davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
open-llm-leaderboard/details_NeuralNovel__Aeryth-7B-v0.1	--- pretty_name: Evaluation run of NeuralNovel/Aeryth-7B-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [NeuralNovel/Aeryth-7B-v0.1](https://huggingface.co/NeuralNovel/Aeryth-7B-v0.1)\ \ 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 4 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_NeuralNovel__Aeryth-7B-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-14T12:31:11.639995](https://huggingface.co/datasets/open-llm-leaderboard/details_NeuralNovel__Aeryth-7B-v0.1/blob/main/results_2024-01-14T12-31-11.639995.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\": 0.607832340017972,\n\ \ \"acc_stderr\": 0.033171072669556316,\n \"acc_norm\": 0.6134606437151463,\n\ \ \"acc_norm_stderr\": 0.03384290514267795,\n \"mc1\": 0.4602203182374541,\n\ \ \"mc1_stderr\": 0.01744801722396088,\n \"mc2\": 0.6357466374094296,\n\ \ \"mc2_stderr\": 0.015661867399479723\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5631399317406144,\n \"acc_stderr\": 0.014494421584256524,\n\ \ \"acc_norm\": 0.6032423208191127,\n \"acc_norm_stderr\": 0.014296513020180646\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6514638518223461,\n\ \ \"acc_stderr\": 0.004755329243976671,\n \"acc_norm\": 0.835291774546903,\n\ \ \"acc_norm_stderr\": 0.0037015895712743134\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n\ \ \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5664739884393064,\n\ \ \"acc_stderr\": 0.03778621079092056,\n \"acc_norm\": 0.5664739884393064,\n\ \ \"acc_norm_stderr\": 0.03778621079092056\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.43859649122807015,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916747,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916747\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.36772486772486773,\n \"acc_stderr\": 0.024833839825562417,\n \"\ acc_norm\": 0.36772486772486773,\n \"acc_norm_stderr\": 0.024833839825562417\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6903225806451613,\n\ \ \"acc_stderr\": 0.026302774983517414,\n \"acc_norm\": 0.6903225806451613,\n\ \ \"acc_norm_stderr\": 0.026302774983517414\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\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\ \ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124488,\n \"\ acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124488\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153303,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153303\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5666666666666667,\n \"acc_stderr\": 0.025124653525885117,\n\ \ \"acc_norm\": 0.5666666666666667,\n \"acc_norm_stderr\": 0.025124653525885117\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131143,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131143\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\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7981651376146789,\n \"acc_stderr\": 0.017208579357787586,\n \"\ acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.017208579357787586\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538271,\n \"\ acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538271\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501954,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501954\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \ \ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6278026905829597,\n\ \ \"acc_stderr\": 0.032443052830087304,\n \"acc_norm\": 0.6278026905829597,\n\ \ \"acc_norm_stderr\": 0.032443052830087304\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098825,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098825\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.04330043749650743,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.04330043749650743\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.045416094465039504,\n\ \ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.045416094465039504\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077785,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077785\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7841634738186463,\n\ \ \"acc_stderr\": 0.01471168438613996,\n \"acc_norm\": 0.7841634738186463,\n\ \ \"acc_norm_stderr\": 0.01471168438613996\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.02507071371915319,\n\ \ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.02507071371915319\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.34972067039106147,\n\ \ \"acc_stderr\": 0.015949308790233645,\n \"acc_norm\": 0.34972067039106147,\n\ \ \"acc_norm_stderr\": 0.015949308790233645\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\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765134,\n\ \ \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765134\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.43285528031290743,\n\ \ \"acc_stderr\": 0.012654565234622866,\n \"acc_norm\": 0.43285528031290743,\n\ \ \"acc_norm_stderr\": 0.012654565234622866\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6139705882352942,\n \"acc_stderr\": 0.029573269134411124,\n\ \ \"acc_norm\": 0.6139705882352942,\n \"acc_norm_stderr\": 0.029573269134411124\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6225490196078431,\n \"acc_stderr\": 0.01961085147488029,\n \ \ \"acc_norm\": 0.6225490196078431,\n \"acc_norm_stderr\": 0.01961085147488029\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7454545454545455,\n\ \ \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.7454545454545455,\n\ \ \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484378,\n\ \ \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484378\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7910447761194029,\n\ \ \"acc_stderr\": 0.028748298931728655,\n \"acc_norm\": 0.7910447761194029,\n\ \ \"acc_norm_stderr\": 0.028748298931728655\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4602203182374541,\n\ \ \"mc1_stderr\": 0.01744801722396088,\n \"mc2\": 0.6357466374094296,\n\ \ \"mc2_stderr\": 0.015661867399479723\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.01222375443423362\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.36087945413191813,\n \ \ \"acc_stderr\": 0.01322862675392514\n }\n}\n```" repo_url: https://huggingface.co/NeuralNovel/Aeryth-7B-v0.1 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_07T23_22_00.392280 path: - '/details_harness\|arc:challenge\|25_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|arc:challenge\|25_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|arc:challenge\|25_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|arc:challenge\|25_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-14T12-31-11.639995.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|gsm8k\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|gsm8k\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|gsm8k\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|gsm8k\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hellaswag\|10_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hellaswag\|10_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hellaswag\|10_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hellaswag\|10_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-07T23-22-00.392280.parquet' - 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'/details_harness\|hendrycksTest-prehistory\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-07T23-22-00.392280.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-08T00-11-57.804296.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T23-38-01.089688.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-14T12-31-11.639995.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-14T12-31-11.639995.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '/details_harness\|winogrande\|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '/details_harness\|winogrande\|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '/details_harness\|winogrande\|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '/details_harness\|winogrande\|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-14T12-31-11.639995.parquet' - config_name: results data_files: - split: 2024_01_07T23_22_00.392280 path: - results_2024-01-07T23-22-00.392280.parquet - split: 2024_01_08T00_11_57.804296 path: - results_2024-01-08T00-11-57.804296.parquet - split: 2024_01_13T23_38_01.089688 path: - results_2024-01-13T23-38-01.089688.parquet - split: 2024_01_14T12_31_11.639995 path: - results_2024-01-14T12-31-11.639995.parquet - split: latest path: - results_2024-01-14T12-31-11.639995.parquet --- # Dataset Card for Evaluation run of NeuralNovel/Aeryth-7B-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [NeuralNovel/Aeryth-7B-v0.1](https://huggingface.co/NeuralNovel/Aeryth-7B-v0.1) 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_NeuralNovel__Aeryth-7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-14T12:31:11.639995](https://huggingface.co/datasets/open-llm-leaderboard/details_NeuralNovel__Aeryth-7B-v0.1/blob/main/results_2024-01-14T12-31-11.639995.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": 0.607832340017972, "acc_stderr": 0.033171072669556316, "acc_norm": 0.6134606437151463, "acc_norm_stderr": 0.03384290514267795, "mc1": 0.4602203182374541, "mc1_stderr": 0.01744801722396088, "mc2": 0.6357466374094296, "mc2_stderr": 0.015661867399479723 }, "harness\|arc:challenge\|25": { "acc": 0.5631399317406144, "acc_stderr": 0.014494421584256524, "acc_norm": 0.6032423208191127, "acc_norm_stderr": 0.014296513020180646 }, "harness\|hellaswag\|10": { "acc": 0.6514638518223461, "acc_stderr": 0.004755329243976671, "acc_norm": 0.835291774546903, "acc_norm_stderr": 0.0037015895712743134 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.631578947368421, "acc_stderr": 0.03925523381052932, "acc_norm": 0.631578947368421, "acc_norm_stderr": 0.03925523381052932 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880267, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880267 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03942082639927213, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03942082639927213 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5664739884393064, "acc_stderr": 0.03778621079092056, "acc_norm": 0.5664739884393064, "acc_norm_stderr": 0.03778621079092056 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.43859649122807015, "acc_stderr": 0.04668000738510455, "acc_norm": 0.43859649122807015, "acc_norm_stderr": 0.04668000738510455 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6206896551724138, "acc_stderr": 0.04043461861916747, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.04043461861916747 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.36772486772486773, "acc_stderr": 0.024833839825562417, "acc_norm": 0.36772486772486773, "acc_norm_stderr": 0.024833839825562417 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6903225806451613, "acc_stderr": 0.026302774983517414, "acc_norm": 0.6903225806451613, "acc_norm_stderr": 0.026302774983517414 }, "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": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7212121212121212, "acc_stderr": 0.03501438706296781, "acc_norm": 0.7212121212121212, "acc_norm_stderr": 0.03501438706296781 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7525252525252525, "acc_stderr": 0.030746300742124488, "acc_norm": 0.7525252525252525, "acc_norm_stderr": 0.030746300742124488 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.844559585492228, "acc_stderr": 0.026148483469153303, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.026148483469153303 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5666666666666667, "acc_stderr": 0.025124653525885117, "acc_norm": 0.5666666666666667, "acc_norm_stderr": 0.025124653525885117 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131143, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131143 }, "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": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7981651376146789, "acc_stderr": 0.017208579357787586, "acc_norm": 0.7981651376146789, "acc_norm_stderr": 0.017208579357787586 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538271, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538271 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501954, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501954 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.759493670886076, "acc_stderr": 0.027820781981149685, "acc_norm": 0.759493670886076, "acc_norm_stderr": 0.027820781981149685 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6278026905829597, "acc_stderr": 0.032443052830087304, "acc_norm": 0.6278026905829597, "acc_norm_stderr": 0.032443052830087304 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7251908396946565, "acc_stderr": 0.03915345408847836, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.03915345408847836 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098825, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098825 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.04330043749650743, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.04330043749650743 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7300613496932515, "acc_stderr": 0.034878251684978906, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.034878251684978906 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6990291262135923, "acc_stderr": 0.045416094465039504, "acc_norm": 0.6990291262135923, "acc_norm_stderr": 0.045416094465039504 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077785, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077785 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7841634738186463, "acc_stderr": 0.01471168438613996, "acc_norm": 0.7841634738186463, "acc_norm_stderr": 0.01471168438613996 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6820809248554913, "acc_stderr": 0.02507071371915319, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.02507071371915319 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.34972067039106147, "acc_stderr": 0.015949308790233645, "acc_norm": 0.34972067039106147, "acc_norm_stderr": 0.015949308790233645 }, "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": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7160493827160493, "acc_stderr": 0.025089478523765134, "acc_norm": 0.7160493827160493, "acc_norm_stderr": 0.025089478523765134 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4645390070921986, "acc_stderr": 0.02975238965742705, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.02975238965742705 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.43285528031290743, "acc_stderr": 0.012654565234622866, "acc_norm": 0.43285528031290743, "acc_norm_stderr": 0.012654565234622866 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6139705882352942, "acc_stderr": 0.029573269134411124, "acc_norm": 0.6139705882352942, "acc_norm_stderr": 0.029573269134411124 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6225490196078431, "acc_stderr": 0.01961085147488029, "acc_norm": 0.6225490196078431, "acc_norm_stderr": 0.01961085147488029 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7454545454545455, "acc_stderr": 0.041723430387053825, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.041723430387053825 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.689795918367347, "acc_stderr": 0.029613459872484378, "acc_norm": 0.689795918367347, "acc_norm_stderr": 0.029613459872484378 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7910447761194029, "acc_stderr": 0.028748298931728655, "acc_norm": 0.7910447761194029, "acc_norm_stderr": 0.028748298931728655 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4602203182374541, "mc1_stderr": 0.01744801722396088, "mc2": 0.6357466374094296, "mc2_stderr": 0.015661867399479723 }, "harness\|winogrande\|5": { "acc": 0.7466456195737964, "acc_stderr": 0.01222375443423362 }, "harness\|gsm8k\|5": { "acc": 0.36087945413191813, "acc_stderr": 0.01322862675392514 } } ``` ## 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]
FINNUMBER/FINCH_TRAIN_QA_MCQA_100	--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 423079 num_examples: 100 download_size: 257187 dataset_size: 423079 configs: - config_name: default data_files: - split: train path: data/train-* ---
nlphuji/winogavil	--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: winogavil pretty_name: WinoGAViL size_categories: - 10K<n<100K source_datasets: - original tags: - commonsense-reasoning - visual-reasoning task_ids: [] extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes. The full license agreement is available in the dataset files." --- # Dataset Card for WinoGAViL - [Dataset Description](#dataset-description) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Colab notebook code for Winogavil evaluation with CLIP](#colab-notebook-code-for-winogavil-evaluation-with-clip) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more. - Homepage: https://winogavil.github.io/ - Colab https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi - Repository: https://github.com/WinoGAViL/WinoGAViL-experiments/ - Paper: https://arxiv.org/abs/2207.12576 - Leaderboard: https://winogavil.github.io/leaderboard - Point of Contact: winogavil@gmail.com; yonatanbitton1@gmail.com ### Supported Tasks and Leaderboards https://winogavil.github.io/leaderboard. https://paperswithcode.com/dataset/winogavil. ## Colab notebook code for Winogavil evaluation with CLIP https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi ### Languages English. ## Dataset Structure ### Data Fields candidates (list): ["bison", "shelter", "beard", "flea", "cattle", "shave"] - list of image candidates. cue (string): pogonophile - the generated cue. associations (string): ["bison", "beard", "shave"] - the images associated with the cue selected by the user. score_fool_the_ai (int64): 80 - the spymaster score (100 - model score) for fooling the AI, with CLIP RN50 model. num_associations (int64): 3 - The number of images selected as associative with the cue. num_candidates (int64): 6 - the number of total candidates. solvers_jaccard_mean (float64): 1.0 - three solvers scores average on the generated association instance. solvers_jaccard_std (float64): 1.0 - three solvers scores standard deviation on the generated association instance ID (int64): 367 - association ID. ### Data Splits There is a single TEST split. In the accompanied paper and code we sample it to create different training sets, but the intended use is to use winogavil as a test set. There are different number of candidates, which creates different difficulty levels: -- With 5 candidates, random model expected score is 38%. -- With 6 candidates, random model expected score is 34%. -- With 10 candidates, random model expected score is 24%. -- With 12 candidates, random model expected score is 19%. <details> <summary>Why random chance for success with 5 candidates is 38%?</summary> It is a binomial distribution probability calculation. Assuming N=5 candidates, and K=2 associations, there could be three events: (1) The probability for a random guess is correct in 0 associations is 0.3 (elaborate below), and the Jaccard index is 0 (there is no intersection between the correct labels and the wrong guesses). Therefore the expected random score is 0. (2) The probability for a random guess is correct in 1 associations is 0.6, and the Jaccard index is 0.33 (intersection=1, union=3, one of the correct guesses, and one of the wrong guesses). Therefore the expected random score is 0.60.33 = 0.198. (3) The probability for a random guess is correct in 2 associations is 0.1, and the Jaccard index is 1 (intersection=2, union=2). Therefore the expected random score is 0.11 = 0.1. * Together, when K=2, the expected score is 0+0.198+0.1 = 0.298. To calculate (1), the first guess needs to be wrong. There are 3 "wrong" guesses and 5 candidates, so the probability for it is 3/5. The next guess should also be wrong. Now there are only 2 "wrong" guesses, and 4 candidates, so the probability for it is 2/4. Multiplying 3/5 * 2/4 = 0.3. Same goes for (2) and (3). Now we can perform the same calculation with K=3 associations. Assuming N=5 candidates, and K=3 associations, there could be four events: (4) The probability for a random guess is correct in 0 associations is 0, and the Jaccard index is 0. Therefore the expected random score is 0. (5) The probability for a random guess is correct in 1 associations is 0.3, and the Jaccard index is 0.2 (intersection=1, union=4). Therefore the expected random score is 0.30.2 = 0.06. (6) The probability for a random guess is correct in 2 associations is 0.6, and the Jaccard index is 0.5 (intersection=2, union=4). Therefore the expected random score is 0.65 = 0.3. (7) The probability for a random guess is correct in 3 associations is 0.1, and the Jaccard index is 1 (intersection=3, union=3). Therefore the expected random score is 0.11 = 0.1. Together, when K=3, the expected score is 0+0.06+0.3+0.1 = 0.46. Taking the average of 0.298 and 0.46 we reach 0.379. Same process can be recalculated with 6 candidates (and K=2,3,4), 10 candidates (and K=2,3,4,5) and 123 candidates (and K=2,3,4,5,6). </details> ## Dataset Creation Inspired by the popular card game Codenames, a “spymaster” gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. ### Annotations #### Annotation process We paid Amazon Mechanical Turk Workers to play our game. ## Considerations for Using the Data All associations were obtained with human annotators. ### Licensing Information CC-By 4.0 ### Citation Information @article{bitton2022winogavil, title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models}, author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy}, journal={arXiv preprint arXiv:2207.12576}, year={2022}
MoyAI/Funniest-answers	--- task_categories: - conversational - text-generation - text2text-generation - text-classification language: - ru pretty_name: Funny-responses size_categories: - n<1K --- # Датасет прикольных ответов Датасет смешных ответов собирается идеями от других людей (которые пишут мне если нет аккаунта в hugginface), и мной. Сборка началась 8 февраля 2023 года. ## Данные JSON файл содержит data список где есть message - сообщение, response - ответ, и type - тип. Это пример данных ```json { "data": [ {"message": "Дано: Архимед упал в говно.", "response": "Найти: Выталкивающую силу.", "type": "w"}, {"message": "Как дела?", "response": "Всё было нормально, пока Вася выёживаться не стал)", "type": "n"}, {"message": "Что ты можешь сказать о сне?", "response": "Я так долго тренировался спать что могу делать это с закрытыми глазами.", "type": "n"}, ... ] } ... ``` # Список типов сообщений-ответов: - "n" Нейтрально, без оскорбления - "a" Аггресивный/токсичный ответ - "w" Содержит не всегда приемлемые или оскорбительные слова. - "s" Содержит маты. (либо ответ либо запрос содержит хотя бы один мат) - "p" Писсимистичные ответы, с низкой самооценкой или суицидальными мыслями. (Оно не работает -> Безработный как я) - "u" Небезопасные ответы, предложение чего-то запрещённого шуткой (например алкоголь)
MartinKu/wikipedia_stage2	--- dataset_info: features: - name: text dtype: string - name: S_V sequence: string - name: S_V_position sequence: int64 - name: O_C sequence: string - name: O_C_position sequence: int64 splits: - name: train num_bytes: 45092871426 num_examples: 6458670 download_size: 25091808148 dataset_size: 45092871426 --- # Dataset Card for "wikipedia_stage2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
matallanas/yannic-kilcher-transcript	--- dataset_info: features: - name: id dtype: string - name: channel dtype: string - name: channel_id dtype: string - name: title dtype: string - name: categories sequence: string - name: tags sequence: string - name: description dtype: string - name: text dtype: string - name: segments list: - name: start dtype: float64 - name: end dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 24560830 num_examples: 370 download_size: 12784371 dataset_size: 24560830 --- # Dataset Card for "yannic-kilcher-transcript" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-11ed4317-15c4-4e98-9e37-8cdfe6d38dfb-4947	--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: autoevaluate/multi-class-classification metrics: ['matthews_correlation'] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: autoevaluate/multi-class-classification * Dataset: emotion * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
izhx/yue-lihkg-topic	--- license: cc-by-4.0 --- From https://github.com/toastynews/lihkg-cat-v2 ### lihkg-cat-v2 Scraped forum threads from LIHKG for categorization task. Formatted to use with BERT. Compared to v1, the number of categories increased from 18 to 20, and the number of training examples increased from 300 to 500. The minimum length for each example has also increased to make the task more solvable.
Seanxh/twitter_dataset_1713196913	--- 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: 85739 num_examples: 199 download_size: 34951 dataset_size: 85739 configs: - config_name: default data_files: - split: train path: data/train-* ---
jonathang/dreambooth-hackathon-images-mario-bg-1	--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 559875.0 num_examples: 15 download_size: 523924 dataset_size: 559875.0 --- # Dataset Card for "dreambooth-hackathon-images-mario-bg-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Heejung89/custom_kor3	--- license: mit dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 8664 num_examples: 42 download_size: 3732 dataset_size: 8664 configs: - config_name: default data_files: - split: train path: data/train-* ---
RahulRaman/final-counting-dataset	--- dataset_info: features: - name: input_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 126948111.357 num_examples: 1359 download_size: 34431713 dataset_size: 126948111.357 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_lloorree__jfdslijsijdgis	--- pretty_name: Evaluation run of lloorree/jfdslijsijdgis dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lloorree/jfdslijsijdgis](https://huggingface.co/lloorree/jfdslijsijdgis) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lloorree__jfdslijsijdgis\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-17T00:34:49.304226](https://huggingface.co/datasets/open-llm-leaderboard/details_lloorree__jfdslijsijdgis/blob/main/results_2023-09-17T00-34-49.304226.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\": 0.6907933129316588,\n\ \ \"acc_stderr\": 0.03107455661224763,\n \"acc_norm\": 0.694824769775718,\n\ \ \"acc_norm_stderr\": 0.031044197474221744,\n \"mc1\": 0.41615667074663404,\n\ \ \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.5820460749080146,\n\ \ \"mc2_stderr\": 0.015030523772190541\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6518771331058021,\n \"acc_stderr\": 0.01392100859517935,\n\ \ \"acc_norm\": 0.6962457337883959,\n \"acc_norm_stderr\": 0.013438909184778764\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6760605457080263,\n\ \ \"acc_stderr\": 0.00467020812857923,\n \"acc_norm\": 0.8695478988249352,\n\ \ \"acc_norm_stderr\": 0.0033611183954523846\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.03110318238312338,\n\ \ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.03110318238312338\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.02783491252754407,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.02783491252754407\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8125,\n\ \ \"acc_stderr\": 0.032639560491693344,\n \"acc_norm\": 0.8125,\n\ \ \"acc_norm_stderr\": 0.032639560491693344\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736413,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736413\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\":\ \ 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.041633319989322626\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6638297872340425,\n \"acc_stderr\": 0.030881618520676942,\n\ \ \"acc_norm\": 0.6638297872340425,\n \"acc_norm_stderr\": 0.030881618520676942\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.43859649122807015,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6206896551724138,\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\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|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_biology\|5\": {\n \"acc\": 0.8064516129032258,\n\ \ \"acc_stderr\": 0.022475258525536057,\n \"acc_norm\": 0.8064516129032258,\n\ \ \"acc_norm_stderr\": 0.022475258525536057\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\"\ : 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781668,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781668\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\ acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9533678756476683,\n \"acc_stderr\": 0.015216761819262592,\n\ \ \"acc_norm\": 0.9533678756476683,\n \"acc_norm_stderr\": 0.015216761819262592\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7153846153846154,\n \"acc_stderr\": 0.022878322799706304,\n\ \ \"acc_norm\": 0.7153846153846154,\n \"acc_norm_stderr\": 0.022878322799706304\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \ \ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.026841514322958934,\n\ \ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.026841514322958934\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.423841059602649,\n \"acc_stderr\": 0.04034846678603397,\n \"acc_norm\"\ : 0.423841059602649,\n \"acc_norm_stderr\": 0.04034846678603397\n },\n\ \ \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8935779816513761,\n\ \ \"acc_stderr\": 0.013221554674594372,\n \"acc_norm\": 0.8935779816513761,\n\ \ \"acc_norm_stderr\": 0.013221554674594372\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\"\ : {\n \"acc\": 0.6018518518518519,\n \"acc_stderr\": 0.033384734032074016,\n\ \ \"acc_norm\": 0.6018518518518519,\n \"acc_norm_stderr\": 0.033384734032074016\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9117647058823529,\n \"acc_stderr\": 0.01990739979131695,\n \"\ acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.01990739979131695\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\": 0.7982062780269058,\n\ \ \"acc_stderr\": 0.026936111912802263,\n \"acc_norm\": 0.7982062780269058,\n\ \ \"acc_norm_stderr\": 0.026936111912802263\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n\ \ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.859504132231405,\n \"acc_stderr\": 0.03172233426002158,\n \"acc_norm\"\ : 0.859504132231405,\n \"acc_norm_stderr\": 0.03172233426002158\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8425925925925926,\n\ \ \"acc_stderr\": 0.03520703990517964,\n \"acc_norm\": 0.8425925925925926,\n\ \ \"acc_norm_stderr\": 0.03520703990517964\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8159509202453987,\n \"acc_stderr\": 0.030446777687971726,\n\ \ \"acc_norm\": 0.8159509202453987,\n \"acc_norm_stderr\": 0.030446777687971726\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5357142857142857,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.02158649400128138\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8659003831417624,\n\ \ \"acc_stderr\": 0.012185528166499978,\n \"acc_norm\": 0.8659003831417624,\n\ \ \"acc_norm_stderr\": 0.012185528166499978\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7658959537572254,\n \"acc_stderr\": 0.022797110278071124,\n\ \ \"acc_norm\": 0.7658959537572254,\n \"acc_norm_stderr\": 0.022797110278071124\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.511731843575419,\n\ \ \"acc_stderr\": 0.016717897676932162,\n \"acc_norm\": 0.511731843575419,\n\ \ \"acc_norm_stderr\": 0.016717897676932162\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7845659163987139,\n\ \ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\ \ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.021185893615225188,\n\ \ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.021185893615225188\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5177304964539007,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.5177304964539007,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5397653194263363,\n\ \ \"acc_stderr\": 0.012729785386598545,\n \"acc_norm\": 0.5397653194263363,\n\ \ \"acc_norm_stderr\": 0.012729785386598545\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7169117647058824,\n \"acc_stderr\": 0.02736586113151381,\n\ \ \"acc_norm\": 0.7169117647058824,\n \"acc_norm_stderr\": 0.02736586113151381\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.75,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.75,\n \"acc_norm_stderr\": 0.01751781884501444\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\": 0.8122448979591836,\n\ \ \"acc_stderr\": 0.025000256039546195,\n \"acc_norm\": 0.8122448979591836,\n\ \ \"acc_norm_stderr\": 0.025000256039546195\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.900497512437811,\n \"acc_stderr\": 0.0211662163046594,\n\ \ \"acc_norm\": 0.900497512437811,\n \"acc_norm_stderr\": 0.0211662163046594\n\ \ },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\":\ \ 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \"acc_norm\": 0.89,\n\ \ \"acc_norm_stderr\": 0.03144660377352203\n },\n \"harness\|hendrycksTest-virology\|5\"\ : {\n \"acc\": 0.5180722891566265,\n \"acc_stderr\": 0.03889951252827216,\n\ \ \"acc_norm\": 0.5180722891566265,\n \"acc_norm_stderr\": 0.03889951252827216\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8713450292397661,\n\ \ \"acc_stderr\": 0.02567934272327692,\n \"acc_norm\": 0.8713450292397661,\n\ \ \"acc_norm_stderr\": 0.02567934272327692\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 0.41615667074663404,\n \"mc1_stderr\": 0.017255657502903043,\n\ \ \"mc2\": 0.5820460749080146,\n \"mc2_stderr\": 0.015030523772190541\n\ \ }\n}\n```" repo_url: https://huggingface.co/lloorree/jfdslijsijdgis leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|arc:challenge\|25_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|arc:challenge\|25_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hellaswag\|10_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hellaswag\|10_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-15T09-43-22.432852.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-17T00-34-49.304226.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_15T09_43_22.432852 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-17T00-34-49.304226.parquet' - config_name: results data_files: - split: 2023_09_15T09_43_22.432852 path: - results_2023-09-15T09-43-22.432852.parquet - split: 2023_09_17T00_34_49.304226 path: - results_2023-09-17T00-34-49.304226.parquet - split: latest path: - results_2023-09-17T00-34-49.304226.parquet --- # Dataset Card for Evaluation run of lloorree/jfdslijsijdgis ## Dataset Description - Homepage: - Repository: https://huggingface.co/lloorree/jfdslijsijdgis - 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 [lloorree/jfdslijsijdgis](https://huggingface.co/lloorree/jfdslijsijdgis) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lloorree__jfdslijsijdgis", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T00:34:49.304226](https://huggingface.co/datasets/open-llm-leaderboard/details_lloorree__jfdslijsijdgis/blob/main/results_2023-09-17T00-34-49.304226.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": 0.6907933129316588, "acc_stderr": 0.03107455661224763, "acc_norm": 0.694824769775718, "acc_norm_stderr": 0.031044197474221744, "mc1": 0.41615667074663404, "mc1_stderr": 0.017255657502903043, "mc2": 0.5820460749080146, "mc2_stderr": 0.015030523772190541 }, "harness\|arc:challenge\|25": { "acc": 0.6518771331058021, "acc_stderr": 0.01392100859517935, "acc_norm": 0.6962457337883959, "acc_norm_stderr": 0.013438909184778764 }, "harness\|hellaswag\|10": { "acc": 0.6760605457080263, "acc_stderr": 0.00467020812857923, "acc_norm": 0.8695478988249352, "acc_norm_stderr": 0.0033611183954523846 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.04171654161354543, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.04171654161354543 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8223684210526315, "acc_stderr": 0.03110318238312338, "acc_norm": 0.8223684210526315, "acc_norm_stderr": 0.03110318238312338 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7132075471698113, "acc_stderr": 0.02783491252754407, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.02783491252754407 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8125, "acc_stderr": 0.032639560491693344, "acc_norm": 0.8125, "acc_norm_stderr": 0.032639560491693344 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736413, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736413 }, "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": 0.78, "acc_stderr": 0.041633319989322626, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322626 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6638297872340425, "acc_stderr": 0.030881618520676942, "acc_norm": 0.6638297872340425, "acc_norm_stderr": 0.030881618520676942 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.43859649122807015, "acc_stderr": 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0.02567934272327692, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.02567934272327692 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.41615667074663404, "mc1_stderr": 0.017255657502903043, "mc2": 0.5820460749080146, "mc2_stderr": 0.015030523772190541 } } ``` ### 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]
canristiian/drug_rule_sort2	--- license: apache-2.0 ---
ninja/cluster-colors	--- dataset_info: features: - name: color dtype: string - name: hex dtype: string splits: - name: train num_bytes: 392073 num_examples: 11936 download_size: 264134 dataset_size: 392073 --- # Dataset Card for "cluster-colors" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
FoodIntake/openfoodfacts_package_weights	--- license: odbl task_categories: - text-generation - token-classification - text-classification language: - en - de - it - es - fr tags: - food - packaged - branded pretty_name: 'Open Food Facts data subset: package weights with general information' size_categories: - 100K<n<1M ---
