norm\": 0.24870466321243523,\n \"acc_norm_stderr\": 0.03119584087770031\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2846153846153846,\n \"acc_stderr\": 0.022878322799706287,\n\ \ \"acc_norm\": 0.2846153846153846,\n \"acc_norm_stderr\": 0.022878322799706287\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275805,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275805\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.24789915966386555,\n \"acc_stderr\": 0.028047967224176896,\n\ \ \"acc_norm\": 0.24789915966386555,\n \"acc_norm_stderr\": 0.028047967224176896\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2052980132450331,\n \"acc_stderr\": 0.03297986648473834,\n \"\ acc_norm\": 0.2052980132450331,\n \"acc_norm_stderr\": 0.03297986648473834\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.23302752293577983,\n \"acc_stderr\": 0.0181256691808615,\n \"\ acc_norm\": 0.23302752293577983,\n \"acc_norm_stderr\": 0.0181256691808615\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.3425925925925926,\n \"acc_stderr\": 0.03236585252602158,\n \"\ acc_norm\": 0.3425925925925926,\n \"acc_norm_stderr\": 0.03236585252602158\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.2911392405063291,\n \"acc_stderr\": 0.029571601065753374,\n\ \ \"acc_norm\": 0.2911392405063291,\n \"acc_norm_stderr\": 0.029571601065753374\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.37668161434977576,\n\ \ \"acc_stderr\": 0.032521134899291884,\n \"acc_norm\": 0.37668161434977576,\n\ \ \"acc_norm_stderr\": 0.032521134899291884\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.21374045801526717,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.21374045801526717,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.23140495867768596,\n \"acc_stderr\": 0.03849856098794088,\n \"\ acc_norm\": 0.23140495867768596,\n \"acc_norm_stderr\": 0.03849856098794088\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.23148148148148148,\n\ \ \"acc_stderr\": 0.04077494709252628,\n \"acc_norm\": 0.23148148148148148,\n\ \ \"acc_norm_stderr\": 0.04077494709252628\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.3312883435582822,\n \"acc_stderr\": 0.03697983910025588,\n\ \ \"acc_norm\": 0.3312883435582822,\n \"acc_norm_stderr\": 0.03697983910025588\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.22321428571428573,\n\ \ \"acc_stderr\": 0.039523019677025116,\n \"acc_norm\": 0.22321428571428573,\n\ \ \"acc_norm_stderr\": 0.039523019677025116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n\ \ \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.24358974358974358,\n\ \ \"acc_stderr\": 0.02812096650391441,\n \"acc_norm\": 0.24358974358974358,\n\ \ \"acc_norm_stderr\": 0.02812096650391441\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2835249042145594,\n\ \ \"acc_stderr\": 0.016117318166832283,\n \"acc_norm\": 0.2835249042145594,\n\ \ \"acc_norm_stderr\": 0.016117318166832283\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n\ \ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.24183006535947713,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.24183006535947713,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2861736334405145,\n\ \ \"acc_stderr\": 0.025670259242188947,\n \"acc_norm\": 0.2861736334405145,\n\ \ \"acc_norm_stderr\": 0.025670259242188947\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2654320987654321,\n \"acc_stderr\": 0.024569223600460845,\n\ \ \"acc_norm\": 0.2654320987654321,\n \"acc_norm_stderr\": 0.024569223600460845\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.23049645390070922,\n \"acc_stderr\": 0.025123739226872395,\n \ \ \"acc_norm\": 0.23049645390070922,\n \"acc_norm_stderr\": 0.025123739226872395\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2438070404172099,\n\ \ \"acc_stderr\": 0.010966507972178475,\n \"acc_norm\": 0.2438070404172099,\n\ \ \"acc_norm_stderr\": 0.010966507972178475\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.025767252010855963,\n\ \ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.025767252010855963\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.32727272727272727,\n \"acc_stderr\": 0.044942908662520896,\n\ \ \"acc_norm\": 0.32727272727272727,\n \"acc_norm_stderr\": 0.044942908662520896\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.17959183673469387,\n\ \ \"acc_stderr\": 0.024573293589585637,\n \"acc_norm\": 0.17959183673469387,\n\ \ \"acc_norm_stderr\": 0.024573293589585637\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.21393034825870647,\n \"acc_stderr\": 0.028996909693328927,\n\ \ \"acc_norm\": 0.21393034825870647,\n \"acc_norm_stderr\": 0.028996909693328927\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.3253012048192771,\n \"acc_stderr\": 0.03647168523683227,\n\ \ \"acc_norm\": 0.3253012048192771,\n \"acc_norm_stderr\": 0.03647168523683227\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.21052631578947367,\n\ \ \"acc_stderr\": 0.031267817146631786,\n \"acc_norm\": 0.21052631578947367,\n\ \ \"acc_norm_stderr\": 0.031267817146631786\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.22643818849449204,\n \"mc1_stderr\": 0.014651337324602574,\n\ \ \"mc2\": 0.372644846918848,\n \"mc2_stderr\": 0.014009270688888235\n\ \ },\n \"harness|winogrande|5\": {\n \"acc\": 0.6479873717442778,\n\ \ \"acc_stderr\": 0.013422874824929714\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.028051554207733132,\n \"acc_stderr\": 0.004548229533836337\n\ \ }\n}\n```" repo_url: https://huggingface.co/Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2 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_04T18_17_11.697806 path: - '**/details_harness|arc:challenge|25_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|arc:challenge|25_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|arc:challenge|25_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-04T20-26-53.463273.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|gsm8k|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|gsm8k|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|gsm8k|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hellaswag|10_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hellaswag|10_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hellaswag|10_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T18-17-11.697806.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T18-17-11.697806.parquet' - 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'**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T18-52-11.664162.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T18-52-11.664162.parquet' - 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'**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-04T20-26-53.463273.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-management|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-management|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-management|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-04T20-26-53.463273.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|truthfulqa:mc|0_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|truthfulqa:mc|0_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|truthfulqa:mc|0_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-04T20-26-53.463273.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_04T18_17_11.697806 path: - '**/details_harness|winogrande|5_2024-02-04T18-17-11.697806.parquet' - split: 2024_02_04T18_52_11.664162 path: - '**/details_harness|winogrande|5_2024-02-04T18-52-11.664162.parquet' - split: 2024_02_04T20_26_53.463273 path: - '**/details_harness|winogrande|5_2024-02-04T20-26-53.463273.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-04T20-26-53.463273.parquet' - config_name: results data_files: - split: 2024_02_04T18_17_11.697806 path: - results_2024-02-04T18-17-11.697806.parquet - split: 2024_02_04T18_52_11.664162 path: - results_2024-02-04T18-52-11.664162.parquet - split: 2024_02_04T20_26_53.463273 path: - results_2024-02-04T20-26-53.463273.parquet - split: latest path: - results_2024-02-04T20-26-53.463273.parquet --- # Dataset Card for Evaluation run of Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2  Dataset automatically created during the evaluation run of model [Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2](https://huggingface.co/Josephgflowers/Tinyllama-1.3B-Cinder-Reason-Test-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-04T20:26:53.463273](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__Tinyllama-1.3B-Cinder-Reason-Test-2/blob/main/results_2024-02-04T20-26-53.463273.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": 0.2605954222902013, "acc_stderr": 0.030887287206153434, "acc_norm": 0.2609822344299048, "acc_norm_stderr": 0.031636108991043924, "mc1": 0.22643818849449204, "mc1_stderr": 0.014651337324602574, "mc2": 0.372644846918848, "mc2_stderr": 0.014009270688888235 }, "harness|arc:challenge|25": { "acc": 0.29436860068259385, "acc_stderr": 0.013318528460539422, "acc_norm": 0.32764505119453924, "acc_norm_stderr": 0.013715847940719346 }, "harness|hellaswag|10": { "acc": 0.4347739494124676, "acc_stderr": 0.004947141797384123, "acc_norm": 0.5791674965146385, "acc_norm_stderr": 0.004926837572202166 }, "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": 0.24444444444444444, "acc_stderr": 0.03712537833614866, "acc_norm": 0.24444444444444444, "acc_norm_stderr": 0.03712537833614866 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03317672787533157, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03317672787533157 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2679245283018868, "acc_stderr": 0.027257260322494845, "acc_norm": 0.2679245283018868, "acc_norm_stderr": 0.027257260322494845 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2152777777777778, "acc_stderr": 0.03437079344106134, "acc_norm": 0.2152777777777778, "acc_norm_stderr": 0.03437079344106134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.23699421965317918, "acc_stderr": 0.03242414757483099, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483099 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179961, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179961 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3191489361702128, "acc_stderr": 0.030472973363380045, "acc_norm": 0.3191489361702128, "acc_norm_stderr": 0.030472973363380045 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2543859649122807, "acc_stderr": 