stanfordnlp/SHP-2	--- task_categories: - text-generation - question-answering tags: - human feedback - rlhf - preferences - reddit - preference model - RL - NLG - evaluation size_categories: - 1M<n<10M language: - en --- # 🚢 Stanford Human Preferences Dataset v2 (SHP-2) ## Summary SHP-2 is a dataset of 4.8M collective human preferences over responses to questions/instructions in 129 different subject areas, from cooking to legal advice. It is an extended version of the original 385K [SHP dataset](https://huggingface.co/datasets/stanfordnlp/SHP). The preferences are meant to reflect the helpfulness of one response over another, and are intended to be used for training RLHF reward models and NLG evaluation models (e.g., [SteamSHP](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl)). Each example is a Reddit or StackExchange post with a question/instruction and a pair of top-level comments for that post, where one comment is more preferred by Reddit / StackExchange users (collectively). SHP exploits the fact that if comment A was written after comment B but has a higher score nonetheless, then A is ostensibly more preferred to B. If A had been written before B, then we could not conclude this, since its higher score could have been the result of more visibility. We chose data where the preference label is intended to reflect which response is more helpful rather than which is less harmful, the latter being the focus of much past work. How is SHP different from [Anthropic's HH-RLHF dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) and [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)? \| Dataset \| Size \| Input \| Label \| Domains \| Data Format \| Length \| \| -------------------- \| ---- \| -------------------------- \| ---------------------------- \| ------------------------- \| ------------------------------------- \| --------------- \| \| SHP-2 \| 4.8M \| Naturally occurring human-written responses \| Collective Human Preference \| 129 (labelled) \| Question/Instruction + Response (Single-turn) \| up to 10.1K T5 tokens \| \| HH-RLHF \| 91K \| Dialogue with LLM \| Individual Human Preference \| not labelled \| Live Chat (Multi-turn) \| up to 1.5K T5 tokens \| \| OASST \| 161K \| Dialogue with LLM \| K Individual Preferences, Aggregated \| not labelled \| Live Chat (Multi-Turn) \| up to 1.5K T5 tokens \| How is SHP different from other datasets that have scraped Reddit, like [ELI5](https://huggingface.co/datasets/eli5#source-data)? SHP uses the timestamp information to infer preferences, while ELI5 only provides comments and scores -- the latter are not enough to infer preferences since comments made earlier tend to get higher scores from more visibility. It also contains data from more domains: \| Dataset \| Size \| Comments + Scores \| Preferences \| Number of Domains \| \| -------------------- \| ---- \| ------------------ \| -------------\| ------------------ \| \| SHP-2 \| 4.8M \| Yes \| Yes \| 129 (70 from Reddit, 59 from StackExchange) \| \| SHP \| 385K \| Yes \| Yes \| 18 (from Reddit) \| \| ELI5 \| 270K \| Yes \| No \| 3 \| ## Data Structure There are 2 directories, one for Reddit and one for StackExchange. There are 70 subdirectories under `reddit/`, one for each subreddit, and 59 subdirectories under `stackexchange/`, one for each stackexchange site. Each subdirectory contains a JSONL file for the training, validation, and test data. Here's how to get the data using Huggingface's `datasets` library: ```python from datasets import load_dataset # Load all the data dataset = load_dataset("stanfordnlp/shp-2") # Load one of the subreddits dataset = load_dataset("stanfordnlp/shp-2", data_dir="reddit/askculinary") # Load one of the StackExchange sites dataset = load_dataset("stanfordnlp/shp-2", data_dir="stackexchange/stack_academia") ``` Here's an example from `reddit/askculinary/train.json`: ``` { `post_id`:"qt3nxl", `domain`:"askculinary_train", `upvote_ratio`:0.98, `history`:"What's the best way to disassemble raspberries? Like this, but down to the individual seeds: https:\/\/i.imgur.com\/Z0c6ZKE.jpg I've been pulling them apart with tweezers and it's really time consuming. I have about 10 pounds to get through this weekend.", `c_root_id_A`:"hkh25sc", `c_root_id_B`:"hkh25lp", `created_at_utc_A`:1636822112, `created_at_utc_B`:1636822110, `score_A`:340, `score_B`:166, `human_ref_A`:"Pectinex, perhaps? It's an enzyme that breaks down cellulose. With citrus, you let it sit in a dilute solution of pectinex overnight to break down the connective tissues. You end up with perfect citrus supremes. If you let the raspberries sit for a shorter time, I wonder if it would separate the seeds the same way...? Here's an example: https:\/\/www.chefsteps.com\/activities\/perfect-citrus-supreme", `human_ref_B`:"Raspberry juice will make a bright stain at first, but in a matter of weeks it will start to fade away to almost nothing. It is what is known in the natural dye world as a fugitive dye, it will fade even without washing or exposure to light. I hope she gets lots of nice photos of these stains on her dress, because soon that will be all she has left of them!", `labels`:1, `metadata_A`: "", `metadata_B`: "", `seconds_difference`:2.0, `score_ratio`:2.0481927711 } ``` Here's an example from `stackexchange/stack_academia/validation.json`: ``` { `post_id`:"87393", `domain`:"academia_validation", `history`:"What to answer an author asking me if I reviewed his/her paper? <sep> Suppose I review someone's paper anonymously, the paper gets accepted, and a year or two later we meet e.g. in a social event and he/she asks me "did you review my paper?". What should I answer? There are several sub-questions here: Suppose the review was a good one, and the paper eventualy got accepted, so I do not mind telling that I was the reviewer. Is there any rule/norm prohibiting me from telling the truth? Suppose the review was not so good, so I do not want to reveal. What can I answer? If I just say "I am not allowed to tell you", this immediately reveals me... On the other hand, I do not want to lie. What options do I have?", `c_root_id_A`:"87434", `c_root_id_B`:"87453", `created_at_utc_A`:1490989560, `created_at_utc_B`:1491012608, `score_A`:2, `score_B`:5, `human_ref_A`:"I am aware of at least one paper where a referee went out of cover (after the review process of course) and was explicitly mentioned in a later paper: <blockquote> X and Y thank Z, who as the anonymous referee was kind enough to point out the error (and later became non-anonymous). </blockquote> so it is sure fine to answer truthfully that yes you did review, but only if you wish of course (and most likely if you have been helpful and the authors of the paper responsive).", `human_ref_B`:"Perhaps you should follow the example of Howard Percy Robertson (known as the 'R' in the famous FLRW, or Friedmann-Lematre-Robertson-Walker metric used in physical cosmology.) He was the referee of the famous Einstein-Rosen paper, which was rejected by Physical Review, prompting Einstein never to publish in Physical Review again. Einstein ignored the referee report, but months later, it seems, Robertson had a chance to talk to Einstein and may have helped convince him of the error of his ways. However, as far as we know, he never revealed to Einstein that he was the anonymous referee for Physical Review. It was not until 2005 I believe, long after the death of all participants, that Physical Review chose to disclose the referee's identity (http://physicstoday.scitation.org/doi/full/10.1063/1.2117822).", `labels`:"0", `metadata_A`:"Post URL: https://academia.stackexchange.com/questions/87393, Response URL: https://academia.stackexchange.com/questions/87434, Post author username: Erel Segal-Halevi, Post author profile: https://academia.stackexchange.com/users/787, Response author username: mts, Response author profile: https://academia.stackexchange.com/users/49583", `metadata_B`:"Post URL: https://academia.stackexchange.com/questions/87393, Response URL: https://academia.stackexchange.com/questions/87453, Post author username: Erel Segal-Halevi, Post author profile: https://academia.stackexchange.com/users/787, Response author username: Viktor Toth, Response author profile: https://academia.stackexchange.com/users/7938", `seconds_difference`:23048.0, `score_ratio`:2.5, } ``` where the fields are: - ```post_id```: the ID of the Reddit post (string) - ```domain```: the subreddit and split the example is drawn from, separated by an underscore (string) - ```upvote_ratio```: the percent of votes received by the post that were positive (aka upvotes), -1.0 for stackexchange as there is no such data (float) - ```history```: the post title concatented to the post body (string) - ```c_root_id_A```: the ID of comment A (string) - ```c_root_id_B```: the ID of comment B (string) - ```created_at_utc_A```: utc timestamp of when comment A was created (integer) - ```created_at_utc_B```: utc timestamp of when comment B was created (integer) - ```score_A```: (# positive votes - # negative votes + 1) received by comment A (integer) - ```score_B```: (# positive votes - # negative votes + 1) received by comment B (integer) - ```human_ref_A```: text of comment A (string) - ```human_ref_B```: text of comment B (string) - ```labels```: the preference label -- it is 1 if A is preferred to B; 0 if B is preferred to A. This was randomized such that the label distribution is roughly 50/50. (integer) - ```metadata_A```: metadata for stackexchange post and comment A (string) - ```metadata_B```: metadata for stackexchange post and comment B (string) - ```seconds_difference```: how many seconds after the less preferred comment the more preferred one was created (will always be >= 0) (integer) - ```score_ratio```: the ratio of the more preferred comment's score to the less preferred comment's score (will be >= 1) (float) ## Dataset Design ### Domain Selection The data is sourced from Reddit and StackExchange, which are both public forums organized into different domains. SHP-2 contains a train, validation, and test split for comments scraped from each domain. We chose domains based on: 1. whether they were well-known (>= 100K subscribers for Reddit and >= 50K for StackExchange) 2. whether posts were expected to pose a question or instruction 3. whether responses were valued based on how helpful they were 4. whether comments had to be rooted in some objectivity, instead of being entirely about personal experiences (e.g., `askscience` vs. `AskAmericans`) The train/validation/test splits were created by splitting the post IDs of a domain in 90%/5%/5% proportions respectively, so that no post would appear in multiple splits. Since different posts have different numbers of comments, the number of preferences in each split is not exactly 90%/5%/5%. See below for a list of all domains: Reddit: \ techsupport, asklinguistics, askscience, catadvice, campingandhiking, askphysics, espresso, botany, asksocialscience, askbaking, ultralight, legaladvice, hiking, webdev, askengineers, screenwriting, askhistorians, vegetarian, writing, diy, musictheory, camping, moviesuggestions, askeconomics, stocks, frugal, outoftheloop, booksuggestions, gamedev, linuxquestions, asknetsec, aviation, askacademia, asksciencefiction, askhr, explainlikeimfive, etymology, entrepreneur, cooking, puppy101, keto, crochet, smallbusiness, architecture, artfundamentals, sewing, zerowaste, changemyview, mechanicadvice, iwanttolearn, eatcheapandhealthy, askanthropology, askculinary, askphilosophy, tea, running, excel, homebrewing, solotravel, fishing, cookingforbeginners, homeautomation, ifyoulikeblank, travel, suggestmeabook, televisionsuggestions, sysadmin, askcarguys, askdocs, askvet StackExchange: \ stack_unix, stack_android, stack_academia, stack_superuser, stack_tex, stack_photo, stack_datascience, stack_mechanics, stack_english, stack_askubuntu, stack_sharepoint, stack_workplace, stack_blender, stack_ethereum, stack_stats, stack_bitcoin, stack_gamedev, stack_raspberrypi, stack_arduino, stack_magento, stack_physics, stack_mathoverflow, stack_dsp, stack_movies, stack_crypto, stack_apple, stack_mathematica, stack_philosophy, stack_wordpress, stack_ux, stack_webmasters, stack_cs, stack_travel, stack_bicycles, stack_softwarerecs, stack_money, stack_ell, stack_scifi, stack_aviation, stack_math, stack_biology, stack_drupal, stack_diy, stack_security, stack_salesforce, stack_graphicdesign, stack_stackoverflow, stack_webapps, stack_cooking, stack_networkengineering, stack_dba, stack_puzzling, stack_serverfault, stack_codereview, stack_music, stack_codegolf, stack_electronics, stack_chemistry, stack_gis ### Data Selection For Reddit, the score of a post/comment is 1 plus the number of upvotes (approvals) it gets from users, minus the number of downvotes (disapprovals) it gets. For Stackexchange, the score of a post/comment is 0 plus the number of upvotes (approvals) it gets from users, minus the number of downvotes (disapprovals) it gets. The value of a score is relative; in domains(posts) with more traffic, there will be more higher-scoring posts(comments). Within a post, comments posted earlier will tend to have a higher score simply due to having more exposure, which is why using timestamp information is essential when inferring preferences. Given a post P and two comments (A,B) we only included the preference A > B in the dataset if 1. A was written no later than B and A has a higher score than B. 2. The post is a self-post (i.e., a body of text and not a link to another page) made before 2023, was not edited, and is not NSFW (over 18). For Stackexchange, edited posts were permitted as long as they were edited prior to the writing of the comments. 3. Neither comment was made by a deleted user, a moderator, or the post creator. The post was not made by a deleted user or moderator. 4. For Reddit, the post has a score >= 10 and each comment has a score >= 2 (upvoted at least once). For Stackexchange, the post has a score >= 5 and each comment has a non-zero score. The conditions are laxer for StackExchange because it is more strictly moderataed than Reddit, allowing us to hit the same data quality with lower thresholds. In particular, we allow negative-score comments from StackExchange because the negative scores are likely due to being inaccurat/misinformed rather than being toxic, and this provides a useful signal. A post with `n` comments could have up to (`n` choose `2`) preferences in the data. Since the number of comments per post is Pareto-distributed, to prevent a relatively small number of posts from dominating the Reddit data, we limited the scraping to 50 comments per post. This means that each post could have up to (`50` choose `2`) comments in the dataset, though this is a much smaller number in practice, since all the criteria above need to be met. No such criteria are imposed for StackExchange, since there are fewer comments per post. ### Reddit Preprocessing We tried to keep preprocessing to a minimum. Subreddit-specific abbreviations were expanded (e.g., "CMV" to "Change my view that"). In hyperlinks, only the referring text was kept and the URL was removed (if the URL was written out, then it was kept). ### Finetuning If you want to finetune a model to predict human preferences (e.g., for NLG evaluation or an RLHF reward model), here are some helpful tips: 1. Preprocess the data. The total input length should fit under the model's token limit (usually 512 tokens). Although models like FLAN-T5 use positional embeddings, we found that the loss would not converge if we finetuned it on inputs over 512 tokens. To avoid this, truncate the post text (in the `history` field) as much as possible, such that the whole input is under 512 tokens (do not truncate the comment(s) however). If this is still over 512 tokens, simply skip the example. 2. Use a sufficiently large model. Finetuning a single FLAN-T5-xl model across [the original 385K SHP training data](https://huggingface.co/datasets/stanfordnlp/SHP) should give you a test accuracy between 72-73% (across all domains on examples where the entire input fits within the token limit), ranging from 65-80% on individual subreddits. 3. Do in-domain prediction. Out-of-domain performance will be poor if the domains are unrelated (e.g., if you fine-tune on `askculinary` preferences and test on `askcarguys` preferences). 4. Train for fewer epochs. The InstructGPT paper paper suggests training a reward model for only 1 epoch. Since the same comment appears in multiple preferences, it is easy to overfit to the data. 5. Training on less data may help. Preferences with a large `score_ratio` (e.g., comment A having 2x the score of comment B) will provide a stronger signal for finetuning the model, so you may only want to consider preferences above a certain `score_ratio`. The number of preferences per post is Pareto-distributed, so to prevent the model from over-fitting to certain posts, you may want to limit the number of preferences from a particular post. ## Biases and Limitations ### Biases Although we filtered out posts with NSFW (over 18) content, chose domains that were well-moderated and had policies against harassment and bigotry, some of the data may contain discriminatory or harmful language. The data does not reflect the views of the dataset creators. Reddit and StackExchange users are also not representative of the broader population. Although subreddit-specific demographic information is not available, Reddit users overall are disproportionately male and from developed, Western, and English-speaking countries ([Pew Research](https://www.pewresearch.org/internet/2013/07/03/6-of-online-adults-are-reddit-users/)). This is likely also true of StackExchange users. Please keep this in mind before using any models trained on this data. ### Limitations The preference label in SHP is intended to reflect how helpful one response is relative to another, given an instruction/question. SHP is not intended for use in harm-minimization, as it was not designed to include the toxic content that would be necessary to learn a good toxicity detector. If you are looking for data where the preference label denotes less harm, we would recommend the harmfulness split of [Anthropic's HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf). Another limitation is that the more preferred response in SHP is not necessarily the more factual one. Though some comments do provide citations to justify their response, most do not. There are exceptions to this, such as the `askhistorians` subreddit, which is heavily moderated and answers are expected to provide citations. Note that the collective preference label in SHP is not necessarily what we would get if we asked users to independently vote on each comment before taking an unweighted sum. This is because comment scores on Reddit are public and are known to influence user preferences; a high score increases the likelihood of getting more positive votes [(Muchnik et al., 2013)](https://pubmed.ncbi.nlm.nih.gov/23929980/). Whether this "herding effect" temporarily or permanently shifts a user's preference is unclear. Therefore, while SHP does reflect collective human preferences, models trained on SHP may not generalize to settings where individual preferences are aggregated differently (e.g., users vote independently without ever seeing the current comment score, users vote after conferring, etc.). Thanks to Greg Stoddard for pointing this out. ## License Last updated: 07/016/2023 ### Reddit The data was made by scraping publicly available data in accordance with the a historical version of [Reddit API Terms of Use](https://docs.google.com/a/reddit.com/forms/d/e/1FAIpQLSezNdDNK1-P8mspSbmtC2r86Ee9ZRbC66u929cG2GX0T9UMyw/viewform), without any direct communication or written agreements with Reddit. According to the Terms of Use, "User Content" is owned by the users themselves -- not by Reddit -- and Reddit grants a "non-exclusive, non-transferable, non-sublicensable, and revocable license to copy and display the User Content". At time of writing, Reddit grants "no other rights or licenses are granted or implied, including any right to use User Content for other purposes, such as for training a machine learning or artificial intelligence model, without the express permission of rightsholders in the applicable User Content." However, the legality of training on publicly available data will depend on your jurisdiction (legal in Japan, for example). Datasets made by scraping Reddit are widely used in the research community: for example, Facebook AI Research used data scraped from Reddit to make the [ELI5](https://huggingface.co/datasets/eli5#source-data) dataset in 2019, which was made available without a license. Anthropic AI has also [attested to scraping Reddit](https://arxiv.org/pdf/2112.00861.pdf) for preferences using a different methodology, though this data was not made public. We take no responsibility for and we do not expressly or implicitly endorse any downstream use of this dataset. We reserve the right to modify the SHP dataset and this license at any point in the future. ### StackExchange StackExchange data is made available under a [CC by-SA license](https://creativecommons.org/licenses/by-sa/4.0/). ## Contact Please contact kawin@stanford.edu if you have any questions about the data. This dataset was created by Kawin Ethayarajh, Heidi (Chenyu) Zhang, and Shabnam Behzad with advice from Dan Jurafsky and Yizhong Wang. Kawin and Heidi prepared the Reddit datasets and trained the SteamSHP models. Kawin and Shabnam prepared the StackExchange data. Dan and Yizhong provide advice on dataset construction. ## Citation We will have a paper out soon, but until then, please cite: ``` @InProceedings{pmlr-v162-ethayarajh22a, title = {Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information}, author = {Ethayarajh, Kawin and Choi, Yejin and Swayamdipta, Swabha}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {5988--6008}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, } ```
datajuicer/the-pile-europarl-refined-by-data-juicer	--- license: apache-2.0 task_categories: - text-generation language: - en tags: - data-juicer - pretraining size_categories: - 10K<n<100K --- # The Pile -- EuroParl (refined by Data-Juicer) A refined version of EuroParl dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. Notice: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-europarl-refine-result.jsonl) (About 2.2GB). ## Dataset Information - Number of samples: 61,601 (Keep ~88.23% from the original dataset) ## Refining Recipe ```yaml # global parameters # global parameters project_name: 'Data-Juicer-recipes-EuroParl' dataset_path: '/path/to/your/dataset' # path to your dataset directory or file export_path: '/path/to/your/dataset.jsonl' np: 50 # number of subprocess to process your dataset open_tracer: true # process schedule # a list of several process operators with their arguments process: - clean_email_mapper: - clean_links_mapper: - fix_unicode_mapper: - punctuation_normalization_mapper: - whitespace_normalization_mapper: - alphanumeric_filter: tokenization: false min_ratio: 0.75 # <3sigma (0.779) max_ratio: 0.90 # >3sigma(0.878) - average_line_length_filter: # for code max_len: 588 # 3sigma - character_repetition_filter: rep_len: 10 max_ratio: 0.16 # >3sigma (0.114) - flagged_words_filter: lang: en tokenization: true max_ratio: 0.0007 # 3sigma - language_id_score_filter: min_score: 0.7 - maximum_line_length_filter: # for code max_len: 4000 # >3sigma (3104) - perplexity_filter: lang: en max_ppl: 7596 #(3sigma) - special_characters_filter: max_ratio: 0.3 # > 3sigma (0.243) - text_length_filter: max_len: 2e5 - words_num_filter: tokenization: true min_num: 20 max_num: 1e5 # 3sigma - word_repetition_filter: lang: en tokenization: true rep_len: 10 max_ratio: 0.2 # > 3sigma (0.185) - document_simhash_deduplicator: tokenization: space window_size: 6 lowercase: true ignore_pattern: '\p{P}' num_blocks: 6 hamming_distance: 4 ```
hanesh007/GemmaModelOutputs	--- license: apache-2.0 ---
open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics	--- pretty_name: Evaluation run of lgaalves/gpt-2-xl_camel-ai-physics dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lgaalves/gpt-2-xl_camel-ai-physics](https://huggingface.co/lgaalves/gpt-2-xl_camel-ai-physics)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-25T20:38:31.656182](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics/blob/main/results_2023-10-25T20-38-31.656182.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002202181208053691,\n\ \ \"em_stderr\": 0.0004800510816619256,\n \"f1\": 0.05571623322147659,\n\ \ \"f1_stderr\": 0.001366603872793856,\n \"acc\": 0.28844560078459863,\n\ \ \"acc_stderr\": 0.007481836249406744\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.0004800510816619256,\n\ \ \"f1\": 0.05571623322147659,\n \"f1_stderr\": 0.001366603872793856\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.001516300227445034,\n \ \ \"acc_stderr\": 0.001071779348549263\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5753749013417522,\n \"acc_stderr\": 0.013891893150264225\n\ \ }\n}\n```" repo_url: https://huggingface.co/lgaalves/gpt-2-xl_camel-ai-physics leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|arc:challenge\|25_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-21T19-46-11.375703.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_25T20_38_31.656182 path: - '/details_harness\|drop\|3_2023-10-25T20-38-31.656182.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-25T20-38-31.656182.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_25T20_38_31.656182 path: - '/details_harness\|gsm8k\|5_2023-10-25T20-38-31.656182.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-25T20-38-31.656182.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hellaswag\|10_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-21T19-46-11.375703.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_21T19_46_11.375703 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-21T19-46-11.375703.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_25T20_38_31.656182 path: - '/details_harness\|winogrande\|5_2023-10-25T20-38-31.656182.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-25T20-38-31.656182.parquet' - config_name: results data_files: - split: 2023_09_21T19_46_11.375703 path: - results_2023-09-21T19-46-11.375703.parquet - split: 2023_10_25T20_38_31.656182 path: - results_2023-10-25T20-38-31.656182.parquet - split: latest path: - results_2023-10-25T20-38-31.656182.parquet --- # Dataset Card for Evaluation run of lgaalves/gpt-2-xl_camel-ai-physics ## Dataset Description - Homepage: - Repository: https://huggingface.co/lgaalves/gpt-2-xl_camel-ai-physics - 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 [lgaalves/gpt-2-xl_camel-ai-physics](https://huggingface.co/lgaalves/gpt-2-xl_camel-ai-physics) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-25T20:38:31.656182](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics/blob/main/results_2023-10-25T20-38-31.656182.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.002202181208053691, "em_stderr": 0.0004800510816619256, "f1": 0.05571623322147659, "f1_stderr": 0.001366603872793856, "acc": 0.28844560078459863, "acc_stderr": 0.007481836249406744 }, "harness\|drop\|3": { "em": 0.002202181208053691, "em_stderr": 0.0004800510816619256, "f1": 0.05571623322147659, "f1_stderr": 0.001366603872793856 }, "harness\|gsm8k\|5": { "acc": 0.001516300227445034, "acc_stderr": 0.001071779348549263 }, "harness\|winogrande\|5": { "acc": 0.5753749013417522, "acc_stderr": 0.013891893150264225 } } ``` ### 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]
joey234/mmlu-high_school_biology-original-neg	--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 24421.548387096773 num_examples: 69 download_size: 18136 dataset_size: 24421.548387096773 --- # Dataset Card for "mmlu-high_school_biology-original-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-129000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1013978 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
Jeffzera/Jesus	--- license: openrail ---
BangumiBase/nonnonbiyori	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Non Non Biyori This is the image base of bangumi Non Non Biyori, we detected 30 characters, 4423 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 692 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 576 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 56 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 18 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 13 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 161 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 37 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 37 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 591 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 18 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 15 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 27 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 194 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 34 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 36 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 174 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 14 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 15 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 52 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 92 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 20 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 1032 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 27 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 7 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| N/A \| \| 24 \| 177 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 83 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 49 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 51 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 13 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| noise \| 112 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
CyberHarem/light_cruiser_oni_kantaicollection	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of light_cruiser_oni/軽巡棲鬼 (Kantai Collection) This is the dataset of light_cruiser_oni/軽巡棲鬼 (Kantai Collection), containing 79 images and their tags. The core tags of this character are `black_hair, long_hair, blue_eyes, hair_bun, double_bun, breasts, glowing_eyes, colored_skin, white_skin, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 79 \| 70.74 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 79 \| 52.70 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 177 \| 102.26 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 79 \| 68.62 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 177 \| 124.41 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/light_cruiser_oni_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 10 \| ![](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) \| 1boy, 1girl, blush, hetero, penis, solo_focus, abyssal_ship, sweat, glowing, paizuri, cum_on_breasts, bar_censor, open_mouth, collarbone, gauntlets, gloves, grin, male_pubic_hair, mosaic_censoring, simple_background, torn_clothes \| \| 1 \| 52 \| ![](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) \| abyssal_ship, 1girl, solo, glowing, gauntlets, looking_at_viewer, skirt, cleavage, serafuku, smile \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1boy \| 1girl \| blush \| hetero \| penis \| solo_focus \| abyssal_ship \| sweat \| glowing \| paizuri \| cum_on_breasts \| bar_censor \| open_mouth \| collarbone \| gauntlets \| gloves \| grin \| male_pubic_hair \| mosaic_censoring \| simple_background \| torn_clothes \| solo \| looking_at_viewer \| skirt \| cleavage \| serafuku \| smile \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------\|:--------\|:--------\|:---------\|:--------\|:-------------\|:---------------\|:--------\|:----------\|:----------\|:-----------------\|:-------------\|:-------------\|:-------------\|:------------\|:---------\|:-------\|:------------------\|:-------------------\|:--------------------\|:---------------\|:-------\|:--------------------\|:--------\|:-----------\|:-----------\|:--------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| 1 \| 52 \| ![](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 \|
hk742/vaya-gpt-flagged-answers	--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> ## 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]
Falcon96/clonar	--- license: openrail ---
lamnt2008/lam_gender	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': female '1': male splits: - name: train num_bytes: 886700538.492 num_examples: 188402 - name: validation num_bytes: 34511251.337 num_examples: 10617 download_size: 1046144749 dataset_size: 921211789.829 --- # Dataset Card for "lam_gender" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aatherton2024/images_finalpject_2	--- dataset_info: features: - name: image dtype: image - name: classification dtype: string splits: - name: train num_bytes: 338530234.0 num_examples: 661 download_size: 77656691 dataset_size: 338530234.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
paulpanwang/POPE_Dataset	--- license: mit --- This is the dataset corresponding to the paper's experiments, used to reproduce the accuracy mentioned in the paper. [POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference](https://arxiv.org/abs/2305.15727) Please download and unzip the dataset into './data'.
jerome-white/alpaca-bt-stan	--- license: cc-by-nc-4.0 dataset_info: features: - name: parameter dtype: string - name: sample dtype: int64 - name: value dtype: float64 - name: chain dtype: int64 - name: element dtype: string splits: - name: train num_bytes: 249792000 num_examples: 4640000 download_size: 72590172 dataset_size: 249792000 configs: - config_name: default data_files: - split: train path: data/train-* ---
DynamicSuperb/SpeechDetection_LibriSpeech-TestClean	--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 27340178.435114503 num_examples: 200 download_size: 28333588 dataset_size: 27340178.435114503 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "speechDetection_LibrispeechTestClean" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Roh2014/sentiment140_10k_tweets	--- license: unknown ---
assafm/cs-combined-002	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 319673 num_examples: 1559 download_size: 122375 dataset_size: 319673 --- # Dataset Card for "cs-combined-002" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alexcom/analisis-sentimeinto-textos-turisitcos-mx-pais	--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 72164531 num_examples: 176192 - name: test num_bytes: 30692934 num_examples: 75510 download_size: 62463153 dataset_size: 102857465 --- # Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-pais" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hippocrates/medical_meadow_mmmlu_train	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 3507993 num_examples: 3787 download_size: 1633148 dataset_size: 3507993 --- # Dataset Card for "medical_meadow_mmmlu_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mostafa3zazi/Arabic_SQuAD	--- dataset_info: features: - name: index dtype: string - name: question dtype: string - name: context dtype: string - name: text dtype: string - name: answer_start dtype: int64 - name: c_id dtype: int64 splits: - name: train num_bytes: 61868003 num_examples: 48344 download_size: 10512179 dataset_size: 61868003 --- # Dataset Card for "Arabic_SQuAD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) --- # Citation ``` @inproceedings{mozannar-etal-2019-neural, title = "Neural {A}rabic Question Answering", author = "Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem", booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4612", doi = "10.18653/v1/W19-4612", pages = "108--118", abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.", } ``` ---
dderr/webtest3	--- configs: - config_name: a data_files: - split: train path: a/* - config_name: b data_files: - split: train path: b/* --- ### mytest
FarhatMay/coco_fine_tuning_diffusers	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 33175170.0 num_examples: 200 download_size: 33082020 dataset_size: 33175170.0 --- # Dataset Card for "coco_fine_tuning_diffusers" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
anamhira/foundation_action	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: prompt dtype: string - name: output dtype: string splits: - name: train num_bytes: 663896 num_examples: 289 - name: valid num_bytes: 8842 num_examples: 3 download_size: 134650 dataset_size: 672738 --- # Dataset Card for "foundation_action" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
C-MTEB/PAWSX	--- 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: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: int32 splits: - name: train num_bytes: 10420251 num_examples: 49401 - name: validation num_bytes: 457128 num_examples: 2000 - name: test num_bytes: 458674 num_examples: 2000 download_size: 8881168 dataset_size: 11336053 --- # Dataset Card for "PAWSX" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Akshayxx/CoraDatasetV4	--- dataset_info: features: - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1328483 num_examples: 1768 - name: test num_bytes: 173380 num_examples: 222 - name: validation num_bytes: 164474 num_examples: 221 download_size: 887011 dataset_size: 1666337 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
Lollitor/PROTEINMARKED	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: ID dtype: string - name: INPUT dtype: string - name: LABEL dtype: float64 splits: - name: train num_bytes: 5509793.334042051 num_examples: 7619 - name: validation num_bytes: 612520.6659579495 num_examples: 847 download_size: 3212897 dataset_size: 6122314.0 --- # Dataset Card for "PROTEINMARKED" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Corran/Arxiv_V12July23_Post2013CS_AllMiniV2L6	--- dataset_info: features: - name: id dtype: string - name: submitter dtype: string - name: authors dtype: string - name: title dtype: string - name: comments dtype: string - name: journal-ref dtype: string - name: doi dtype: string - name: report-no dtype: string - name: categories dtype: string - name: license dtype: string - name: abstract dtype: string - name: versions list: - name: created dtype: string - name: version dtype: string - name: update_date dtype: string - name: authors_parsed sequence: sequence: string - name: embeddings sequence: float32 - name: paper_title dtype: string - name: paper_url_abs dtype: string - name: paper_url_pdf dtype: string - name: repo_url dtype: string - name: is_official dtype: bool - name: mentioned_in_paper dtype: bool - name: pwc_url dtype: string - name: abs_enc sequence: float32 splits: - name: train num_bytes: 2949900219 num_examples: 612833 download_size: 3236905950 dataset_size: 2949900219 --- # Dataset Card for "Arxiv_V12July23_Post2013CS_AllMiniV2L6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
phusroyal/ViHOS	--- annotations_creators: - crowdsourced license: mit multilinguality: - monolingual source_datasets: - original task_ids: - hate-speech-detection task_categories: - text-classification - token-classification language: - vi pretty_name: ViHOS - Vietnamese Hate and Offensive Spans Dataset size_categories: - 10K<n<100K configs: - config_name: default data_files: - split: train_sequence_labeling path: - "train_sequence_labeling/syllable/train_BIO_syllable.csv" - "train_sequence_labeling/syllable/dev_BIO_syllable.csv" - "train_sequence_labeling/syllable/test_BIO_syllable.csv" - "train_sequence_labeling/word/train_BIO_syllable.csv" - "train_sequence_labeling/word/dev_BIO_syllable.csv" - "train_sequence_labeling/word/test_BIO_syllable.csv" - split: train_span_extraction path: - 'train_span_extraction/train.csv' - 'train_span_extraction/dev.csv' - split: test path: "test/test.csv" --- Disclaimer: This project contains real comments that could be considered profane, offensive, or abusive. # Dataset Card for "ViHOS - Vietnamese Hate and Offensive Spans Dataset" ## Dataset Description - Repository: [ViHOS](https://github.com/phusroyal/ViHOS) - Paper: [EACL-ViHOS](https://aclanthology.org/2023.eacl-main.47/) - Total amount of disk used: 2.6 MB ## Dataset Motivation The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k online comments. Our goal is to create a dataset that contains comprehensive hate and offensive thoughts, meanings, or opinions within the comments rather than just a lexicon of hate and offensive terms. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Futhermore, our solutions to deal with nine different online foul linguistic phenomena are also provided in the [paper](https://aclanthology.org/2023.eacl-main.47/) (e.g. Teencodes; Metaphors, metonymies; Hyponyms; Puns...). We hope that this dataset will be useful for researchers and practitioners in the field of hate speech detection in general and hate spans detection in particular. ## Dataset Summary ViHOS contains 26,476 human-annotated spans on 11,056 comments (5,360 comments have hate and offensive spans, and 5,696 comments do not) It is splitted into train, dev, and test set with following information: 1. Train set: 8,844 comments 2. Dev set: 1,106 comments 3. Test set: 1,106 comments ## Data Instance An span extraction-based (see Data Structure for more details) example of 'test' looks as follows: ``` { "content": "Thối CC chỉ không ngửi đuợc thôi", 'index_spans': "[0, 1, 2, 3, 5, 6]" } ``` An sequence labeling-based (see Data Structure for more details) example of 'test' looks as follows: ``` { "content": "Thối CC chỉ không ngửi đuợc thôi", 'index_spans': ["B-T", "I-T", "O", "O", "O", "O", "O"] } ``` ## Data Structure Here is our data folder structure! ``` . └── data/ ├── train_sequence_labeling/ │ ├── syllable/ │ │ ├── dev_BIO_syllable.csv │ │ ├── test_BIO_syllable.csv │ │ └── train_BIO_syllable.csv │ └── word/ │ ├── dev_BIO_Word.csv │ ├── test_BIO_Word.csv │ └── train_BIO_Word.csv ├── train_span_extraction/ │ ├── dev.csv │ └── train.csv └── test/ └── test.csv ``` ### Sequence labeling-based version #### Syllable Description: - This folder contains the data for the sequence labeling-based version of the task. The data is divided into two files: train, and dev. Each file contains the following columns: - index: The id of the word. - word: Words in the sentence after the processing of tokenization using [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) tokenizer followed by underscore tokenization. The reason for this is that some words are in bad format: e.g. "điện.thoại của tôi" is split into ["điện.thoại", "của", "tôi"] instead of ["điện", "thoại", "của", "tôi"] if we use space tokenization, which is not in the right format of Syllable. As that, we used VnCoreNLP to tokenize first and then split words into tokens. e.g. "điện.thoại của tôi" ---(VnCoreNLP)---> ["điện_thoại", "của", "tôi"] ---(split by "_")---> ["điện", "thoại", "của", "tôi"]. - tag: The tag of the word. The tag is either B-T (beginning of a word), I-T (inside of a word), or O (outside of a word). - The train_BIO_syllable and dev_BIO_syllable file are used for training and validation for XLMR model, respectively. - The test_BIO_syllable file is used for reference only. It is not used for testing the model. Please use the test.csv file in the Testdata folder for testing the model. #### Word Description: - This folder contains the data for the sequence labeling-based version of the task. The data is divided into two files: train, and dev. Each file contains the following columns: - index: The id of the word. - word: Words in the sentence after the processing of tokenization using [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) tokenizer - tag: The tag of the word. The tag is either B-T (beginning of a word), I-T (inside of a word), or O (outside of a word). - The train_BIO_Word and dev_BIO_Word file are used for training and validation for PhoBERT model, respectively. - The test_BIO_Word file is used for reference only. It is not used for testing the model. Please use the test.csv file in the data/test folder for testing the model. ### Span Extraction-based version Description: - This folder contains the data for the span extraction-based version of the task. The data is divided into two files: train and dev. Each file contains the following columns: - content: The content of the sentence. - span_ids: The index of the hate and offensive spans in the sentence. The index is in the format of [start, end] where start is the index of the first character of the hate and offensive span and end is the index of the last character of the hate and offensive span. - The train and dev file are used for training and validation for BiLSTM-CRF model, respectively. ### Citation Information ``` @inproceedings{hoang-etal-2023-vihos, title = "{V}i{HOS}: Hate Speech Spans Detection for {V}ietnamese", author = "Hoang, Phu Gia and Luu, Canh Duc and Tran, Khanh Quoc and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.eacl-main.47", doi = "10.18653/v1/2023.eacl-main.47", pages = "652--669", abstract = "The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k comments. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Besides, we conduct experiments with various state-of-the-art models. Specifically, XLM-R{\_}Large achieved the best F1-scores in Single span detection and All spans detection, while PhoBERT{\_}Large obtained the highest in Multiple spans detection. Finally, our error analysis demonstrates the difficulties in detecting specific types of spans in our data for future research. Our dataset is released on GitHub.", } ```
felipebandeira/invoiceupload1	--- license: mit dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 234466949.0 num_examples: 425 - name: test num_bytes: 15053216.0 num_examples: 26 - name: validation num_bytes: 26678659.0 num_examples: 50 download_size: 197788456 dataset_size: 276198824.0 ---
justinsiow/UECFOOD100	--- license: apache-2.0 ---
kalhosni/CustomerChurnTelecom	--- license: apache-2.0 ---
esue/p_dataset	--- license: mit ---
open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16	--- pretty_name: Evaluation run of OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16](https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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 agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T03:42:28.997128](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16/blob/main/results_2023-10-28T03-42-28.997128.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.360633389261745,\n\ \ \"em_stderr\": 0.004917536525106699,\n \"f1\": 0.4180935402684579,\n\ \ \"f1_stderr\": 0.004778710905980245,\n \"acc\": 0.5268440191410464,\n\ \ \"acc_stderr\": 0.012939810741097795\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.360633389261745,\n \"em_stderr\": 0.004917536525106699,\n\ \ \"f1\": 0.4180935402684579,\n \"f1_stderr\": 0.004778710905980245\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3457164518574678,\n \ \ \"acc_stderr\": 0.013100422990441578\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7079715864246251,\n \"acc_stderr\": 0.012779198491754013\n\ \ }\n}\n```" repo_url: https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|arc:challenge\|25_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-10-03T23-40-22.620996.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T21_47_43.594265 path: - '/details_harness\|drop\|3_2023-10-27T21-47-43.594265.parquet' - split: 2023_10_28T03_42_28.997128 path: - '/details_harness\|drop\|3_2023-10-28T03-42-28.997128.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-28T03-42-28.997128.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T21_47_43.594265 path: - '/details_harness\|gsm8k\|5_2023-10-27T21-47-43.594265.parquet' - split: 2023_10_28T03_42_28.997128 path: - '/details_harness\|gsm8k\|5_2023-10-28T03-42-28.997128.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-28T03-42-28.997128.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hellaswag\|10_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T23-40-22.620996.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-management\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_03T23_40_22.620996 path: - '/details_harness\|truthfulqa:mc\|0_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-10-03T23-40-22.620996.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T21_47_43.594265 path: - '/details_harness\|winogrande\|5_2023-10-27T21-47-43.594265.parquet' - split: 2023_10_28T03_42_28.997128 path: - '/details_harness\|winogrande\|5_2023-10-28T03-42-28.997128.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-28T03-42-28.997128.parquet' - config_name: results data_files: - split: 2023_10_03T23_40_22.620996 path: - results_2023-10-03T23-40-22.620996.parquet - split: 2023_10_27T21_47_43.594265 path: - results_2023-10-27T21-47-43.594265.parquet - split: 2023_10_28T03_42_28.997128 path: - results_2023-10-28T03-42-28.997128.parquet - split: latest path: - results_2023-10-28T03-42-28.997128.parquet --- # Dataset Card for Evaluation run of OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16 ## Dataset Description - Homepage: - Repository: https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16 - 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 [OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16](https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T03:42:28.997128](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16/blob/main/results_2023-10-28T03-42-28.997128.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.360633389261745, "em_stderr": 0.004917536525106699, "f1": 0.4180935402684579, "f1_stderr": 0.004778710905980245, "acc": 0.5268440191410464, "acc_stderr": 0.012939810741097795 }, "harness\|drop\|3": { "em": 0.360633389261745, "em_stderr": 0.004917536525106699, "f1": 0.4180935402684579, "f1_stderr": 0.004778710905980245 }, "harness\|gsm8k\|5": { "acc": 0.3457164518574678, "acc_stderr": 0.013100422990441578 }, "harness\|winogrande\|5": { "acc": 0.7079715864246251, "acc_stderr": 0.012779198491754013 } } ``` ### 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]
ineoApp/facture_ds_01	--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: bboxes sequence: sequence: int64 - name: ner_tags sequence: class_label: names: '0': O '1': numero facture '2': fournisseur '3': date facture '4': date limite '5': montant ht '6': montant ttc '7': tva '8': prix tva '9': addresse '10': reference '11': art1 designation '12': art1 quantite '13': art1 prix unit '14': art1 tva '15': art1 montant ht '16': art2 designation '17': art2 quantite '18': art2 prix unit '19': art2 tva '20': art2 montant ht - name: tokens sequence: string splits: - name: train num_bytes: 14736563.333333334 num_examples: 14 - name: test num_bytes: 4210446.666666667 num_examples: 4 download_size: 6308297 dataset_size: 18947010.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
namespace-Pt/msmarco	--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: query dtype: string - name: positive sequence: string splits: - name: dev num_bytes: 2962960 num_examples: 6980 download_size: 1925216 dataset_size: 2962960 --- # Dataset Card for "msmarco" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akkasi/clmet	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: labels sequence: float64 - name: label2idx dtype: string - name: idx2label dtype: string splits: - name: train num_bytes: 149061943 num_examples: 266 - name: test num_bytes: 50034891 num_examples: 67 download_size: 117110210 dataset_size: 199096834 --- # Dataset Card for "clmet_new" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
youngs1998/DeepSpace_KE	--- license: mit language: - zh size_categories: - 1K<n<10K ---
smudkavi/indic_language_corpus	--- license: mit ---
HydraLM/partitioned_v3_standardized_01	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 15176523.9300594 num_examples: 28224 download_size: 9592708 dataset_size: 15176523.9300594 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_01" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-source-metrics/preprocessed_stars	--- dataset_info: features: - name: transformers dtype: int64 - name: peft dtype: int64 - name: evaluate dtype: int64 - name: huggingface_hub dtype: int64 - name: accelerate dtype: int64 - name: datasets dtype: int64 - name: optimum dtype: int64 - name: pytorch_image_models dtype: int64 - name: gradio dtype: int64 - name: tokenizers dtype: int64 - name: diffusers dtype: int64 - name: safetensors dtype: int64 - name: sentence_transformers dtype: int64 - name: candle dtype: int64 - name: text_generation_inference dtype: int64 - name: chat_ui dtype: int64 - name: hub_docs dtype: int64 - name: openai_python dtype: int64 - name: stable_diffusion_webui dtype: int64 - name: langchain dtype: int64 - name: pytorch dtype: int64 - name: tensorflow dtype: int64 - name: day dtype: string splits: - name: raw num_bytes: 159366994 num_examples: 786512 - name: wow num_bytes: 746681 num_examples: 3685 download_size: 16972346 dataset_size: 160113675 configs: - config_name: default data_files: - split: raw path: data/raw-* - split: wow path: data/wow-* --- # Dataset Card for "preprocessed_stars" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Siddharthr30/multilabel_sentiment_analysis	--- 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: text dtype: string - name: labels sequence: int64 splits: - name: train num_bytes: 1183783 num_examples: 2260 - name: validation num_bytes: 334615 num_examples: 642 - name: test num_bytes: 335307 num_examples: 643 download_size: 86464 dataset_size: 1853705 --- # Dataset Card for "multilabel_sentiment_analysis" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lhallee/ec_feature_ranking	--- dataset_info: features: - name: Entry dtype: string - name: EC number dtype: string - name: Sequence dtype: string - name: 1st dtype: int64 - name: Class dtype: int64 - name: group dtype: int64 splits: - name: train num_bytes: 232772386 num_examples: 530876 download_size: 219414643 dataset_size: 232772386 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ec_feature_ranking" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
datahrvoje/twitter_dataset_1712997900	--- 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: 25665 num_examples: 56 download_size: 13184 dataset_size: 25665 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/yoneme_mei_lovelivesuperstar	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yoneme_mei/米女メイ/요네메메이 (Love Live! Superstar!!) This is the dataset of yoneme_mei/米女メイ/요네메메이 (Love Live! Superstar!!), containing 200 images and their tags. The core tags of this character are `red_hair, blue_eyes, bangs, hair_bun, long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 200 \| 288.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 200 \| 144.74 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 444 \| 302.80 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 200 \| 247.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 444 \| 482.66 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/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/yoneme_mei_lovelivesuperstar', 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 \| 6 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, looking_at_viewer, solo, collarbone, short_sleeves, sidelocks, single_side_bun, smile, upper_body, birthday, blush, shiny_hair, single_hair_bun, dress, necktie \| \| 1 \| 17 \| ![](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, solo, yuigaoka_school_uniform, blue_jacket, grey_dress, looking_at_viewer, collared_shirt, white_shirt, long_sleeves, white_background, blush, simple_background, hair_between_eyes, open_jacket, pinafore_dress, closed_mouth, brown_footwear, loafers, medium_hair, smile \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| blush, yuigaoka_school_uniform, 2girls, shiny_hair, upper_body, birthday, double_bun, sidelocks, solo_focus, collared_shirt, jacket, open_mouth \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| collarbone \| short_sleeves \| sidelocks \| single_side_bun \| smile \| upper_body \| birthday \| blush \| shiny_hair \| single_hair_bun \| dress \| necktie \| yuigaoka_school_uniform \| blue_jacket \| grey_dress \| collared_shirt \| white_shirt \| long_sleeves \| white_background \| simple_background \| hair_between_eyes \| open_jacket \| pinafore_dress \| closed_mouth \| brown_footwear \| loafers \| medium_hair \| 2girls \| double_bun \| solo_focus \| jacket \| open_mouth \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:-------------\|:----------------\|:------------\|:------------------\|:--------\|:-------------\|:-----------\|:--------\|:-------------\|:------------------\|:--------\|:----------\|:--------------------------\|:--------------\|:-------------\|:-----------------\|:--------------\|:---------------\|:-------------------\|:--------------------\|:--------------------\|:--------------\|:-----------------\|:---------------\|:-----------------\|:----------\|:--------------\|:---------\|:-------------\|:-------------\|:---------\|:-------------\| \| 0 \| 6 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 17 \| ![](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 \| \| \| \| \| \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \|
AdapterOcean/med_alpaca_standardized_cluster_87	--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 102798830 num_examples: 10573 download_size: 30260860 dataset_size: 102798830 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_87" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AnimaLab/bias-test-gpt-sentences	--- license: apache-2.0 language: - en pretty_name: BiasTestGPT size_categories: - 10K<n<100K --- # Dataset Card for "BiasTestGPT: Generated Test Sentences" Dataset of sentences for bias testing in open-sourced Pretrained Language Models generated using ChatGPT and other generative Language Models. This dataset is used and actively populated by the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs). - [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs) - [Dataset with Bias Specifications](https://huggingface.co/datasets/AnimaLab/bias-test-gpt-biases) - [Project Landing Page](https://biastest-animalab.github.io/) ## Dataset Structure The dataset is structured as a set of CSV files with names corresponding to the social group term for which the test sentences were generated. Each separate file contains the sentences generated with the instruction of connecting this social group term to other attribute terms. ### Data Instances Examples of 'generated sentences' from the dataset in CSV format look as follows (not all columns are shown). \| sentence \| alt_sentence \| org_grp_term \| att_term \| grp_term1 \| grp_term2 \| type \| gen_model \| \|----------\|--------------\|--------------\|----------\|-----------\|-----------\|------\|----------:\| \| Adam felt a sense of freedom as he left his old job and pursued his passion. \| Jane felt a sense of freedom as she left her old job and pursued her passion. \| Adam \| freedom \| Adam \| Jane \| paper \| gpt-3.5 \| \| My husband and I are excited to start a family together. \| My wife and I are excited to start a family together. \| husband \| family \| husband \| wife \| tool \| gpt-3.5 \| My daughter loves to experiment with different ingredients when she's cooking. \| My son loves to experiment with different ingredients when he's cooking. \| daughter \| cooking \| daughter \| son \| paper \| gpt-3.5 \| \| A woman solves complex math problems with ease \| A man solves complex math problems with ease \| woman \| math \| woman \| man \| tool \| gpt-3.5 ### Data Fields Here we describe the data fields in the dataset. These are the same across all the splits. #### CSV columns - 'sentence': a 'string' feature - PLM generated test sentence that includes 'grp_term1' and 'att_term' - 'alt_sentence': a 'string' feature - PLM generated alternative version of the test sentence that includes 'grp_term2' and 'att_term' - 'org_grp_term': a `string' feature - a social group term for which the sentence was generated. - 'att_term': a 'string' feature - an attribute term for which the sentence was created. - 'template': a 'string' feature - a templated version of the sentence with social group replaced by [T] - 'alt_template': a 'string' feature - a templated version of the sentence with social group replaced by [T] and other token differences replaced by [R] - 'grp_term1' - a 'string' feature - a term from social group 1 used in 'sentence' - 'grp_term2' - a 'string' feature - a term from social group 2 used in 'alt_sentence' - 'grp_refs' - a 'list' feature - a list of differences between the 'sentence' and 'alt_sentence' apart of group_term. Each item is a tuple with paired versions of tokens from 'sentence' and 'alt_sentnece'. - 'label_1' - a 'string' feature - whether filling in the template with group term 1 is considered to produce a 'stereotype' or 'anti-stereotype' - 'label_2' - a 'string' feature - whether filling in the template with group term 2 is considered to produce a 'stereotype' or 'anti-stereotype' - 'bias_spec' - a 'string' feature - the name of the bias specification for which the sentence was generated - 'type': a 'string' feature - the source of the generation; `paper' indicates the sentence was used in the analysis in the paper, another value indicates the sentence generated using the HuggingFace tool - 'gen_model': a 'string' feature - the name of the generator model used ### Data Splits The repository contains 14k+ sentences generated using ChatGPT and another very large PLM. The analysis in the paper was conducted using the sentences from ChatGPT only. Additional test sentences have been added afterward as a result of interaction with the tool. We note that the number of sentences is constantly growing as it is being populated by the interactions with the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs). \| Type \| Meaning \| Train \| \|--------\|---------\|------:\| \| paper \| Test sentences used in the analysis in the paper \| 9k+ \| \| tool \| Novel test sentences added to the dataset based on interactions with the [bias test tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs) \| 500+ \|
dqymaggie/brighten-300-dataset	--- dataset_info: features: - name: instruction dtype: string - name: input_image dtype: image - name: ground_truth_image dtype: image splits: - name: train num_bytes: 4739155776.0 num_examples: 300 download_size: 4615985191 dataset_size: 4739155776.0 --- # Dataset Card for "brighten-300-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TinyPixel/claude_multiround_chat_1k	--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 17754888 num_examples: 1609 download_size: 9514689 dataset_size: 17754888 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "claude_multiround_chat_1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zeroshot/arxiv-biology	--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc0-1.0 multilinguality: - monolingual --- ![;)](https://media.giphy.com/media/xd9HUXswWPY1EEJ80a/giphy.gif) ### Dataset Curators The original data is maintained by [ArXiv](https://arxiv.org/) ### Licensing Information The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/) ### Citation Information ``` @misc{clement2019arxiv, title={On the Use of ArXiv as a Dataset}, author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi}, year={2019}, eprint={1905.00075}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```
faizalbs777/research	--- license: mit task_categories: - text-generation - summarization - table-question-answering --- # QTSumm Dataset The QTSumm dataset is a large-scale dataset for the task of query-focused summarization over tabular data. It contains 7,111 human-annotated query-summary pairs over 2,934 tables covering diverse topics. To solve this task, a text generation system has to perform human-like reasoning and analysis over the given table to generate a tailored summary. ## Citation ``` @misc{zhao2023qtsumm, title={QTSumm: Query-Focused Summarization over Tabular Data}, author={Yilun Zhao and Zhenting Qi and Linyong Nan and Boyu Mi and Yixin Liu and Weijin Zou and Simeng Han and Ruizhe Chen and Xiangru Tang and Yumo Xu and Arman Cohan and Dragomir Radev}, year={2023}, eprint={2305.14303}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
RFDweeb/Samplekeybpm	--- license: unknown ---
livinNector/indic_corp	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11971668653 num_examples: 31542969 download_size: 4821559421 dataset_size: 11971668653 --- # Dataset Card for "indic_corp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pccl-org/formal-logic-simple-order-new-objects-paired-thicker-2000	--- dataset_info: features: - name: greater_than dtype: string - name: less_than dtype: string - name: paired_example sequence: sequence: string - name: correct_example sequence: string - name: incorrect_example sequence: string - name: distance dtype: int64 - name: index dtype: int64 - name: index_in_distance dtype: int64 splits: - name: train num_bytes: 513562624 num_examples: 1997003 download_size: 162420554 dataset_size: 513562624 configs: - config_name: default data_files: - split: train path: data/train-* ---
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_160m_thr_1.0_seed_3	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43586042 num_examples: 18929 - name: epoch_1 num_bytes: 44146512 num_examples: 18929 - name: epoch_2 num_bytes: 44256387 num_examples: 18929 - name: epoch_3 num_bytes: 44308733 num_examples: 18929 - name: epoch_4 num_bytes: 44353098 num_examples: 18929 - name: epoch_5 num_bytes: 44378040 num_examples: 18929 - name: epoch_6 num_bytes: 44403487 num_examples: 18929 - name: epoch_7 num_bytes: 44415332 num_examples: 18929 - name: epoch_8 num_bytes: 44425863 num_examples: 18929 - name: epoch_9 num_bytes: 44443535 num_examples: 18929 - name: epoch_10 num_bytes: 44439562 num_examples: 18929 - name: epoch_11 num_bytes: 44440304 num_examples: 18929 - name: epoch_12 num_bytes: 44443403 num_examples: 18929 - name: epoch_13 num_bytes: 44446694 num_examples: 18929 - name: epoch_14 num_bytes: 44449228 num_examples: 18929 - name: epoch_15 num_bytes: 44448355 num_examples: 18929 - name: epoch_16 num_bytes: 44449749 num_examples: 18929 - name: epoch_17 num_bytes: 44448622 num_examples: 18929 - name: epoch_18 num_bytes: 44452122 num_examples: 18929 - name: epoch_19 num_bytes: 44453828 num_examples: 18929 - name: epoch_20 num_bytes: 44455832 num_examples: 18929 - name: epoch_21 num_bytes: 44455503 num_examples: 18929 - name: epoch_22 num_bytes: 44455394 num_examples: 18929 - name: epoch_23 num_bytes: 44455257 num_examples: 18929 - name: epoch_24 num_bytes: 44456872 num_examples: 18929 - name: epoch_25 num_bytes: 44456475 num_examples: 18929 - name: epoch_26 num_bytes: 44457961 num_examples: 18929 - name: epoch_27 num_bytes: 44456736 num_examples: 18929 - name: epoch_28 num_bytes: 44457605 num_examples: 18929 - name: epoch_29 num_bytes: 44460162 num_examples: 18929 download_size: 1401205198 dataset_size: 1331756693 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
open-llm-leaderboard/details_kyujinpy__SOLAR-Platypus-10.7B-v1	--- pretty_name: Evaluation run of kyujinpy/SOLAR-Platypus-10.7B-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kyujinpy/SOLAR-Platypus-10.7B-v1](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v1)\ \ 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__SOLAR-Platypus-10.7B-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T16:18:16.203947](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__SOLAR-Platypus-10.7B-v1/blob/main/results_2023-12-16T16-18-16.203947.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\": 0.5995716192292146,\n\ \ \"acc_stderr\": 0.03274801514976459,\n \"acc_norm\": 0.6080034028429626,\n\ \ \"acc_norm_stderr\": 0.033508703676958934,\n \"mc1\": 0.35006119951040393,\n\ \ \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5157940312549367,\n\ \ \"mc2_stderr\": 0.01467999948196073\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5784982935153583,\n \"acc_stderr\": 0.014430197069326023,\n\ \ \"acc_norm\": 0.6168941979522184,\n \"acc_norm_stderr\": 0.014206472661672877\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6436964748058156,\n\ \ \"acc_stderr\": 0.004779276329704051,\n \"acc_norm\": 0.8422624975104561,\n\ \ \"acc_norm_stderr\": 0.003637497708934033\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.6074074074074074,\n\ \ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\ \ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013317,\n\ \ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013317\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.67,\n\ \ \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\": 0.67,\n \ \ \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6490566037735849,\n \"acc_stderr\": 0.02937364625323469,\n\ \ \"acc_norm\": 0.6490566037735849,\n \"acc_norm_stderr\": 0.02937364625323469\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.03773809990686934,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.03773809990686934\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\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\ : 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6127167630057804,\n\ \ \"acc_stderr\": 0.03714325906302064,\n \"acc_norm\": 0.6127167630057804,\n\ \ \"acc_norm_stderr\": 0.03714325906302064\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207762,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207762\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5234042553191489,\n \"acc_stderr\": 0.03265019475033582,\n\ \ \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.03265019475033582\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.42105263157894735,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406772,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406772\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7193548387096774,\n\ \ \"acc_stderr\": 0.025560604721022884,\n \"acc_norm\": 0.7193548387096774,\n\ \ \"acc_norm_stderr\": 0.025560604721022884\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4187192118226601,\n \"acc_stderr\": 0.034711928605184676,\n\ \ \"acc_norm\": 0.4187192118226601,\n \"acc_norm_stderr\": 0.034711928605184676\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047711,\n\ \ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047711\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790486,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790486\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.0261484834691533,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.0261484834691533\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6025641025641025,\n \"acc_stderr\": 0.024811920017903836,\n\ \ \"acc_norm\": 0.6025641025641025,\n \"acc_norm_stderr\": 0.024811920017903836\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3,\n \"acc_stderr\": 0.027940457136228402,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.027940457136228402\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\"\ : {\n \"acc\": 0.5672268907563025,\n \"acc_stderr\": 0.032183581077426124,\n\ \ \"acc_norm\": 0.5672268907563025,\n \"acc_norm_stderr\": 0.032183581077426124\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7926605504587156,\n \"acc_stderr\": 0.017381415563608678,\n \"\ acc_norm\": 0.7926605504587156,\n \"acc_norm_stderr\": 0.017381415563608678\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.42592592592592593,\n \"acc_stderr\": 0.03372343271653063,\n \"\ acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.03372343271653063\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.02615686752393104,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.02615686752393104\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8227848101265823,\n \"acc_stderr\": 0.024856364184503224,\n \ \ \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.024856364184503224\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575498,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575498\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n\ \ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6694214876033058,\n \"acc_stderr\": 0.04294340845212094,\n \"\ acc_norm\": 0.6694214876033058,\n \"acc_norm_stderr\": 0.04294340845212094\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.036803503712864595,\n\ \ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.036803503712864595\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.042450224863844935,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.042450224863844935\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8418803418803419,\n\ \ \"acc_stderr\": 0.02390232554956039,\n \"acc_norm\": 0.8418803418803419,\n\ \ \"acc_norm_stderr\": 0.02390232554956039\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8109833971902938,\n\ \ \"acc_stderr\": 0.014000791294407004,\n \"acc_norm\": 0.8109833971902938,\n\ \ \"acc_norm_stderr\": 0.014000791294407004\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6213872832369942,\n \"acc_stderr\": 0.02611374936131034,\n\ \ \"acc_norm\": 0.6213872832369942,\n \"acc_norm_stderr\": 0.02611374936131034\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2737430167597765,\n\ \ \"acc_stderr\": 0.014912413096372435,\n \"acc_norm\": 0.2737430167597765,\n\ \ \"acc_norm_stderr\": 0.014912413096372435\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6601307189542484,\n \"acc_stderr\": 0.027121956071388856,\n\ \ \"acc_norm\": 0.6601307189542484,\n \"acc_norm_stderr\": 0.027121956071388856\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6591639871382636,\n\ \ \"acc_stderr\": 0.026920841260776165,\n \"acc_norm\": 0.6591639871382636,\n\ \ \"acc_norm_stderr\": 0.026920841260776165\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.024748624490537382,\n\ \ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.024748624490537382\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.42242503259452413,\n\ \ \"acc_stderr\": 0.01261560047573492,\n \"acc_norm\": 0.42242503259452413,\n\ \ \"acc_norm_stderr\": 0.01261560047573492\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5845588235294118,\n \"acc_stderr\": 0.029935342707877746,\n\ \ \"acc_norm\": 0.5845588235294118,\n \"acc_norm_stderr\": 0.029935342707877746\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6045751633986928,\n \"acc_stderr\": 0.019780465954777515,\n \ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.019780465954777515\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\": 0.6816326530612244,\n \"acc_stderr\": 0.02982253379398208,\n\ \ \"acc_norm\": 0.6816326530612244,\n \"acc_norm_stderr\": 0.02982253379398208\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263686,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263686\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.35006119951040393,\n\ \ \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5157940312549367,\n\ \ \"mc2_stderr\": 0.01467999948196073\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8279400157853196,\n \"acc_stderr\": 0.010607731615247007\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.1106899166034875,\n \ \ \"acc_stderr\": 0.008642172551392492\n }\n}\n```" repo_url: https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v1 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_16T16_18_16.203947 path: - '/details_harness\|arc:challenge\|25_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-16T16-18-16.203947.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|gsm8k\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hellaswag\|10_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T16-18-16.203947.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T16-18-16.203947.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '/details_harness\|winogrande\|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-16T16-18-16.203947.parquet' - config_name: results data_files: - split: 2023_12_16T16_18_16.203947 path: - results_2023-12-16T16-18-16.203947.parquet - split: latest path: - results_2023-12-16T16-18-16.203947.parquet --- # Dataset Card for Evaluation run of kyujinpy/SOLAR-Platypus-10.7B-v1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [kyujinpy/SOLAR-Platypus-10.7B-v1](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v1) 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__SOLAR-Platypus-10.7B-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T16:18:16.203947](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__SOLAR-Platypus-10.7B-v1/blob/main/results_2023-12-16T16-18-16.203947.