0.04096985139843672, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.04096985139843672 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2689655172413793, "acc_stderr": 0.03695183311650232, "acc_norm": 0.2689655172413793, "acc_norm_stderr": 0.03695183311650232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2566137566137566, "acc_stderr": 0.022494510767503154, "acc_norm": 0.2566137566137566, "acc_norm_stderr": 0.022494510767503154 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.20634920634920634, "acc_stderr": 0.036196045241242515, "acc_norm": 0.20634920634920634, "acc_norm_stderr": 0.036196045241242515 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.03861229196653695, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24838709677419354, "acc_stderr": 0.024580028921481003, "acc_norm": 0.24838709677419354, "acc_norm_stderr": 0.024580028921481003 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2660098522167488, "acc_stderr": 0.03108982600293753, "acc_norm": 0.2660098522167488, "acc_norm_stderr": 0.03108982600293753 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.21717171717171718, "acc_stderr": 0.029376616484945637, "acc_norm": 0.21717171717171718, "acc_norm_stderr": 0.029376616484945637 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.24870466321243523, "acc_stderr": 0.03119584087770031, "acc_norm": 0.24870466321243523, "acc_norm_stderr": 0.03119584087770031 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2846153846153846, "acc_stderr": 0.022878322799706287, "acc_norm": 0.2846153846153846, "acc_norm_stderr": 0.022878322799706287 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275805, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275805 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.24789915966386555, "acc_stderr": 0.028047967224176896, "acc_norm": 0.24789915966386555, "acc_norm_stderr": 0.028047967224176896 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2052980132450331, "acc_stderr": 0.03297986648473834, "acc_norm": 0.2052980132450331, "acc_norm_stderr": 0.03297986648473834 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.23302752293577983, "acc_stderr": 0.0181256691808615, "acc_norm": 0.23302752293577983, "acc_norm_stderr": 0.0181256691808615 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3425925925925926, "acc_stderr": 0.03236585252602158, "acc_norm": 0.3425925925925926, "acc_norm_stderr": 0.03236585252602158 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2911392405063291, "acc_stderr": 0.029571601065753374, "acc_norm": 0.2911392405063291, "acc_norm_stderr": 0.029571601065753374 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.37668161434977576, "acc_stderr": 0.032521134899291884, "acc_norm": 0.37668161434977576, "acc_norm_stderr": 0.032521134899291884 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.21374045801526717, "acc_stderr": 0.0359546161177469, "acc_norm": 0.21374045801526717, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.23140495867768596, "acc_stderr": 0.03849856098794088, "acc_norm": 0.23140495867768596, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.23148148148148148, "acc_stderr": 0.04077494709252628, "acc_norm": 0.23148148148148148, "acc_norm_stderr": 0.04077494709252628 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.3312883435582822, "acc_stderr": 0.03697983910025588, "acc_norm": 0.3312883435582822, "acc_norm_stderr": 0.03697983910025588 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.22321428571428573, "acc_stderr": 0.039523019677025116, "acc_norm": 0.22321428571428573, "acc_norm_stderr": 0.039523019677025116 }, "harness|hendrycksTest-management|5": { "acc": 0.2524271844660194, "acc_stderr": 0.04301250399690877, "acc_norm": 0.2524271844660194, "acc_norm_stderr": 0.04301250399690877 }, "harness|hendrycksTest-marketing|5": { "acc": 0.24358974358974358, "acc_stderr": 0.02812096650391441, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.02812096650391441 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2835249042145594, "acc_stderr": 0.016117318166832283, "acc_norm": 0.2835249042145594, "acc_norm_stderr": 0.016117318166832283 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24566473988439305, "acc_stderr": 0.02317629820399201, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.02317629820399201 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.24183006535947713, "acc_stderr": 0.024518195641879334, "acc_norm": 0.24183006535947713, "acc_norm_stderr": 0.024518195641879334 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2861736334405145, "acc_stderr": 0.025670259242188947, "acc_norm": 0.2861736334405145, "acc_norm_stderr": 0.025670259242188947 }, 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"harness|truthfulqa:mc|0": { "mc1": 0.22643818849449204, "mc1_stderr": 0.014651337324602574, "mc2": 0.372644846918848, "mc2_stderr": 0.014009270688888235 }, "harness|winogrande|5": { "acc": 0.6479873717442778, "acc_stderr": 0.013422874824929714 }, "harness|gsm8k|5": { "acc": 0.028051554207733132, "acc_stderr": 0.004548229533836337 } } ``` ## 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 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[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]

Samburskoy/TT3

--- license: openrail ---