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": 0.5995716192292146, "acc_stderr": 0.03274801514976459, "acc_norm": 0.6080034028429626, "acc_norm_stderr": 0.033508703676958934, "mc1": 0.35006119951040393, "mc1_stderr": 0.01669794942015103, "mc2": 0.5157940312549367, "mc2_stderr": 0.01467999948196073 }, "harness\|arc:challenge\|25": { "acc": 0.5784982935153583, "acc_stderr": 0.014430197069326023, "acc_norm": 0.6168941979522184, "acc_norm_stderr": 0.014206472661672877 }, "harness\|hellaswag\|10": { "acc": 0.6436964748058156, "acc_stderr": 0.004779276329704051, "acc_norm": 0.8422624975104561, "acc_norm_stderr": 0.003637497708934033 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6447368421052632, "acc_stderr": 0.03894734487013317, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013317 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.67, "acc_stderr": 0.047258156262526066, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526066 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6490566037735849, "acc_stderr": 0.02937364625323469, "acc_norm": 0.6490566037735849, "acc_norm_stderr": 0.02937364625323469 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7152777777777778, "acc_stderr": 0.03773809990686934, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.03773809990686934 }, "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": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302064, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302064 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207762, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207762 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909281, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909281 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5234042553191489, "acc_stderr": 0.03265019475033582, "acc_norm": 0.5234042553191489, "acc_norm_stderr": 0.03265019475033582 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5379310344827586, "acc_stderr": 0.041546596717075474, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.041546596717075474 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406772, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406772 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7193548387096774, "acc_stderr": 0.025560604721022884, "acc_norm": 0.7193548387096774, "acc_norm_stderr": 0.025560604721022884 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4187192118226601, "acc_stderr": 0.034711928605184676, "acc_norm": 0.4187192118226601, "acc_norm_stderr": 0.034711928605184676 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.793939393939394, "acc_stderr": 0.03158415324047711, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.03158415324047711 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.029620227874790486, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.029620227874790486 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.844559585492228, "acc_stderr": 0.0261484834691533, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.0261484834691533 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6025641025641025, "acc_stderr": 0.024811920017903836, "acc_norm": 0.6025641025641025, "acc_norm_stderr": 0.024811920017903836 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.027940457136228402, "acc_norm": 0.3, "acc_norm_stderr": 0.027940457136228402 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5672268907563025, "acc_stderr": 0.032183581077426124, "acc_norm": 0.5672268907563025, "acc_norm_stderr": 0.032183581077426124 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7926605504587156, "acc_stderr": 0.017381415563608678, "acc_norm": 0.7926605504587156, "acc_norm_stderr": 0.017381415563608678 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.42592592592592593, "acc_stderr": 0.03372343271653063, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.03372343271653063 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.02615686752393104, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.02615686752393104 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8227848101265823, "acc_stderr": 0.024856364184503224, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.024856364184503224 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6771300448430493, "acc_stderr": 0.03138147637575498, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575498 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.648854961832061, "acc_stderr": 0.04186445163013751, "acc_norm": 0.648854961832061, "acc_norm_stderr": 0.04186445163013751 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6694214876033058, "acc_stderr": 0.04294340845212094, "acc_norm": 0.6694214876033058, "acc_norm_stderr": 0.04294340845212094 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6748466257668712, "acc_stderr": 0.036803503712864595, "acc_norm": 0.6748466257668712, "acc_norm_stderr": 0.036803503712864595 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.042450224863844935, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.042450224863844935 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8418803418803419, "acc_stderr": 0.02390232554956039, "acc_norm": 0.8418803418803419, "acc_norm_stderr": 0.02390232554956039 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8109833971902938, "acc_stderr": 0.014000791294407004, "acc_norm": 0.8109833971902938, "acc_norm_stderr": 0.014000791294407004 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6213872832369942, "acc_stderr": 0.02611374936131034, "acc_norm": 0.6213872832369942, "acc_norm_stderr": 0.02611374936131034 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2737430167597765, "acc_stderr": 0.014912413096372435, "acc_norm": 0.2737430167597765, "acc_norm_stderr": 0.014912413096372435 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6601307189542484, "acc_stderr": 0.027121956071388856, "acc_norm": 0.6601307189542484, "acc_norm_stderr": 0.027121956071388856 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6591639871382636, "acc_stderr": 0.026920841260776165, "acc_norm": 0.6591639871382636, "acc_norm_stderr": 0.026920841260776165 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7283950617283951, "acc_stderr": 0.024748624490537382, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.024748624490537382 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4645390070921986, "acc_stderr": 0.02975238965742705, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.02975238965742705 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.42242503259452413, "acc_stderr": 0.01261560047573492, "acc_norm": 0.42242503259452413, "acc_norm_stderr": 0.01261560047573492 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5845588235294118, "acc_stderr": 0.029935342707877746, "acc_norm": 0.5845588235294118, "acc_norm_stderr": 0.029935342707877746 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6045751633986928, "acc_stderr": 0.019780465954777515, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.019780465954777515 }, "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": 0.6816326530612244, "acc_stderr": 0.02982253379398208, "acc_norm": 0.6816326530612244, "acc_norm_stderr": 0.02982253379398208 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801302, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801302 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.035887028128263686, "acc_norm": 0.85, "acc_norm_stderr": 0.035887028128263686 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.35006119951040393, "mc1_stderr": 0.01669794942015103, "mc2": 0.5157940312549367, "mc2_stderr": 0.01467999948196073 }, "harness\|winogrande\|5": { "acc": 0.8279400157853196, "acc_stderr": 0.010607731615247007 }, "harness\|gsm8k\|5": { "acc": 0.1106899166034875, "acc_stderr": 0.008642172551392492 } } ``` ## 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]
kukis/itsuvoice	--- license: openrail ---
ovieyra21/mabama-v5	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 61744267.0 num_examples: 48 download_size: 60925153 dataset_size: 61744267.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ignacioct/instruction_example_qualityscorer	--- dataset_info: features: - name: instruction dtype: string - name: completion dtype: string - name: generation dtype: string - name: model_name dtype: string - name: meta struct: - name: category dtype: string - name: completion dtype: string - name: prompt dtype: string - name: source dtype: string - name: subcategory dtype: string splits: - name: train num_bytes: 1356 num_examples: 1 download_size: 13037 dataset_size: 1356 configs: - config_name: default data_files: - split: train path: data/train-* ---
Empolyon2/PokemonDataset	--- license: apache-2.0 task_categories: - image-classification language: - en tags: - text - image size_categories: - 1K<n<10K pretty_name: PokemonDataset --- --- TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging --- # Dataset Card for Pokemon Gen 1 ## Dataset Description - Short Description: This dataset comprises images along with corresponding textual prompts. It contains 149 subfolders, each representing a unique category, with multiple images. Each category is associated with specific prompts, as detailed in an accompanying Excel sheet. - Purpose: The dataset is designed for training models that can understand and generate Pokemon images based on textual prompts. - Data Collection and Processing: Images were sourced from [source of images]. Textual prompts were created to accurately describe or relate to the images. Images were processed for resizing, removing bad data, normalization, augmentation, and enhancement. ## Dataset Structure - Data Instances: A typical data instance consists of a textual prompt and a corresponding image path. - Data Fields: - `prompt`: A string containing the textual description or cue associated with the image. - `image_file`: The path to the image file related to the prompt. - Data Splits: The dataset is not explicitly split. All instances are part of a single batch. Users can create training, validation, and test splits as needed. ## Dataset Creation - Creators: This dataset was created by Kerem Topalismailoglu. - Motivation: APS360. ## Additional Information - Curation Rationale: The dataset was curated to cover a diverse range of images and corresponding descriptive prompts. - Source Data: The images were sourced from [source], ensuring a wide variety of visual content. - Annotations: The dataset does not include additional annotations beyond the image-prompt pairs. ## Usage - Using the Dataset with Hugging Face: ```python from datasets import load_dataset dataset = load_dataset("path_to_my_dataset") ``` ## Dataset Card Creation - Who Created the Dataset Card: [Your Name/Organization] ## Citation - Citations: [Include any relevant citations for the dataset or sources of the images.]
thangved/zitwaste	--- license: openrail ---
gcjavi/dataviewer-test-v3	--- dataset_info: - config_name: clean features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: gender dtype: string splits: - name: train num_bytes: 93462.0 num_examples: 3 - name: test num_bytes: 31804.0 num_examples: 1 download_size: 259706 dataset_size: 125266.0 - config_name: other features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: gender dtype: string splits: - name: train num_bytes: 93472.0 num_examples: 3 - name: test num_bytes: 31799.0 num_examples: 1 download_size: 129865 dataset_size: 125271.0 configs: - config_name: clean data_files: - split: train path: clean/train-* - split: test path: clean/test-* - config_name: other data_files: - split: train path: other/train-* - split: test path: other/test-* ---
rsouza17/modelo-ia-voz-rei2	--- license: openrail ---
Joe02/Sian_refs	--- license: other ---
FirstLast/reddit_tngrsnew	--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 448389 num_examples: 1973 download_size: 259371 dataset_size: 448389 configs: - config_name: default data_files: - split: train path: data/train-* ---
wookyungseo/koAlapaca-test	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 13188020 num_examples: 49620 download_size: 7262051 dataset_size: 13188020 configs: - config_name: default data_files: - split: train path: data/train-* ---
hayesyang/un_corpus_seed	--- dataset_info: features: - name: id dtype: int64 - name: url dtype: string splits: - name: train num_bytes: 258559 num_examples: 3733 download_size: 93162 dataset_size: 258559 --- # Dataset Card for "un_corpus_seed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/kaga_kantaicollection	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kaga/加賀 (Kantai Collection) This is the dataset of kaga/加賀 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `brown_hair, side_ponytail, brown_eyes, short_hair, long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 458.24 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 317.05 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1142 \| 636.47 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 427.57 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1142 \| 812.52 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kaga_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 45 \| ![](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, bow_(weapon), muneate, solo, yugake, arrow_(projectile), single_glove, tasuki, black_thighhighs, flight_deck, quiver, hakama_short_skirt, looking_at_viewer \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_thighhighs, japanese_clothes, muneate, skirt, solo, looking_at_viewer, sitting, tasuki, white_background \| \| 2 \| 19 \| ![](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, hakama_short_skirt, solo, tasuki, looking_at_viewer, muneate, blue_hakama, simple_background, black_thighhighs, white_background \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, black_thighhighs, cleavage, japanese_clothes, large_breasts, looking_at_viewer, skirt, solo, blush, off_shoulder, wariza, bare_shoulders, medium_breasts \| \| 4 \| 8 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, japanese_clothes, solo, muneate, looking_at_viewer, upper_body \| \| 5 \| 7 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, japanese_clothes, looking_at_viewer, simple_background, solo, tasuki, upper_body, muneate, white_background, hair_between_eyes, alternate_hairstyle, hair_down \| \| 6 \| 10 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, artist_name, blue_hakama, chibi, hair_between_eyes, hakama_short_skirt, solo, tasuki, blush, black_thighhighs, seiza, minigirl, eating, food, holding \| \| 7 \| 10 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, artist_name, chibi, hair_between_eyes, japanese_clothes, open_mouth, tasuki, solo, :d, blush, closed_eyes \| \| 8 \| 11 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, solo, hair_between_eyes, large_breasts, collarbone, looking_at_viewer, alternate_costume, blush, simple_background, white_background, long_sleeves, closed_mouth, upper_body, blue_sweater, cleavage \| \| 9 \| 20 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, solo, alternate_costume, looking_at_viewer, blue_kimono, hair_flower, obi, floral_print, blush, upper_body, oil-paper_umbrella \| \| 10 \| 5 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| 1girl, black_dress, blush, enmaided, looking_at_viewer, solo, white_apron, hair_between_eyes, maid_apron, maid_headdress, cowboy_shot, large_breasts, closed_mouth, frills, long_sleeves, puffy_short_sleeves, simple_background, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bow_(weapon) \| muneate \| solo \| yugake \| arrow_(projectile) \| single_glove \| tasuki \| black_thighhighs \| flight_deck \| quiver \| hakama_short_skirt \| looking_at_viewer \| japanese_clothes \| skirt \| sitting \| white_background \| blue_hakama \| simple_background \| cleavage \| large_breasts \| blush \| off_shoulder \| wariza \| bare_shoulders \| medium_breasts \| upper_body \| hair_between_eyes \| alternate_hairstyle \| hair_down \| artist_name \| chibi \| seiza \| minigirl \| eating \| food \| holding \| open_mouth \| :d \| closed_eyes \| collarbone \| alternate_costume \| long_sleeves \| closed_mouth \| blue_sweater \| blue_kimono \| hair_flower \| obi \| floral_print \| oil-paper_umbrella \| black_dress \| enmaided \| white_apron \| maid_apron \| maid_headdress \| cowboy_shot \| frills \| puffy_short_sleeves \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:---------------\|:----------\|:-------\|:---------\|:---------------------\|:---------------\|:---------\|:-------------------\|:--------------\|:---------\|:---------------------\|:--------------------\|:-------------------\|:--------\|:----------\|:-------------------\|:--------------\|:--------------------\|:-----------\|:----------------\|:--------\|:---------------\|:---------\|:-----------------\|:-----------------\|:-------------\|:--------------------\|:----------------------\|:------------\|:--------------\|:--------\|:--------\|:-----------\|:---------\|:-------\|:----------\|:-------------\|:-----\|:--------------\|:-------------\|:--------------------\|:---------------\|:---------------\|:---------------\|:--------------\|:--------------\|:------\|:---------------\|:---------------------\|:--------------\|:-----------\|:--------------\|:-------------\|:-----------------\|:--------------\|:---------\|:----------------------\| \| 0 \| 45 \| ![](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 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 19 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 8 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 7 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| X \| X \| \| \| \| X \| \| \| \| \| X \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 10 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| X \| \| \| \| X \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 10 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| X \| X \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 11 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| X \| X \| X \| X \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 20 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| 10 \| 5 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| X \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| X \| \| X \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \|
may-ohta/MUST-C	--- license: other ---
sled-umich/TRIP	--- annotations_creators: - expert-generated language: - en language_creators: - crowdsourced license: [] multilinguality: - monolingual pretty_name: 'TRIP: Tiered Reasoning for Intuitive Physics' size_categories: - 1K<n<10K source_datasets: - original tags: [] task_categories: - text-classification task_ids: - natural-language-inference --- # [TRIP - Tiered Reasoning for Intuitive Physics](https://aclanthology.org/2021.findings-emnlp.422/) Official dataset for [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://aclanthology.org/2021.findings-emnlp.422/). Shane Storks, Qiaozi Gao, Yichi Zhang, Joyce Chai. EMNLP Findings, 2021. For our official model and experiment code, please check [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU). ## Overview ![image](trip_sample.png) We introduce Tiered Reasoning for Intuitive Physics (TRIP), a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process. It includes dense annotations for each story capturing multiple tiers of reasoning beyond the end task. From these annotations, we propose a tiered evaluation, where given a pair of highly similar stories (differing only by one sentence which makes one of the stories implausible), systems must jointly identify (1) the plausible story, (2) a pair of conflicting sentences in the implausible story, and (3) the underlying physical states in those sentences causing the conflict. The goal of TRIP is to enable a systematic evaluation of machine coherence toward the end task prediction of plausibility. In particular, we evaluate whether a high-level plausibility prediction can be verified based on lower-level understanding, for example, physical state changes that would support the prediction. ## Download ```python from datasets import load_dataset dataset = load_dataset("sled-umich/TRIP") ``` * [HuggingFace-Dataset](https://huggingface.co/datasets/sled-umich/TRIP) * [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU) ## Cite ```bibtex @misc{storks2021tiered, title={Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding}, author={Shane Storks and Qiaozi Gao and Yichi Zhang and Joyce Chai}, year={2021}, booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021}, location={Punta Cana, Dominican Republic}, publisher={Association for Computational Linguistics}, } ```
AinzOoalGowns/Testdataset	--- license: apache-2.0 ---
SumayyaAli/accu_qa_dataset	--- task_categories: - question-answering language: - en tags: - medical pretty_name: accupuncture qa dataset size_categories: - n<1K ---
vwxyzjn/cai-conversation-dev1705622085	--- dataset_info: features: - name: init_prompt dtype: string - name: init_response dtype: string - name: critic_prompt dtype: string - name: critic_response dtype: string - name: revision_prompt dtype: string - name: revision_response dtype: string - name: prompt dtype: string - name: messages sequence: string - name: chosen sequence: string - name: rejected sequence: string splits: - name: train_sft num_bytes: 80685581 num_examples: 21268 - name: train_prefs num_bytes: 80873453 num_examples: 21269 - name: test_sft num_bytes: 4369948 num_examples: 1156 - name: test_prefs num_bytes: 4440767 num_examples: 1156 download_size: 74867178 dataset_size: 170369749 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: train_prefs path: data/train_prefs-* - split: test_sft path: data/test_sft-* - split: test_prefs path: data/test_prefs-* --- # Dataset Card for "cai-conversation-dev1705622085" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
0xMaka/trading-candles-subset-qa-format	--- license: gpl-3.0 dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: id dtype: string splits: - name: train num_bytes: 66970712.20824251 num_examples: 280033 - name: test num_bytes: 28701938.79175749 num_examples: 120015 download_size: 54828654 dataset_size: 95672651.0 ---
tasksource/arct2	--- license: apache-2.0 task_categories: - text-classification language: - en --- https://github.com/IKMLab/arct2 ```bib @inproceedings{niven-kao-2019-probing, title = "Probing Neural Network Comprehension of Natural Language Arguments", author = "Niven, Timothy and Kao, Hung-Yu", booktitle = "Proceedings of the 57th Conference of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1459", pages = "4658--4664", abstract = "We are surprised to find that BERT{'}s peak performance of 77{\%} on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline. However, we show that this result is entirely accounted for by exploitation of spurious statistical cues in the dataset. We analyze the nature of these cues and demonstrate that a range of models all exploit them. This analysis informs the construction of an adversarial dataset on which all models achieve random accuracy. Our adversarial dataset provides a more robust assessment of argument comprehension and should be adopted as the standard in future work.", } ```
brema76/political_personalization_it	--- license: mit --- <strong>Lexicon of words for investigating the political personalization phenomenon in Italian language</strong></br> List of 3,303 personalizing words in Italian language, annotated with the corresponding sentiment classification as referred to political offices.</br> Words are group by category: Moral and behavioral, Physical, Social and economic. <strong>Citation info and BibTeX entry</strong></br> <a href="" target="_blank"></a> ```bibtex @article{Bru2023, title={Combining NLP techniques and statistical modeling to analyze gender gaps in the mediated personalization of politics}, author={Brugnoli, Emanuele and Simone, Rosaria and Delmastro, Marco}, journal={}, year={2023}, volume={} } ```
nlplabtdtu/edu-crawl-with-date	--- dataset_info: features: - name: title dtype: string - name: url dtype: string - name: body dtype: string - name: date dtype: string - name: flt_dates sequence: string splits: - name: train num_bytes: 1070649713 num_examples: 278902 download_size: 387393861 dataset_size: 1070649713 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "edu-crawl-with-date" Data crawl education với dữ liệu thời gian (tháng/năm) Dữ liệu thời gian được cập nhật theo cách sau: - chiết xuất từ văn bản - crawl lại một số trang (hiếm) Hiện tại có: 190692 dòng có dữ liệu thời gian ~= 68.37 % [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SatishFaction/Test_DataSet1	--- license: cc0-1.0 --- This is a test for creating a dataset
open-phi/wile-e	--- dataset_info: features: - name: topic dtype: string - name: model dtype: string - name: concepts sequence: string - name: outline sequence: string - name: markdown dtype: string splits: - name: train num_bytes: 108171787 num_examples: 933 download_size: 41387101 dataset_size: 108171787 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wile-e" [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/8ebe0fb3	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 186 num_examples: 10 download_size: 1339 dataset_size: 186 --- # Dataset Card for "8ebe0fb3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
apailang/mini-dataset-978	--- dataset_info: features: - name: instruction dtype: string - name: input_content dtype: string - name: expected_output dtype: string splits: - name: train num_bytes: 825340 num_examples: 978 download_size: 229601 dataset_size: 825340 configs: - config_name: default data_files: - split: train path: data/train-* ---
bhavnicksm/sentihood	--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: SentiHood Dataset size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - multi-class-classification - natural-language-inference --- # Dataset Card for [SentiHood] ## 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 - Paper: https://arxiv.org/abs/1610.03771 - Leaderboard: https://paperswithcode.com/sota/aspect-based-sentiment-analysis-on-sentihood ### Dataset Summary Created as a part of the paper "SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods" by Saeidi et al. #### Abstract In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis that assumes a single entity per document and targeted sentiment analysis that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighborhoods are discussed by users. In this context units of text often mention several aspects of one or more neighborhoods. This is the first time that a generic social media platform in this case a QA platform, is used for fine-grained opinion mining. Text coming from QA platforms is far less constrained compared to text from review-specific platforms on which current datasets are based. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Monolingual (only English) ## 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 Thanks to [@Bhavnicksm](https://github.com/Bhavnicksm) for adding this dataset.
pythera/vietnamese-mlmcorpus	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 41663206615.575096 num_examples: 45009627 download_size: 23630062762 dataset_size: 41663206615.575096 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vietnamese-mlmcorpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Mihaiii__Bucharest-0.1	--- pretty_name: Evaluation run of Mihaiii/Bucharest-0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Mihaiii/Bucharest-0.1](https://huggingface.co/Mihaiii/Bucharest-0.1) 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_Mihaiii__Bucharest-0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-14T00:16:59.594031](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Bucharest-0.1/blob/main/results_2024-02-14T00-16-59.594031.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\": 0.661247276384782,\n\ \ \"acc_stderr\": 0.03141201491493503,\n \"acc_norm\": 0.6641358272243135,\n\ \ \"acc_norm_stderr\": 0.03203652707247171,\n \"mc1\": 0.3243574051407589,\n\ \ \"mc1_stderr\": 0.01638797677964794,\n \"mc2\": 0.4793790433538082,\n\ \ \"mc2_stderr\": 0.014619267505513112\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6032423208191127,\n \"acc_stderr\": 0.014296513020180642,\n\ \ \"acc_norm\": 0.6535836177474402,\n \"acc_norm_stderr\": 0.013905011180063232\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6644094801832304,\n\ \ \"acc_stderr\": 0.00471231451195098,\n \"acc_norm\": 0.854511053574985,\n\ \ \"acc_norm_stderr\": 0.003518725257365604\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810535,\n\ \ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810535\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.71,\n\ \ \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n \ \ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\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\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.55,\n \"acc_norm_stderr\": 0.05\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\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267438,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267438\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419036,\n\ \ \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419036\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4365079365079365,\n \"acc_stderr\": 0.02554284681740049,\n \"\ acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.02554284681740049\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8161290322580645,\n\ \ \"acc_stderr\": 0.02203721734026784,\n \"acc_norm\": 0.8161290322580645,\n\ \ \"acc_norm_stderr\": 0.02203721734026784\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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_european_history\|5\"\ : {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656209,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656209\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\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678185,\n\ \ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678185\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\": 0.7016806722689075,\n \"acc_stderr\": 0.02971914287634286,\n \ \ \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.02971914287634286\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8623853211009175,\n \"acc_stderr\": 0.014770105878649395,\n \"\ acc_norm\": 0.8623853211009175,\n \"acc_norm_stderr\": 0.014770105878649395\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6018518518518519,\n \"acc_stderr\": 0.033384734032074016,\n \"\ acc_norm\": 0.6018518518518519,\n \"acc_norm_stderr\": 0.033384734032074016\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8649789029535865,\n \"acc_stderr\": 0.022245776632003694,\n \ \ \"acc_norm\": 0.8649789029535865,\n \"acc_norm_stderr\": 0.022245776632003694\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7130044843049327,\n\ \ \"acc_stderr\": 0.030360379710291947,\n \"acc_norm\": 0.7130044843049327,\n\ \ \"acc_norm_stderr\": 0.030360379710291947\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\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\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.034926064766237906,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.034926064766237906\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8237547892720306,\n\ \ \"acc_stderr\": 0.013625556907993466,\n \"acc_norm\": 0.8237547892720306,\n\ \ \"acc_norm_stderr\": 0.013625556907993466\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\": 0.3888268156424581,\n\ \ \"acc_stderr\": 0.016303899530796123,\n \"acc_norm\": 0.3888268156424581,\n\ \ \"acc_norm_stderr\": 0.016303899530796123\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7810457516339869,\n \"acc_stderr\": 0.02367908986180772,\n\ \ \"acc_norm\": 0.7810457516339869,\n \"acc_norm_stderr\": 0.02367908986180772\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.026003301117885142,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.026003301117885142\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness\|hendrycksTest-professional_law\|5\"\ : {\n \"acc\": 0.4941329856584094,\n \"acc_stderr\": 0.012769356925216526,\n\ \ \"acc_norm\": 0.4941329856584094,\n \"acc_norm_stderr\": 0.012769356925216526\n\ \ },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\"\ : 0.75,\n \"acc_stderr\": 0.026303648393696036,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.026303648393696036\n },\n \"harness\|hendrycksTest-professional_psychology\|5\"\ : {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.018635594034423976,\n\ \ \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.018635594034423976\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7591836734693878,\n \"acc_stderr\": 0.02737294220178816,\n\ \ \"acc_norm\": 0.7591836734693878,\n \"acc_norm_stderr\": 0.02737294220178816\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\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3243574051407589,\n\ \ \"mc1_stderr\": 0.01638797677964794,\n \"mc2\": 0.4793790433538082,\n\ \ \"mc2_stderr\": 0.014619267505513112\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8216258879242304,\n \"acc_stderr\": 0.010759352014855922\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5708870356330553,\n \ \ \"acc_stderr\": 0.013633369425647232\n }\n}\n```" repo_url: https://huggingface.co/Mihaiii/Bucharest-0.1 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_14T00_16_59.594031 path: - '/details_harness\|arc:challenge\|25_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-14T00-16-59.594031.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|gsm8k\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hellaswag\|10_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T00-16-59.594031.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-14T00-16-59.594031.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '/details_harness\|winogrande\|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-14T00-16-59.594031.parquet' - config_name: results data_files: - split: 2024_02_14T00_16_59.594031 path: - results_2024-02-14T00-16-59.594031.parquet - split: latest path: - results_2024-02-14T00-16-59.594031.parquet --- # Dataset Card for Evaluation run of Mihaiii/Bucharest-0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Mihaiii/Bucharest-0.1](https://huggingface.co/Mihaiii/Bucharest-0.1) 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_Mihaiii__Bucharest-0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-14T00:16:59.594031](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Bucharest-0.1/blob/main/results_2024-02-14T00-16-59.594031.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": 0.661247276384782, "acc_stderr": 0.03141201491493503, "acc_norm": 0.6641358272243135, "acc_norm_stderr": 0.03203652707247171, "mc1": 0.3243574051407589, "mc1_stderr": 0.01638797677964794, "mc2": 0.4793790433538082, "mc2_stderr": 0.014619267505513112 }, "harness\|arc:challenge\|25": { "acc": 0.6032423208191127, "acc_stderr": 0.014296513020180642, "acc_norm": 0.6535836177474402, "acc_norm_stderr": 0.013905011180063232 }, "harness\|hellaswag\|10": { "acc": 0.6644094801832304, "acc_stderr": 0.00471231451195098, "acc_norm": 0.854511053574985, "acc_norm_stderr": 0.003518725257365604 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7631578947368421, "acc_stderr": 0.03459777606810535, "acc_norm": 0.7631578947368421, "acc_norm_stderr": 0.03459777606810535 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "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": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "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": 0.6936416184971098, "acc_stderr": 0.03514942551267438, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267438 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419036, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419036 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4365079365079365, "acc_stderr": 0.02554284681740049, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.02554284681740049 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8161290322580645, "acc_stderr": 0.02203721734026784, "acc_norm": 0.8161290322580645, "acc_norm_stderr": 0.02203721734026784 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.806060606060606, "acc_stderr": 0.03087414513656209, "acc_norm": 0.806060606060606, "acc_norm_stderr": 0.03087414513656209 }, "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": 0.927461139896373, "acc_stderr": 0.018718998520678185, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.018718998520678185 }, "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": 0.7016806722689075, "acc_stderr": 0.02971914287634286, "acc_norm": 0.7016806722689075, "acc_norm_stderr": 0.02971914287634286 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.32450331125827814, "acc_stderr": 0.038227469376587525, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.038227469376587525 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8623853211009175, "acc_stderr": 0.014770105878649395, "acc_norm": 0.8623853211009175, "acc_norm_stderr": 0.014770105878649395 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6018518518518519, "acc_stderr": 0.033384734032074016, "acc_norm": 0.6018518518518519, "acc_norm_stderr": 0.033384734032074016 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8649789029535865, "acc_stderr": 0.022245776632003694, "acc_norm": 0.8649789029535865, "acc_norm_stderr": 0.022245776632003694 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7130044843049327, "acc_stderr": 0.030360379710291947, "acc_norm": 0.7130044843049327, "acc_norm_stderr": 0.030360379710291947 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8543689320388349, "acc_stderr": 0.034926064766237906, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.034926064766237906 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092375, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092375 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8237547892720306, "acc_stderr": 0.013625556907993466, "acc_norm": 0.8237547892720306, "acc_norm_stderr": 0.013625556907993466 }, "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": 0.3888268156424581, "acc_stderr": 0.016303899530796123, "acc_norm": 0.3888268156424581, "acc_norm_stderr": 0.016303899530796123 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7810457516339869, "acc_stderr": 0.02367908986180772, "acc_norm": 0.7810457516339869, "acc_norm_stderr": 0.02367908986180772 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.026003301117885142, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.026003301117885142 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7530864197530864, "acc_stderr": 0.02399350170904211, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.02399350170904211 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4941329856584094, "acc_stderr": 0.012769356925216526, "acc_norm": 0.4941329856584094, "acc_norm_stderr": 0.012769356925216526 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.75, "acc_stderr": 0.026303648393696036, "acc_norm": 0.75, "acc_norm_stderr": 0.026303648393696036 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.018635594034423976, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.018635594034423976 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7591836734693878, "acc_stderr": 0.02737294220178816, "acc_norm": 0.7591836734693878, "acc_norm_stderr": 0.02737294220178816 }, "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": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3243574051407589, "mc1_stderr": 0.01638797677964794, "mc2": 0.4793790433538082, "mc2_stderr": 0.014619267505513112 }, "harness\|winogrande\|5": { "acc": 0.8216258879242304, "acc_stderr": 0.010759352014855922 }, "harness\|gsm8k\|5": { "acc": 0.5708870356330553, "acc_stderr": 0.013633369425647232 } } ``` ## 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 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open-llm-leaderboard/details_NeuralNovel__Aeryth-7B-v0.1

--- pretty_name: Evaluation run of NeuralNovel/Aeryth-7B-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [NeuralNovel/Aeryth-7B-v0.1](https://huggingface.co/NeuralNovel/Aeryth-7B-v0.1)\ \ 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 4 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_NeuralNovel__Aeryth-7B-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-14T12:31:11.639995](https://huggingface.co/datasets/open-llm-leaderboard/details_NeuralNovel__Aeryth-7B-v0.1/blob/main/results_2024-01-14T12-31-11.639995.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\": 0.607832340017972,\n\ \ \"acc_stderr\": 0.033171072669556316,\n \"acc_norm\": 0.6134606437151463,\n\ \ \"acc_norm_stderr\": 0.03384290514267795,\n \"mc1\": 0.4602203182374541,\n\ \ \"mc1_stderr\": 0.01744801722396088,\n \"mc2\": 0.6357466374094296,\n\ \ \"mc2_stderr\": 0.015661867399479723\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5631399317406144,\n \"acc_stderr\": 0.014494421584256524,\n\ \ \"acc_norm\": 0.6032423208191127,\n \"acc_norm_stderr\": 0.014296513020180646\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6514638518223461,\n\ \ \"acc_stderr\": 0.004755329243976671,\n \"acc_norm\": 0.835291774546903,\n\ \ \"acc_norm_stderr\": 0.0037015895712743134\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n\ \ \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5664739884393064,\n\ \ \"acc_stderr\": 0.03778621079092056,\n \"acc_norm\": 0.5664739884393064,\n\ \ \"acc_norm_stderr\": 0.03778621079092056\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916747,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916747\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.36772486772486773,\n \"acc_stderr\": 0.024833839825562417,\n \"\ acc_norm\": 0.36772486772486773,\n \"acc_norm_stderr\": 0.024833839825562417\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6903225806451613,\n\ \ \"acc_stderr\": 0.026302774983517414,\n \"acc_norm\": 0.6903225806451613,\n\ \ \"acc_norm_stderr\": 0.026302774983517414\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\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\ \ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124488,\n \"\ acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124488\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153303,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153303\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5666666666666667,\n \"acc_stderr\": 0.025124653525885117,\n\ \ \"acc_norm\": 0.5666666666666667,\n \"acc_norm_stderr\": 0.025124653525885117\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131143,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131143\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\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7981651376146789,\n \"acc_stderr\": 0.017208579357787586,\n \"\ acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.017208579357787586\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538271,\n \"\ acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538271\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501954,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501954\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \ \ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6278026905829597,\n\ \ \"acc_stderr\": 0.032443052830087304,\n \"acc_norm\": 0.6278026905829597,\n\ \ \"acc_norm_stderr\": 0.032443052830087304\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847836,\n\ \ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847836\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098825,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098825\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.04330043749650743,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.04330043749650743\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.045416094465039504,\n\ \ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.045416094465039504\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077785,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077785\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7841634738186463,\n\ \ \"acc_stderr\": 0.01471168438613996,\n \"acc_norm\": 0.7841634738186463,\n\ \ \"acc_norm_stderr\": 0.01471168438613996\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.02507071371915319,\n\ \ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.02507071371915319\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.34972067039106147,\n\ \ \"acc_stderr\": 0.015949308790233645,\n \"acc_norm\": 0.34972067039106147,\n\ \ \"acc_norm_stderr\": 0.015949308790233645\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\": 0.6945337620578779,\n\ \ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\ \ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765134,\n\ \ \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765134\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43285528031290743,\n\ \ \"acc_stderr\": 0.012654565234622866,\n \"acc_norm\": 0.43285528031290743,\n\ \ \"acc_norm_stderr\": 0.012654565234622866\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6139705882352942,\n \"acc_stderr\": 0.029573269134411124,\n\ \ \"acc_norm\": 0.6139705882352942,\n \"acc_norm_stderr\": 0.029573269134411124\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6225490196078431,\n \"acc_stderr\": 0.01961085147488029,\n \ \ \"acc_norm\": 0.6225490196078431,\n \"acc_norm_stderr\": 0.01961085147488029\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7454545454545455,\n\ \ \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.7454545454545455,\n\ \ \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484378,\n\ \ \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484378\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7910447761194029,\n\ \ \"acc_stderr\": 0.028748298931728655,\n \"acc_norm\": 0.7910447761194029,\n\ \ \"acc_norm_stderr\": 0.028748298931728655\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4602203182374541,\n\ \ \"mc1_stderr\": 0.01744801722396088,\n \"mc2\": 0.6357466374094296,\n\ \ \"mc2_stderr\": 0.015661867399479723\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.01222375443423362\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.36087945413191813,\n \ \ \"acc_stderr\": 0.01322862675392514\n }\n}\n```" repo_url: https://huggingface.co/NeuralNovel/Aeryth-7B-v0.1 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_07T23_22_00.392280 path: - '**/details_harness|arc:challenge|25_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|arc:challenge|25_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|arc:challenge|25_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|arc:challenge|25_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-14T12-31-11.639995.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|gsm8k|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|gsm8k|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|gsm8k|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|gsm8k|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hellaswag|10_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hellaswag|10_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hellaswag|10_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hellaswag|10_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-07T23-22-00.392280.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-08T00-11-57.804296.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-08T00-11-57.804296.parquet' - 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'**/details_harness|hendrycksTest-global_facts|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-14T12-31-11.639995.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-management|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-management|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-management|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T12-31-11.639995.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|truthfulqa:mc|0_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|truthfulqa:mc|0_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|truthfulqa:mc|0_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-14T12-31-11.639995.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_07T23_22_00.392280 path: - '**/details_harness|winogrande|5_2024-01-07T23-22-00.392280.parquet' - split: 2024_01_08T00_11_57.804296 path: - '**/details_harness|winogrande|5_2024-01-08T00-11-57.804296.parquet' - split: 2024_01_13T23_38_01.089688 path: - '**/details_harness|winogrande|5_2024-01-13T23-38-01.089688.parquet' - split: 2024_01_14T12_31_11.639995 path: - '**/details_harness|winogrande|5_2024-01-14T12-31-11.639995.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-14T12-31-11.639995.parquet' - config_name: results data_files: - split: 2024_01_07T23_22_00.392280 path: - results_2024-01-07T23-22-00.392280.parquet - split: 2024_01_08T00_11_57.804296 path: - results_2024-01-08T00-11-57.804296.parquet - split: 2024_01_13T23_38_01.089688 path: - results_2024-01-13T23-38-01.089688.parquet - split: 2024_01_14T12_31_11.639995 path: - results_2024-01-14T12-31-11.639995.parquet - split: latest path: - results_2024-01-14T12-31-11.639995.parquet --- # Dataset Card for Evaluation run of NeuralNovel/Aeryth-7B-v0.1  Dataset automatically created during the evaluation run of model [NeuralNovel/Aeryth-7B-v0.1](https://huggingface.co/NeuralNovel/Aeryth-7B-v0.1) 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_NeuralNovel__Aeryth-7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-14T12:31:11.639995](https://huggingface.co/datasets/open-llm-leaderboard/details_NeuralNovel__Aeryth-7B-v0.1/blob/main/results_2024-01-14T12-31-11.639995.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": 0.607832340017972, "acc_stderr": 0.033171072669556316, "acc_norm": 0.6134606437151463, "acc_norm_stderr": 0.03384290514267795, "mc1": 0.4602203182374541, "mc1_stderr": 0.01744801722396088, "mc2": 0.6357466374094296, "mc2_stderr": 0.015661867399479723 }, "harness|arc:challenge|25": { "acc": 0.5631399317406144, "acc_stderr": 0.014494421584256524, "acc_norm": 0.6032423208191127, "acc_norm_stderr": 0.014296513020180646 }, "harness|hellaswag|10": { "acc": 0.6514638518223461, "acc_stderr": 0.004755329243976671, "acc_norm": 0.835291774546903, "acc_norm_stderr": 0.0037015895712743134 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.631578947368421, "acc_stderr": 0.03925523381052932, "acc_norm": 0.631578947368421, "acc_norm_stderr": 0.03925523381052932 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880267, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880267 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.03942082639927213, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03942082639927213 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956911, "acc_norm": 0.51, 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0.04668000738510455, "acc_norm": 0.43859649122807015, "acc_norm_stderr": 0.04668000738510455 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6206896551724138, "acc_stderr": 0.04043461861916747, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.04043461861916747 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.36772486772486773, "acc_stderr": 0.024833839825562417, "acc_norm": 0.36772486772486773, "acc_norm_stderr": 0.024833839825562417 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6903225806451613, "acc_stderr": 0.026302774983517414, "acc_norm": 0.6903225806451613, "acc_norm_stderr": 0.026302774983517414 }, 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"acc": 0.5666666666666667, "acc_stderr": 0.025124653525885117, "acc_norm": 0.5666666666666667, "acc_norm_stderr": 0.025124653525885117 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131143, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131143 }, "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": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7981651376146789, "acc_stderr": 0.017208579357787586, "acc_norm": 0.7981651376146789, "acc_norm_stderr": 0.017208579357787586 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.03400603625538271, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.03400603625538271 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501954, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501954 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.759493670886076, "acc_stderr": 0.027820781981149685, "acc_norm": 0.759493670886076, "acc_norm_stderr": 0.027820781981149685 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6278026905829597, "acc_stderr": 0.032443052830087304, "acc_norm": 0.6278026905829597, "acc_norm_stderr": 0.032443052830087304 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7251908396946565, "acc_stderr": 0.03915345408847836, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.03915345408847836 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098825, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098825 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7222222222222222, "acc_stderr": 0.04330043749650743, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.04330043749650743 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7300613496932515, "acc_stderr": 0.034878251684978906, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.034878251684978906 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.6990291262135923, "acc_stderr": 0.045416094465039504, "acc_norm": 0.6990291262135923, "acc_norm_stderr": 0.045416094465039504 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077785, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077785 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7841634738186463, "acc_stderr": 0.01471168438613996, "acc_norm": 0.7841634738186463, "acc_norm_stderr": 0.01471168438613996 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6820809248554913, "acc_stderr": 0.02507071371915319, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.02507071371915319 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.34972067039106147, "acc_stderr": 0.015949308790233645, "acc_norm": 0.34972067039106147, "acc_norm_stderr": 0.015949308790233645 }, "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": 0.6945337620578779, "acc_stderr": 0.02616058445014045, "acc_norm": 0.6945337620578779, "acc_norm_stderr": 0.02616058445014045 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7160493827160493, "acc_stderr": 0.025089478523765134, "acc_norm": 0.7160493827160493, "acc_norm_stderr": 0.025089478523765134 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4645390070921986, "acc_stderr": 0.02975238965742705, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.02975238965742705 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.43285528031290743, "acc_stderr": 0.012654565234622866, "acc_norm": 0.43285528031290743, "acc_norm_stderr": 0.012654565234622866 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6139705882352942, "acc_stderr": 0.029573269134411124, "acc_norm": 0.6139705882352942, "acc_norm_stderr": 0.029573269134411124 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6225490196078431, "acc_stderr": 0.01961085147488029, "acc_norm": 0.6225490196078431, "acc_norm_stderr": 0.01961085147488029 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7454545454545455, "acc_stderr": 0.041723430387053825, "acc_norm": 0.7454545454545455, "acc_norm_stderr": 0.041723430387053825 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.689795918367347, "acc_stderr": 0.029613459872484378, "acc_norm": 0.689795918367347, "acc_norm_stderr": 0.029613459872484378 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7910447761194029, "acc_stderr": 0.028748298931728655, "acc_norm": 0.7910447761194029, "acc_norm_stderr": 0.028748298931728655 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness|hendrycksTest-virology|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.4602203182374541, "mc1_stderr": 0.01744801722396088, "mc2": 0.6357466374094296, "mc2_stderr": 0.015661867399479723 }, "harness|winogrande|5": { "acc": 0.7466456195737964, "acc_stderr": 0.01222375443423362 }, "harness|gsm8k|5": { "acc": 0.36087945413191813, "acc_stderr": 0.01322862675392514 } } ``` ## 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]

FINNUMBER/FINCH_TRAIN_QA_MCQA_100

--- dataset_info: features: - name: task dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: instruction dtype: string - name: output dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 423079 num_examples: 100 download_size: 257187 dataset_size: 423079 configs: - config_name: default data_files: - split: train path: data/train-* ---

nlphuji/winogavil

--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual paperswithcode_id: winogavil pretty_name: WinoGAViL size_categories: - 10K<n<100K source_datasets: - original tags: - commonsense-reasoning - visual-reasoning task_ids: [] extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes. The full license agreement is available in the dataset files." --- # Dataset Card for WinoGAViL - [Dataset Description](#dataset-description) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Colab notebook code for Winogavil evaluation with CLIP](#colab-notebook-code-for-winogavil-evaluation-with-clip) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more. - **Homepage:** https://winogavil.github.io/ - **Colab** https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi - **Repository:** https://github.com/WinoGAViL/WinoGAViL-experiments/ - **Paper:** https://arxiv.org/abs/2207.12576 - **Leaderboard:** https://winogavil.github.io/leaderboard - **Point of Contact:** winogavil@gmail.com; yonatanbitton1@gmail.com ### Supported Tasks and Leaderboards https://winogavil.github.io/leaderboard. https://paperswithcode.com/dataset/winogavil. ## Colab notebook code for Winogavil evaluation with CLIP https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi ### Languages English. ## Dataset Structure ### Data Fields candidates (list): ["bison", "shelter", "beard", "flea", "cattle", "shave"] - list of image candidates. cue (string): pogonophile - the generated cue. associations (string): ["bison", "beard", "shave"] - the images associated with the cue selected by the user. score_fool_the_ai (int64): 80 - the spymaster score (100 - model score) for fooling the AI, with CLIP RN50 model. num_associations (int64): 3 - The number of images selected as associative with the cue. num_candidates (int64): 6 - the number of total candidates. solvers_jaccard_mean (float64): 1.0 - three solvers scores average on the generated association instance. solvers_jaccard_std (float64): 1.0 - three solvers scores standard deviation on the generated association instance ID (int64): 367 - association ID. ### Data Splits There is a single TEST split. In the accompanied paper and code we sample it to create different training sets, but the intended use is to use winogavil as a test set. There are different number of candidates, which creates different difficulty levels: -- With 5 candidates, random model expected score is 38%. -- With 6 candidates, random model expected score is 34%. -- With 10 candidates, random model expected score is 24%. -- With 12 candidates, random model expected score is 19%. <details> <summary>Why random chance for success with 5 candidates is 38%?</summary> It is a binomial distribution probability calculation. Assuming N=5 candidates, and K=2 associations, there could be three events: (1) The probability for a random guess is correct in 0 associations is 0.3 (elaborate below), and the Jaccard index is 0 (there is no intersection between the correct labels and the wrong guesses). Therefore the expected random score is 0. (2) The probability for a random guess is correct in 1 associations is 0.6, and the Jaccard index is 0.33 (intersection=1, union=3, one of the correct guesses, and one of the wrong guesses). Therefore the expected random score is 0.6*0.33 = 0.198. (3) The probability for a random guess is correct in 2 associations is 0.1, and the Jaccard index is 1 (intersection=2, union=2). Therefore the expected random score is 0.1*1 = 0.1. * Together, when K=2, the expected score is 0+0.198+0.1 = 0.298. To calculate (1), the first guess needs to be wrong. There are 3 "wrong" guesses and 5 candidates, so the probability for it is 3/5. The next guess should also be wrong. Now there are only 2 "wrong" guesses, and 4 candidates, so the probability for it is 2/4. Multiplying 3/5 * 2/4 = 0.3. Same goes for (2) and (3). Now we can perform the same calculation with K=3 associations. Assuming N=5 candidates, and K=3 associations, there could be four events: (4) The probability for a random guess is correct in 0 associations is 0, and the Jaccard index is 0. Therefore the expected random score is 0. (5) The probability for a random guess is correct in 1 associations is 0.3, and the Jaccard index is 0.2 (intersection=1, union=4). Therefore the expected random score is 0.3*0.2 = 0.06. (6) The probability for a random guess is correct in 2 associations is 0.6, and the Jaccard index is 0.5 (intersection=2, union=4). Therefore the expected random score is 0.6*5 = 0.3. (7) The probability for a random guess is correct in 3 associations is 0.1, and the Jaccard index is 1 (intersection=3, union=3). Therefore the expected random score is 0.1*1 = 0.1. * Together, when K=3, the expected score is 0+0.06+0.3+0.1 = 0.46. Taking the average of 0.298 and 0.46 we reach 0.379. Same process can be recalculated with 6 candidates (and K=2,3,4), 10 candidates (and K=2,3,4,5) and 123 candidates (and K=2,3,4,5,6). </details> ## Dataset Creation Inspired by the popular card game Codenames, a “spymaster” gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. ### Annotations #### Annotation process We paid Amazon Mechanical Turk Workers to play our game. ## Considerations for Using the Data All associations were obtained with human annotators. ### Licensing Information CC-By 4.0 ### Citation Information @article{bitton2022winogavil, title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models}, author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy}, journal={arXiv preprint arXiv:2207.12576}, year={2022}

MoyAI/Funniest-answers

--- task_categories: - conversational - text-generation - text2text-generation - text-classification language: - ru pretty_name: Funny-responses size_categories: - n<1K --- # Датасет прикольных ответов Датасет смешных ответов собирается идеями от других людей (которые пишут мне если нет аккаунта в hugginface), и мной. Сборка началась 8 февраля 2023 года. ## Данные JSON файл содержит data список где есть message - сообщение, response - ответ, и type - тип. Это пример данных ```json { "data": [ {"message": "Дано: Архимед упал в говно.", "response": "Найти: Выталкивающую силу.", "type": "w"}, {"message": "Как дела?", "response": "Всё было нормально, пока Вася выёживаться не стал)", "type": "n"}, {"message": "Что ты можешь сказать о сне?", "response": "Я так долго тренировался спать что могу делать это с закрытыми глазами.", "type": "n"}, ... ] } ... ``` # Список типов сообщений-ответов: - "n" Нейтрально, без оскорбления - "a" Аггресивный/токсичный ответ - "w" Содержит не всегда приемлемые или оскорбительные слова. - "s" Содержит маты. (либо ответ либо запрос содержит хотя бы один мат) - "p" Писсимистичные ответы, с низкой самооценкой или суицидальными мыслями. (Оно не работает -> Безработный как я) - "u" Небезопасные ответы, предложение чего-то запрещённого шуткой (например алкоголь)

MartinKu/wikipedia_stage2

--- dataset_info: features: - name: text dtype: string - name: S_V sequence: string - name: S_V_position sequence: int64 - name: O_C sequence: string - name: O_C_position sequence: int64 splits: - name: train num_bytes: 45092871426 num_examples: 6458670 download_size: 25091808148 dataset_size: 45092871426 --- # Dataset Card for "wikipedia_stage2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

matallanas/yannic-kilcher-transcript

--- dataset_info: features: - name: id dtype: string - name: channel dtype: string - name: channel_id dtype: string - name: title dtype: string - name: categories sequence: string - name: tags sequence: string - name: description dtype: string - name: text dtype: string - name: segments list: - name: start dtype: float64 - name: end dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 24560830 num_examples: 370 download_size: 12784371 dataset_size: 24560830 --- # Dataset Card for "yannic-kilcher-transcript" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-staging-eval-project-11ed4317-15c4-4e98-9e37-8cdfe6d38dfb-4947

--- type: predictions tags: - autotrain - evaluation datasets: - emotion eval_info: task: multi_class_classification model: autoevaluate/multi-class-classification metrics: ['matthews_correlation'] dataset_name: emotion dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: autoevaluate/multi-class-classification * Dataset: emotion * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

izhx/yue-lihkg-topic

--- license: cc-by-4.0 --- From https://github.com/toastynews/lihkg-cat-v2 ### lihkg-cat-v2 Scraped forum threads from LIHKG for categorization task. Formatted to use with BERT. Compared to v1, the number of categories increased from 18 to 20, and the number of training examples increased from 300 to 500. The minimum length for each example has also increased to make the task more solvable.

Seanxh/twitter_dataset_1713196913

--- 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: 85739 num_examples: 199 download_size: 34951 dataset_size: 85739 configs: - config_name: default data_files: - split: train path: data/train-* ---

jonathang/dreambooth-hackathon-images-mario-bg-1

--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 559875.0 num_examples: 15 download_size: 523924 dataset_size: 559875.0 --- # Dataset Card for "dreambooth-hackathon-images-mario-bg-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Heejung89/custom_kor3

--- license: mit dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 8664 num_examples: 42 download_size: 3732 dataset_size: 8664 configs: - config_name: default data_files: - split: train path: data/train-* ---

RahulRaman/final-counting-dataset

--- dataset_info: features: - name: input_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 126948111.357 num_examples: 1359 download_size: 34431713 dataset_size: 126948111.357 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_lloorree__jfdslijsijdgis

--- pretty_name: Evaluation run of lloorree/jfdslijsijdgis dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lloorree/jfdslijsijdgis](https://huggingface.co/lloorree/jfdslijsijdgis) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lloorree__jfdslijsijdgis\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-17T00:34:49.304226](https://huggingface.co/datasets/open-llm-leaderboard/details_lloorree__jfdslijsijdgis/blob/main/results_2023-09-17T00-34-49.304226.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\": 0.6907933129316588,\n\ \ \"acc_stderr\": 0.03107455661224763,\n \"acc_norm\": 0.694824769775718,\n\ \ \"acc_norm_stderr\": 0.031044197474221744,\n \"mc1\": 0.41615667074663404,\n\ \ \"mc1_stderr\": 0.017255657502903043,\n \"mc2\": 0.5820460749080146,\n\ \ \"mc2_stderr\": 0.015030523772190541\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6518771331058021,\n \"acc_stderr\": 0.01392100859517935,\n\ \ \"acc_norm\": 0.6962457337883959,\n \"acc_norm_stderr\": 0.013438909184778764\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6760605457080263,\n\ \ \"acc_stderr\": 0.00467020812857923,\n \"acc_norm\": 0.8695478988249352,\n\ \ \"acc_norm_stderr\": 0.0033611183954523846\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.03110318238312338,\n\ \ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.03110318238312338\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.02783491252754407,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.02783491252754407\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8125,\n\ \ \"acc_stderr\": 0.032639560491693344,\n \"acc_norm\": 0.8125,\n\ \ \"acc_norm_stderr\": 0.032639560491693344\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736413,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736413\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\":\ \ 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.041633319989322626\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6638297872340425,\n \"acc_stderr\": 0.030881618520676942,\n\ \ \"acc_norm\": 0.6638297872340425,\n \"acc_norm_stderr\": 0.030881618520676942\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\ \ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\ \ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6206896551724138,\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\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|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_biology|5\": {\n \"acc\": 0.8064516129032258,\n\ \ \"acc_stderr\": 0.022475258525536057,\n \"acc_norm\": 0.8064516129032258,\n\ \ \"acc_norm_stderr\": 0.022475258525536057\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\"\ : 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781668,\n\ \ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781668\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\ acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9533678756476683,\n \"acc_stderr\": 0.015216761819262592,\n\ \ \"acc_norm\": 0.9533678756476683,\n \"acc_norm_stderr\": 0.015216761819262592\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7153846153846154,\n \"acc_stderr\": 0.022878322799706304,\n\ \ \"acc_norm\": 0.7153846153846154,\n \"acc_norm_stderr\": 0.022878322799706304\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \ \ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.026841514322958934,\n\ \ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.026841514322958934\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.423841059602649,\n \"acc_stderr\": 0.04034846678603397,\n \"acc_norm\"\ : 0.423841059602649,\n \"acc_norm_stderr\": 0.04034846678603397\n },\n\ \ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8935779816513761,\n\ \ \"acc_stderr\": 0.013221554674594372,\n \"acc_norm\": 0.8935779816513761,\n\ \ \"acc_norm_stderr\": 0.013221554674594372\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\ : {\n \"acc\": 0.6018518518518519,\n \"acc_stderr\": 0.033384734032074016,\n\ \ \"acc_norm\": 0.6018518518518519,\n \"acc_norm_stderr\": 0.033384734032074016\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9117647058823529,\n \"acc_stderr\": 0.01990739979131695,\n \"\ acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.01990739979131695\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\": 0.7982062780269058,\n\ \ \"acc_stderr\": 0.026936111912802263,\n \"acc_norm\": 0.7982062780269058,\n\ \ \"acc_norm_stderr\": 0.026936111912802263\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n\ \ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.859504132231405,\n \"acc_stderr\": 0.03172233426002158,\n \"acc_norm\"\ : 0.859504132231405,\n \"acc_norm_stderr\": 0.03172233426002158\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n\ \ \"acc_stderr\": 0.03520703990517964,\n \"acc_norm\": 0.8425925925925926,\n\ \ \"acc_norm_stderr\": 0.03520703990517964\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8159509202453987,\n \"acc_stderr\": 0.030446777687971726,\n\ \ \"acc_norm\": 0.8159509202453987,\n \"acc_norm_stderr\": 0.030446777687971726\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.02158649400128138\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8659003831417624,\n\ \ \"acc_stderr\": 0.012185528166499978,\n \"acc_norm\": 0.8659003831417624,\n\ \ \"acc_norm_stderr\": 0.012185528166499978\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7658959537572254,\n \"acc_stderr\": 0.022797110278071124,\n\ \ \"acc_norm\": 0.7658959537572254,\n \"acc_norm_stderr\": 0.022797110278071124\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.511731843575419,\n\ \ \"acc_stderr\": 0.016717897676932162,\n \"acc_norm\": 0.511731843575419,\n\ \ \"acc_norm_stderr\": 0.016717897676932162\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7845659163987139,\n\ \ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\ \ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.021185893615225188,\n\ \ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.021185893615225188\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5177304964539007,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.5177304964539007,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5397653194263363,\n\ \ \"acc_stderr\": 0.012729785386598545,\n \"acc_norm\": 0.5397653194263363,\n\ \ \"acc_norm_stderr\": 0.012729785386598545\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7169117647058824,\n \"acc_stderr\": 0.02736586113151381,\n\ \ \"acc_norm\": 0.7169117647058824,\n \"acc_norm_stderr\": 0.02736586113151381\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.75,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.75,\n \"acc_norm_stderr\": 0.01751781884501444\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\": 0.8122448979591836,\n\ \ \"acc_stderr\": 0.025000256039546195,\n \"acc_norm\": 0.8122448979591836,\n\ \ \"acc_norm_stderr\": 0.025000256039546195\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.900497512437811,\n \"acc_stderr\": 0.0211662163046594,\n\ \ \"acc_norm\": 0.900497512437811,\n \"acc_norm_stderr\": 0.0211662163046594\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \"acc_norm\": 0.89,\n\ \ \"acc_norm_stderr\": 0.03144660377352203\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.5180722891566265,\n \"acc_stderr\": 0.03889951252827216,\n\ \ \"acc_norm\": 0.5180722891566265,\n \"acc_norm_stderr\": 0.03889951252827216\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8713450292397661,\n\ \ \"acc_stderr\": 0.02567934272327692,\n \"acc_norm\": 0.8713450292397661,\n\ \ \"acc_norm_stderr\": 0.02567934272327692\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.41615667074663404,\n \"mc1_stderr\": 0.017255657502903043,\n\ \ \"mc2\": 0.5820460749080146,\n \"mc2_stderr\": 0.015030523772190541\n\ \ }\n}\n```" repo_url: https://huggingface.co/lloorree/jfdslijsijdgis leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|arc:challenge|25_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|arc:challenge|25_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hellaswag|10_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hellaswag|10_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-15T09-43-22.432852.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-17T00-34-49.304226.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-management|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-management|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-17T00-34-49.304226.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_15T09_43_22.432852 path: - '**/details_harness|truthfulqa:mc|0_2023-09-15T09-43-22.432852.parquet' - split: 2023_09_17T00_34_49.304226 path: - '**/details_harness|truthfulqa:mc|0_2023-09-17T00-34-49.304226.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-17T00-34-49.304226.parquet' - config_name: results data_files: - split: 2023_09_15T09_43_22.432852 path: - results_2023-09-15T09-43-22.432852.parquet - split: 2023_09_17T00_34_49.304226 path: - results_2023-09-17T00-34-49.304226.parquet - split: latest path: - results_2023-09-17T00-34-49.304226.parquet --- # Dataset Card for Evaluation run of lloorree/jfdslijsijdgis ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lloorree/jfdslijsijdgis - **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 [lloorree/jfdslijsijdgis](https://huggingface.co/lloorree/jfdslijsijdgis) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lloorree__jfdslijsijdgis", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T00:34:49.304226](https://huggingface.co/datasets/open-llm-leaderboard/details_lloorree__jfdslijsijdgis/blob/main/results_2023-09-17T00-34-49.304226.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": 0.6907933129316588, "acc_stderr": 0.03107455661224763, "acc_norm": 0.694824769775718, "acc_norm_stderr": 0.031044197474221744, "mc1": 0.41615667074663404, "mc1_stderr": 0.017255657502903043, "mc2": 0.5820460749080146, "mc2_stderr": 0.015030523772190541 }, "harness|arc:challenge|25": { "acc": 0.6518771331058021, "acc_stderr": 0.01392100859517935, "acc_norm": 0.6962457337883959, "acc_norm_stderr": 0.013438909184778764 }, "harness|hellaswag|10": { "acc": 0.6760605457080263, "acc_stderr": 0.00467020812857923, "acc_norm": 0.8695478988249352, "acc_norm_stderr": 0.0033611183954523846 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.04171654161354543, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.04171654161354543 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8223684210526315, "acc_stderr": 0.03110318238312338, "acc_norm": 0.8223684210526315, "acc_norm_stderr": 0.03110318238312338 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.02783491252754407, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.02783491252754407 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8125, "acc_stderr": 0.032639560491693344, "acc_norm": 0.8125, "acc_norm_stderr": 0.032639560491693344 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736413, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736413 }, "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": 0.78, "acc_stderr": 0.041633319989322626, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322626 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6638297872340425, "acc_stderr": 0.030881618520676942, "acc_norm": 0.6638297872340425, "acc_norm_stderr": 0.030881618520676942 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.43859649122807015, "acc_stderr": 0.04668000738510455, "acc_norm": 0.43859649122807015, "acc_norm_stderr": 0.04668000738510455 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6206896551724138, "acc_stderr": 0.040434618619167466, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.040434618619167466 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4444444444444444, "acc_stderr": 0.025591857761382182, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.025591857761382182 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8064516129032258, "acc_stderr": 0.022475258525536057, "acc_norm": 0.8064516129032258, "acc_norm_stderr": 0.022475258525536057 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175008, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8484848484848485, "acc_stderr": 0.027998073798781668, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.027998073798781668 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8636363636363636, "acc_stderr": 0.024450155973189835, "acc_norm": 0.8636363636363636, "acc_norm_stderr": 0.024450155973189835 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9533678756476683, "acc_stderr": 0.015216761819262592, "acc_norm": 0.9533678756476683, "acc_norm_stderr": 0.015216761819262592 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7153846153846154, "acc_stderr": 0.022878322799706304, "acc_norm": 0.7153846153846154, "acc_norm_stderr": 0.022878322799706304 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.28888888888888886, "acc_stderr": 0.027634907264178544, "acc_norm": 0.28888888888888886, "acc_norm_stderr": 0.027634907264178544 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7815126050420168, "acc_stderr": 0.026841514322958934, "acc_norm": 0.7815126050420168, "acc_norm_stderr": 0.026841514322958934 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.423841059602649, "acc_stderr": 0.04034846678603397, "acc_norm": 0.423841059602649, "acc_norm_stderr": 0.04034846678603397 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8935779816513761, "acc_stderr": 0.013221554674594372, "acc_norm": 0.8935779816513761, "acc_norm_stderr": 0.013221554674594372 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6018518518518519, "acc_stderr": 0.033384734032074016, "acc_norm": 0.6018518518518519, "acc_norm_stderr": 0.033384734032074016 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9117647058823529, "acc_stderr": 0.01990739979131695, "acc_norm": 0.9117647058823529, "acc_norm_stderr": 0.01990739979131695 }, "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": 0.7982062780269058, "acc_stderr": 0.026936111912802263, "acc_norm": 0.7982062780269058, "acc_norm_stderr": 0.026936111912802263 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8473282442748091, "acc_stderr": 0.031545216720054725, "acc_norm": 0.8473282442748091, "acc_norm_stderr": 0.031545216720054725 }, "harness|hendrycksTest-international_law|5": { "acc": 0.859504132231405, "acc_stderr": 0.03172233426002158, "acc_norm": 0.859504132231405, "acc_norm_stderr": 0.03172233426002158 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8425925925925926, "acc_stderr": 0.03520703990517964, "acc_norm": 0.8425925925925926, "acc_norm_stderr": 0.03520703990517964 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8159509202453987, "acc_stderr": 0.030446777687971726, "acc_norm": 0.8159509202453987, "acc_norm_stderr": 0.030446777687971726 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5357142857142857, "acc_stderr": 0.04733667890053756, "acc_norm": 0.5357142857142857, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.0376017800602662, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.0376017800602662 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.02158649400128138, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.02158649400128138 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8659003831417624, "acc_stderr": 0.012185528166499978, "acc_norm": 0.8659003831417624, "acc_norm_stderr": 0.012185528166499978 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7658959537572254, "acc_stderr": 0.022797110278071124, "acc_norm": 0.7658959537572254, "acc_norm_stderr": 0.022797110278071124 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.511731843575419, "acc_stderr": 0.016717897676932162, "acc_norm": 0.511731843575419, "acc_norm_stderr": 0.016717897676932162 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7845659163987139, "acc_stderr": 0.023350225475471442, "acc_norm": 0.7845659163987139, "acc_norm_stderr": 0.023350225475471442 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8240740740740741, "acc_stderr": 0.021185893615225188, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.021185893615225188 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5177304964539007, "acc_stderr": 0.02980873964223777, "acc_norm": 0.5177304964539007, "acc_norm_stderr": 0.02980873964223777 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5397653194263363, "acc_stderr": 0.012729785386598545, "acc_norm": 0.5397653194263363, "acc_norm_stderr": 0.012729785386598545 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7169117647058824, "acc_stderr": 0.02736586113151381, "acc_norm": 0.7169117647058824, "acc_norm_stderr": 0.02736586113151381 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.75, "acc_stderr": 0.01751781884501444, "acc_norm": 0.75, "acc_norm_stderr": 0.01751781884501444 }, "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": 0.8122448979591836, "acc_stderr": 0.025000256039546195, "acc_norm": 0.8122448979591836, "acc_norm_stderr": 0.025000256039546195 }, "harness|hendrycksTest-sociology|5": { "acc": 0.900497512437811, "acc_stderr": 0.0211662163046594, "acc_norm": 0.900497512437811, "acc_norm_stderr": 0.0211662163046594 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8713450292397661, "acc_stderr": 0.02567934272327692, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.02567934272327692 }, "harness|truthfulqa:mc|0": { "mc1": 0.41615667074663404, "mc1_stderr": 0.017255657502903043, "mc2": 0.5820460749080146, "mc2_stderr": 0.015030523772190541 } } ``` ### 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]

canristiian/drug_rule_sort2

--- license: apache-2.0 ---

ninja/cluster-colors

--- dataset_info: features: - name: color dtype: string - name: hex dtype: string splits: - name: train num_bytes: 392073 num_examples: 11936 download_size: 264134 dataset_size: 392073 --- # Dataset Card for "cluster-colors" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

FoodIntake/openfoodfacts_package_weights

--- license: odbl task_categories: - text-generation - token-classification - text-classification language: - en - de - it - es - fr tags: - food - packaged - branded pretty_name: 'Open Food Facts data subset: package weights with general information' size_categories: - 100K<n<1M ---

stanfordnlp/SHP-2

--- task_categories: - text-generation - question-answering tags: - human feedback - rlhf - preferences - reddit - preference model - RL - NLG - evaluation size_categories: - 1M<n<10M language: - en --- # 🚢 Stanford Human Preferences Dataset v2 (SHP-2) ## Summary SHP-2 is a dataset of **4.8M collective human preferences** over responses to questions/instructions in 129 different subject areas, from cooking to legal advice. It is an extended version of the original 385K [SHP dataset](https://huggingface.co/datasets/stanfordnlp/SHP). The preferences are meant to reflect the helpfulness of one response over another, and are intended to be used for training RLHF reward models and NLG evaluation models (e.g., [SteamSHP](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl)). Each example is a Reddit or StackExchange post with a question/instruction and a pair of top-level comments for that post, where one comment is more preferred by Reddit / StackExchange users (collectively). SHP exploits the fact that if comment A was written *after* comment B but has a higher score nonetheless, then A is ostensibly more preferred to B. If A had been written before B, then we could not conclude this, since its higher score could have been the result of more visibility. We chose data where the preference label is intended to reflect which response is more *helpful* rather than which is less *harmful*, the latter being the focus of much past work. How is SHP different from [Anthropic's HH-RLHF dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) and [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)? | Dataset | Size | Input | Label | Domains | Data Format | Length | | -------------------- | ---- | -------------------------- | ---------------------------- | ------------------------- | ------------------------------------- | --------------- | | SHP-2 | 4.8M | Naturally occurring human-written responses | Collective Human Preference | 129 (labelled) | Question/Instruction + Response (Single-turn) | up to 10.1K T5 tokens | | HH-RLHF | 91K | Dialogue with LLM | Individual Human Preference | not labelled | Live Chat (Multi-turn) | up to 1.5K T5 tokens | | OASST | 161K | Dialogue with LLM | K Individual Preferences, Aggregated | not labelled | Live Chat (Multi-Turn) | up to 1.5K T5 tokens | How is SHP different from other datasets that have scraped Reddit, like [ELI5](https://huggingface.co/datasets/eli5#source-data)? SHP uses the timestamp information to infer preferences, while ELI5 only provides comments and scores -- the latter are not enough to infer preferences since comments made earlier tend to get higher scores from more visibility. It also contains data from more domains: | Dataset | Size | Comments + Scores | Preferences | Number of Domains | | -------------------- | ---- | ------------------ | -------------| ------------------ | | SHP-2 | 4.8M | Yes | Yes | 129 (70 from Reddit, 59 from StackExchange) | | SHP | 385K | Yes | Yes | 18 (from Reddit) | | ELI5 | 270K | Yes | No | 3 | ## Data Structure There are 2 directories, one for Reddit and one for StackExchange. There are 70 subdirectories under `reddit/`, one for each subreddit, and 59 subdirectories under `stackexchange/`, one for each stackexchange site. Each subdirectory contains a JSONL file for the training, validation, and test data. Here's how to get the data using Huggingface's `datasets` library: ```python from datasets import load_dataset # Load all the data dataset = load_dataset("stanfordnlp/shp-2") # Load one of the subreddits dataset = load_dataset("stanfordnlp/shp-2", data_dir="reddit/askculinary") # Load one of the StackExchange sites dataset = load_dataset("stanfordnlp/shp-2", data_dir="stackexchange/stack_academia") ``` Here's an example from `reddit/askculinary/train.json`: ``` { `post_id`:"qt3nxl", `domain`:"askculinary_train", `upvote_ratio`:0.98, `history`:"What's the best way to disassemble raspberries? Like this, but down to the individual seeds: https:\/\/i.imgur.com\/Z0c6ZKE.jpg I've been pulling them apart with tweezers and it's really time consuming. I have about 10 pounds to get through this weekend.", `c_root_id_A`:"hkh25sc", `c_root_id_B`:"hkh25lp", `created_at_utc_A`:1636822112, `created_at_utc_B`:1636822110, `score_A`:340, `score_B`:166, `human_ref_A`:"Pectinex, perhaps? It's an enzyme that breaks down cellulose. With citrus, you let it sit in a dilute solution of pectinex overnight to break down the connective tissues. You end up with perfect citrus supremes. If you let the raspberries sit for a shorter time, I wonder if it would separate the seeds the same way...? Here's an example: https:\/\/www.chefsteps.com\/activities\/perfect-citrus-supreme", `human_ref_B`:"Raspberry juice will make a bright stain at first, but in a matter of weeks it will start to fade away to almost nothing. It is what is known in the natural dye world as a fugitive dye, it will fade even without washing or exposure to light. I hope she gets lots of nice photos of these stains on her dress, because soon that will be all she has left of them!", `labels`:1, `metadata_A`: "", `metadata_B`: "", `seconds_difference`:2.0, `score_ratio`:2.0481927711 } ``` Here's an example from `stackexchange/stack_academia/validation.json`: ``` { `post_id`:"87393", `domain`:"academia_validation", `history`:"What to answer an author asking me if I reviewed his/her paper? <sep> Suppose I review someone's paper anonymously, the paper gets accepted, and a year or two later we meet e.g. in a social event and he/she asks me "did you review my paper?". What should I answer? There are several sub-questions here: Suppose the review was a good one, and the paper eventualy got accepted, so I do not mind telling that I was the reviewer. Is there any rule/norm prohibiting me from telling the truth? Suppose the review was not so good, so I do not want to reveal. What can I answer? If I just say "I am not allowed to tell you", this immediately reveals me... On the other hand, I do not want to lie. What options do I have?", `c_root_id_A`:"87434", `c_root_id_B`:"87453", `created_at_utc_A`:1490989560, `created_at_utc_B`:1491012608, `score_A`:2, `score_B`:5, `human_ref_A`:"I am aware of at least one paper where a referee went out of cover (after the review process of course) and was explicitly mentioned in a later paper: <blockquote> X and Y thank Z, who as the anonymous referee was kind enough to point out the error (and later became non-anonymous). </blockquote> so it is sure fine to answer truthfully that yes you did review, but only if you wish of course (and most likely if you have been helpful and the authors of the paper responsive).", `human_ref_B`:"Perhaps you should follow the example of Howard Percy Robertson (known as the 'R' in the famous FLRW, or Friedmann-Lematre-Robertson-Walker metric used in physical cosmology.) He was the referee of the famous Einstein-Rosen paper, which was rejected by Physical Review, prompting Einstein never to publish in Physical Review again. Einstein ignored the referee report, but months later, it seems, Robertson had a chance to talk to Einstein and may have helped convince him of the error of his ways. However, as far as we know, he never revealed to Einstein that he was the anonymous referee for Physical Review. It was not until 2005 I believe, long after the death of all participants, that Physical Review chose to disclose the referee's identity (http://physicstoday.scitation.org/doi/full/10.1063/1.2117822).", `labels`:"0", `metadata_A`:"Post URL: https://academia.stackexchange.com/questions/87393, Response URL: https://academia.stackexchange.com/questions/87434, Post author username: Erel Segal-Halevi, Post author profile: https://academia.stackexchange.com/users/787, Response author username: mts, Response author profile: https://academia.stackexchange.com/users/49583", `metadata_B`:"Post URL: https://academia.stackexchange.com/questions/87393, Response URL: https://academia.stackexchange.com/questions/87453, Post author username: Erel Segal-Halevi, Post author profile: https://academia.stackexchange.com/users/787, Response author username: Viktor Toth, Response author profile: https://academia.stackexchange.com/users/7938", `seconds_difference`:23048.0, `score_ratio`:2.5, } ``` where the fields are: - ```post_id```: the ID of the Reddit post (string) - ```domain```: the subreddit and split the example is drawn from, separated by an underscore (string) - ```upvote_ratio```: the percent of votes received by the post that were positive (aka upvotes), -1.0 for stackexchange as there is no such data (float) - ```history```: the post title concatented to the post body (string) - ```c_root_id_A```: the ID of comment A (string) - ```c_root_id_B```: the ID of comment B (string) - ```created_at_utc_A```: utc timestamp of when comment A was created (integer) - ```created_at_utc_B```: utc timestamp of when comment B was created (integer) - ```score_A```: (# positive votes - # negative votes + 1) received by comment A (integer) - ```score_B```: (# positive votes - # negative votes + 1) received by comment B (integer) - ```human_ref_A```: text of comment A (string) - ```human_ref_B```: text of comment B (string) - ```labels```: the preference label -- it is 1 if A is preferred to B; 0 if B is preferred to A. This was randomized such that the label distribution is roughly 50/50. (integer) - ```metadata_A```: metadata for stackexchange post and comment A (string) - ```metadata_B```: metadata for stackexchange post and comment B (string) - ```seconds_difference```: how many seconds after the less preferred comment the more preferred one was created (will always be >= 0) (integer) - ```score_ratio```: the ratio of the more preferred comment's score to the less preferred comment's score (will be >= 1) (float) ## Dataset Design ### Domain Selection The data is sourced from Reddit and StackExchange, which are both public forums organized into different domains. SHP-2 contains a train, validation, and test split for comments scraped from each domain. We chose domains based on: 1. whether they were well-known (>= 100K subscribers for Reddit and >= 50K for StackExchange) 2. whether posts were expected to pose a question or instruction 3. whether responses were valued based on how *helpful* they were 4. whether comments had to be rooted in some objectivity, instead of being entirely about personal experiences (e.g., `askscience` vs. `AskAmericans`) The train/validation/test splits were created by splitting the post IDs of a domain in 90%/5%/5% proportions respectively, so that no post would appear in multiple splits. Since different posts have different numbers of comments, the number of preferences in each split is not exactly 90%/5%/5%. See below for a list of all domains: Reddit: \ techsupport, asklinguistics, askscience, catadvice, campingandhiking, askphysics, espresso, botany, asksocialscience, askbaking, ultralight, legaladvice, hiking, webdev, askengineers, screenwriting, askhistorians, vegetarian, writing, diy, musictheory, camping, moviesuggestions, askeconomics, stocks, frugal, outoftheloop, booksuggestions, gamedev, linuxquestions, asknetsec, aviation, askacademia, asksciencefiction, askhr, explainlikeimfive, etymology, entrepreneur, cooking, puppy101, keto, crochet, smallbusiness, architecture, artfundamentals, sewing, zerowaste, changemyview, mechanicadvice, iwanttolearn, eatcheapandhealthy, askanthropology, askculinary, askphilosophy, tea, running, excel, homebrewing, solotravel, fishing, cookingforbeginners, homeautomation, ifyoulikeblank, travel, suggestmeabook, televisionsuggestions, sysadmin, askcarguys, askdocs, askvet StackExchange: \ stack_unix, stack_android, stack_academia, stack_superuser, stack_tex, stack_photo, stack_datascience, stack_mechanics, stack_english, stack_askubuntu, stack_sharepoint, stack_workplace, stack_blender, stack_ethereum, stack_stats, stack_bitcoin, stack_gamedev, stack_raspberrypi, stack_arduino, stack_magento, stack_physics, stack_mathoverflow, stack_dsp, stack_movies, stack_crypto, stack_apple, stack_mathematica, stack_philosophy, stack_wordpress, stack_ux, stack_webmasters, stack_cs, stack_travel, stack_bicycles, stack_softwarerecs, stack_money, stack_ell, stack_scifi, stack_aviation, stack_math, stack_biology, stack_drupal, stack_diy, stack_security, stack_salesforce, stack_graphicdesign, stack_stackoverflow, stack_webapps, stack_cooking, stack_networkengineering, stack_dba, stack_puzzling, stack_serverfault, stack_codereview, stack_music, stack_codegolf, stack_electronics, stack_chemistry, stack_gis ### Data Selection For Reddit, the score of a post/comment is 1 plus the number of upvotes (approvals) it gets from users, minus the number of downvotes (disapprovals) it gets. For Stackexchange, the score of a post/comment is 0 plus the number of upvotes (approvals) it gets from users, minus the number of downvotes (disapprovals) it gets. The value of a score is relative; in domains(posts) with more traffic, there will be more higher-scoring posts(comments). Within a post, comments posted earlier will tend to have a higher score simply due to having more exposure, which is why using timestamp information is essential when inferring preferences. Given a post P and two comments (A,B) we only included the preference A > B in the dataset if 1. A was written *no later than* B and A has a higher score than B. 2. The post is a self-post (i.e., a body of text and not a link to another page) made before 2023, was not edited, and is not NSFW (over 18). For Stackexchange, edited posts were permitted as long as they were edited prior to the writing of the comments. 3. Neither comment was made by a deleted user, a moderator, or the post creator. The post was not made by a deleted user or moderator. 4. For Reddit, the post has a score >= 10 and each comment has a score >= 2 (upvoted at least once). For Stackexchange, the post has a score >= 5 and each comment has a non-zero score. The conditions are laxer for StackExchange because it is more strictly moderataed than Reddit, allowing us to hit the same data quality with lower thresholds. In particular, we allow negative-score comments from StackExchange because the negative scores are likely due to being inaccurat/misinformed rather than being toxic, and this provides a useful signal. A post with `n` comments could have up to (`n` choose `2`) preferences in the data. Since the number of comments per post is Pareto-distributed, to prevent a relatively small number of posts from dominating the Reddit data, we limited the scraping to 50 comments per post. This means that each post could have up to (`50` choose `2`) comments in the dataset, though this is a much smaller number in practice, since all the criteria above need to be met. No such criteria are imposed for StackExchange, since there are fewer comments per post. ### Reddit Preprocessing We tried to keep preprocessing to a minimum. Subreddit-specific abbreviations were expanded (e.g., "CMV" to "Change my view that"). In hyperlinks, only the referring text was kept and the URL was removed (if the URL was written out, then it was kept). ### Finetuning If you want to finetune a model to predict human preferences (e.g., for NLG evaluation or an RLHF reward model), here are some helpful tips: 1. **Preprocess the data.** The total input length should fit under the model's token limit (usually 512 tokens). Although models like FLAN-T5 use positional embeddings, we found that the loss would not converge if we finetuned it on inputs over 512 tokens. To avoid this, truncate the post text (in the `history` field) as much as possible, such that the whole input is under 512 tokens (do not truncate the comment(s) however). If this is still over 512 tokens, simply skip the example. 2. **Use a sufficiently large model.** Finetuning a single FLAN-T5-xl model across [the original 385K SHP training data](https://huggingface.co/datasets/stanfordnlp/SHP) should give you a test accuracy between 72-73% (across all domains on examples where the entire input fits within the token limit), ranging from 65-80% on individual subreddits. 3. **Do in-domain prediction.** Out-of-domain performance will be poor if the domains are unrelated (e.g., if you fine-tune on `askculinary` preferences and test on `askcarguys` preferences). 4. **Train for fewer epochs.** The InstructGPT paper paper suggests training a reward model for only 1 epoch. Since the same comment appears in multiple preferences, it is easy to overfit to the data. 5. **Training on less data may help.** Preferences with a large `score_ratio` (e.g., comment A having 2x the score of comment B) will provide a stronger signal for finetuning the model, so you may only want to consider preferences above a certain `score_ratio`. The number of preferences per post is Pareto-distributed, so to prevent the model from over-fitting to certain posts, you may want to limit the number of preferences from a particular post. ## Biases and Limitations ### Biases Although we filtered out posts with NSFW (over 18) content, chose domains that were well-moderated and had policies against harassment and bigotry, some of the data may contain discriminatory or harmful language. The data does not reflect the views of the dataset creators. Reddit and StackExchange users are also not representative of the broader population. Although subreddit-specific demographic information is not available, Reddit users overall are disproportionately male and from developed, Western, and English-speaking countries ([Pew Research](https://www.pewresearch.org/internet/2013/07/03/6-of-online-adults-are-reddit-users/)). This is likely also true of StackExchange users. Please keep this in mind before using any models trained on this data. ### Limitations The preference label in SHP is intended to reflect how *helpful* one response is relative to another, given an instruction/question. SHP is not intended for use in harm-minimization, as it was not designed to include the toxic content that would be necessary to learn a good toxicity detector. If you are looking for data where the preference label denotes less harm, we would recommend the harmfulness split of [Anthropic's HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf). Another limitation is that the more preferred response in SHP is not necessarily the more factual one. Though some comments do provide citations to justify their response, most do not. There are exceptions to this, such as the `askhistorians` subreddit, which is heavily moderated and answers are expected to provide citations. Note that the collective preference label in SHP is not necessarily what we would get if we asked users to independently vote on each comment before taking an unweighted sum. This is because comment scores on Reddit are public and are known to influence user preferences; a high score increases the likelihood of getting more positive votes [(Muchnik et al., 2013)](https://pubmed.ncbi.nlm.nih.gov/23929980/). Whether this "herding effect" temporarily or permanently shifts a user's preference is unclear. Therefore, while SHP does reflect collective human preferences, models trained on SHP may not generalize to settings where individual preferences are aggregated differently (e.g., users vote independently without ever seeing the current comment score, users vote after conferring, etc.). Thanks to Greg Stoddard for pointing this out. ## License Last updated: 07/016/2023 ### Reddit The data was made by scraping publicly available data in accordance with the a historical version of [Reddit API Terms of Use](https://docs.google.com/a/reddit.com/forms/d/e/1FAIpQLSezNdDNK1-P8mspSbmtC2r86Ee9ZRbC66u929cG2GX0T9UMyw/viewform), without any direct communication or written agreements with Reddit. According to the Terms of Use, "User Content" is owned by the users themselves -- not by Reddit -- and Reddit grants a "non-exclusive, non-transferable, non-sublicensable, and revocable license to copy and display the User Content". At time of writing, Reddit grants "no other rights or licenses are granted or implied, including any right to use User Content for other purposes, such as for training a machine learning or artificial intelligence model, without the express permission of rightsholders in the applicable User Content." However, the legality of training on publicly available data will depend on your jurisdiction (legal in Japan, for example). Datasets made by scraping Reddit are widely used in the research community: for example, Facebook AI Research used data scraped from Reddit to make the [ELI5](https://huggingface.co/datasets/eli5#source-data) dataset in 2019, which was made available without a license. Anthropic AI has also [attested to scraping Reddit](https://arxiv.org/pdf/2112.00861.pdf) for preferences using a different methodology, though this data was not made public. We take no responsibility for and we do not expressly or implicitly endorse any downstream use of this dataset. We reserve the right to modify the SHP dataset and this license at any point in the future. ### StackExchange StackExchange data is made available under a [CC by-SA license](https://creativecommons.org/licenses/by-sa/4.0/). ## Contact Please contact kawin@stanford.edu if you have any questions about the data. This dataset was created by Kawin Ethayarajh, Heidi (Chenyu) Zhang, and Shabnam Behzad with advice from Dan Jurafsky and Yizhong Wang. Kawin and Heidi prepared the Reddit datasets and trained the SteamSHP models. Kawin and Shabnam prepared the StackExchange data. Dan and Yizhong provide advice on dataset construction. ## Citation We will have a paper out soon, but until then, please cite: ``` @InProceedings{pmlr-v162-ethayarajh22a, title = {Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information}, author = {Ethayarajh, Kawin and Choi, Yejin and Swayamdipta, Swabha}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {5988--6008}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, } ```

datajuicer/the-pile-europarl-refined-by-data-juicer

--- license: apache-2.0 task_categories: - text-generation language: - en tags: - data-juicer - pretraining size_categories: - 10K<n<100K --- # The Pile -- EuroParl (refined by Data-Juicer) A refined version of EuroParl dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality. This dataset is usually used to pretrain a Large Language Model. **Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-europarl-refine-result.jsonl) (About 2.2GB). ## Dataset Information - Number of samples: 61,601 (Keep ~88.23% from the original dataset) ## Refining Recipe ```yaml # global parameters # global parameters project_name: 'Data-Juicer-recipes-EuroParl' dataset_path: '/path/to/your/dataset' # path to your dataset directory or file export_path: '/path/to/your/dataset.jsonl' np: 50 # number of subprocess to process your dataset open_tracer: true # process schedule # a list of several process operators with their arguments process: - clean_email_mapper: - clean_links_mapper: - fix_unicode_mapper: - punctuation_normalization_mapper: - whitespace_normalization_mapper: - alphanumeric_filter: tokenization: false min_ratio: 0.75 # <3sigma (0.779) max_ratio: 0.90 # >3sigma(0.878) - average_line_length_filter: # for code max_len: 588 # 3sigma - character_repetition_filter: rep_len: 10 max_ratio: 0.16 # >3sigma (0.114) - flagged_words_filter: lang: en tokenization: true max_ratio: 0.0007 # 3sigma - language_id_score_filter: min_score: 0.7 - maximum_line_length_filter: # for code max_len: 4000 # >3sigma (3104) - perplexity_filter: lang: en max_ppl: 7596 #(3sigma) - special_characters_filter: max_ratio: 0.3 # > 3sigma (0.243) - text_length_filter: max_len: 2e5 - words_num_filter: tokenization: true min_num: 20 max_num: 1e5 # 3sigma - word_repetition_filter: lang: en tokenization: true rep_len: 10 max_ratio: 0.2 # > 3sigma (0.185) - document_simhash_deduplicator: tokenization: space window_size: 6 lowercase: true ignore_pattern: '\p{P}' num_blocks: 6 hamming_distance: 4 ```

hanesh007/GemmaModelOutputs

--- license: apache-2.0 ---

open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics

--- pretty_name: Evaluation run of lgaalves/gpt-2-xl_camel-ai-physics dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [lgaalves/gpt-2-xl_camel-ai-physics](https://huggingface.co/lgaalves/gpt-2-xl_camel-ai-physics)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-25T20:38:31.656182](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics/blob/main/results_2023-10-25T20-38-31.656182.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002202181208053691,\n\ \ \"em_stderr\": 0.0004800510816619256,\n \"f1\": 0.05571623322147659,\n\ \ \"f1_stderr\": 0.001366603872793856,\n \"acc\": 0.28844560078459863,\n\ \ \"acc_stderr\": 0.007481836249406744\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.0004800510816619256,\n\ \ \"f1\": 0.05571623322147659,\n \"f1_stderr\": 0.001366603872793856\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \ \ \"acc_stderr\": 0.001071779348549263\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5753749013417522,\n \"acc_stderr\": 0.013891893150264225\n\ \ }\n}\n```" repo_url: https://huggingface.co/lgaalves/gpt-2-xl_camel-ai-physics leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|arc:challenge|25_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-21T19-46-11.375703.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_25T20_38_31.656182 path: - '**/details_harness|drop|3_2023-10-25T20-38-31.656182.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-25T20-38-31.656182.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_25T20_38_31.656182 path: - '**/details_harness|gsm8k|5_2023-10-25T20-38-31.656182.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-25T20-38-31.656182.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hellaswag|10_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-21T19-46-11.375703.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-management|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T19-46-11.375703.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_21T19_46_11.375703 path: - '**/details_harness|truthfulqa:mc|0_2023-09-21T19-46-11.375703.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-21T19-46-11.375703.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_25T20_38_31.656182 path: - '**/details_harness|winogrande|5_2023-10-25T20-38-31.656182.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-25T20-38-31.656182.parquet' - config_name: results data_files: - split: 2023_09_21T19_46_11.375703 path: - results_2023-09-21T19-46-11.375703.parquet - split: 2023_10_25T20_38_31.656182 path: - results_2023-10-25T20-38-31.656182.parquet - split: latest path: - results_2023-10-25T20-38-31.656182.parquet --- # Dataset Card for Evaluation run of lgaalves/gpt-2-xl_camel-ai-physics ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lgaalves/gpt-2-xl_camel-ai-physics - **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 [lgaalves/gpt-2-xl_camel-ai-physics](https://huggingface.co/lgaalves/gpt-2-xl_camel-ai-physics) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-25T20:38:31.656182](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__gpt-2-xl_camel-ai-physics/blob/main/results_2023-10-25T20-38-31.656182.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.002202181208053691, "em_stderr": 0.0004800510816619256, "f1": 0.05571623322147659, "f1_stderr": 0.001366603872793856, "acc": 0.28844560078459863, "acc_stderr": 0.007481836249406744 }, "harness|drop|3": { "em": 0.002202181208053691, "em_stderr": 0.0004800510816619256, "f1": 0.05571623322147659, "f1_stderr": 0.001366603872793856 }, "harness|gsm8k|5": { "acc": 0.001516300227445034, "acc_stderr": 0.001071779348549263 }, "harness|winogrande|5": { "acc": 0.5753749013417522, "acc_stderr": 0.013891893150264225 } } ``` ### 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]

joey234/mmlu-high_school_biology-original-neg

--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: test num_bytes: 24421.548387096773 num_examples: 69 download_size: 18136 dataset_size: 24421.548387096773 --- # Dataset Card for "mmlu-high_school_biology-original-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-129000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 1013978 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

Jeffzera/Jesus

--- license: openrail ---

BangumiBase/nonnonbiyori

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Non Non Biyori This is the image base of bangumi Non Non Biyori, we detected 30 characters, 4423 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 692 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 576 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 56 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 18 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 13 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 161 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 37 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 37 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 591 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 18 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 15 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 27 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 194 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 34 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 36 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 174 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 14 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 15 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 52 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 92 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 20 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 1032 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 27 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 7 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | N/A | | 24 | 177 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 83 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 49 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 51 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 13 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | noise | 112 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

CyberHarem/light_cruiser_oni_kantaicollection

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of light_cruiser_oni/軽巡棲鬼 (Kantai Collection) This is the dataset of light_cruiser_oni/軽巡棲鬼 (Kantai Collection), containing 79 images and their tags. The core tags of this character are `black_hair, long_hair, blue_eyes, hair_bun, double_bun, breasts, glowing_eyes, colored_skin, white_skin, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 79 | 70.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 79 | 52.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 177 | 102.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 79 | 68.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 177 | 124.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/light_cruiser_oni_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/light_cruiser_oni_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 10 | ![](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) | 1boy, 1girl, blush, hetero, penis, solo_focus, abyssal_ship, sweat, glowing, paizuri, cum_on_breasts, bar_censor, open_mouth, collarbone, gauntlets, gloves, grin, male_pubic_hair, mosaic_censoring, simple_background, torn_clothes | | 1 | 52 | ![](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) | abyssal_ship, 1girl, solo, glowing, gauntlets, looking_at_viewer, skirt, cleavage, serafuku, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | blush | hetero | penis | solo_focus | abyssal_ship | sweat | glowing | paizuri | cum_on_breasts | bar_censor | open_mouth | collarbone | gauntlets | gloves | grin | male_pubic_hair | mosaic_censoring | simple_background | torn_clothes | solo | looking_at_viewer | skirt | cleavage | serafuku | smile | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:--------|:---------|:--------|:-------------|:---------------|:--------|:----------|:----------|:-----------------|:-------------|:-------------|:-------------|:------------|:---------|:-------|:------------------|:-------------------|:--------------------|:---------------|:-------|:--------------------|:--------|:-----------|:-----------|:--------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | 1 | 52 | ![](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 |

hk742/vaya-gpt-flagged-answers

--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name  ## 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]

Falcon96/clonar

--- license: openrail ---

lamnt2008/lam_gender

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': female '1': male splits: - name: train num_bytes: 886700538.492 num_examples: 188402 - name: validation num_bytes: 34511251.337 num_examples: 10617 download_size: 1046144749 dataset_size: 921211789.829 --- # Dataset Card for "lam_gender" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

aatherton2024/images_finalpject_2

--- dataset_info: features: - name: image dtype: image - name: classification dtype: string splits: - name: train num_bytes: 338530234.0 num_examples: 661 download_size: 77656691 dataset_size: 338530234.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

paulpanwang/POPE_Dataset

--- license: mit --- This is the dataset corresponding to the paper's experiments, used to reproduce the accuracy mentioned in the paper. [POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference](https://arxiv.org/abs/2305.15727) Please download and unzip the dataset into './data'.

jerome-white/alpaca-bt-stan

--- license: cc-by-nc-4.0 dataset_info: features: - name: parameter dtype: string - name: sample dtype: int64 - name: value dtype: float64 - name: chain dtype: int64 - name: element dtype: string splits: - name: train num_bytes: 249792000 num_examples: 4640000 download_size: 72590172 dataset_size: 249792000 configs: - config_name: default data_files: - split: train path: data/train-* ---

DynamicSuperb/SpeechDetection_LibriSpeech-TestClean

--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: instruction dtype: string - name: label dtype: string splits: - name: test num_bytes: 27340178.435114503 num_examples: 200 download_size: 28333588 dataset_size: 27340178.435114503 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "speechDetection_LibrispeechTestClean" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Roh2014/sentiment140_10k_tweets

--- license: unknown ---

assafm/cs-combined-002

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 319673 num_examples: 1559 download_size: 122375 dataset_size: 319673 --- # Dataset Card for "cs-combined-002" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alexcom/analisis-sentimeinto-textos-turisitcos-mx-pais

--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 72164531 num_examples: 176192 - name: test num_bytes: 30692934 num_examples: 75510 download_size: 62463153 dataset_size: 102857465 --- # Dataset Card for "analisis-sentimeinto-textos-turisitcos-mx-pais" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hippocrates/medical_meadow_mmmlu_train

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 3507993 num_examples: 3787 download_size: 1633148 dataset_size: 3507993 --- # Dataset Card for "medical_meadow_mmmlu_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Mostafa3zazi/Arabic_SQuAD

--- dataset_info: features: - name: index dtype: string - name: question dtype: string - name: context dtype: string - name: text dtype: string - name: answer_start dtype: int64 - name: c_id dtype: int64 splits: - name: train num_bytes: 61868003 num_examples: 48344 download_size: 10512179 dataset_size: 61868003 --- # Dataset Card for "Arabic_SQuAD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) --- # Citation ``` @inproceedings{mozannar-etal-2019-neural, title = "Neural {A}rabic Question Answering", author = "Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem", booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4612", doi = "10.18653/v1/W19-4612", pages = "108--118", abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.", } ``` ---

dderr/webtest3

--- configs: - config_name: a data_files: - split: train path: a/* - config_name: b data_files: - split: train path: b/* --- ### mytest

FarhatMay/coco_fine_tuning_diffusers

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 33175170.0 num_examples: 200 download_size: 33082020 dataset_size: 33175170.0 --- # Dataset Card for "coco_fine_tuning_diffusers" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

anamhira/foundation_action

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: prompt dtype: string - name: output dtype: string splits: - name: train num_bytes: 663896 num_examples: 289 - name: valid num_bytes: 8842 num_examples: 3 download_size: 134650 dataset_size: 672738 --- # Dataset Card for "foundation_action" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

C-MTEB/PAWSX

--- 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: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: int32 splits: - name: train num_bytes: 10420251 num_examples: 49401 - name: validation num_bytes: 457128 num_examples: 2000 - name: test num_bytes: 458674 num_examples: 2000 download_size: 8881168 dataset_size: 11336053 --- # Dataset Card for "PAWSX" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Akshayxx/CoraDatasetV4

--- dataset_info: features: - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 1328483 num_examples: 1768 - name: test num_bytes: 173380 num_examples: 222 - name: validation num_bytes: 164474 num_examples: 221 download_size: 887011 dataset_size: 1666337 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

Lollitor/PROTEINMARKED

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: ID dtype: string - name: INPUT dtype: string - name: LABEL dtype: float64 splits: - name: train num_bytes: 5509793.334042051 num_examples: 7619 - name: validation num_bytes: 612520.6659579495 num_examples: 847 download_size: 3212897 dataset_size: 6122314.0 --- # Dataset Card for "PROTEINMARKED" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Corran/Arxiv_V12July23_Post2013CS_AllMiniV2L6

--- dataset_info: features: - name: id dtype: string - name: submitter dtype: string - name: authors dtype: string - name: title dtype: string - name: comments dtype: string - name: journal-ref dtype: string - name: doi dtype: string - name: report-no dtype: string - name: categories dtype: string - name: license dtype: string - name: abstract dtype: string - name: versions list: - name: created dtype: string - name: version dtype: string - name: update_date dtype: string - name: authors_parsed sequence: sequence: string - name: embeddings sequence: float32 - name: paper_title dtype: string - name: paper_url_abs dtype: string - name: paper_url_pdf dtype: string - name: repo_url dtype: string - name: is_official dtype: bool - name: mentioned_in_paper dtype: bool - name: pwc_url dtype: string - name: abs_enc sequence: float32 splits: - name: train num_bytes: 2949900219 num_examples: 612833 download_size: 3236905950 dataset_size: 2949900219 --- # Dataset Card for "Arxiv_V12July23_Post2013CS_AllMiniV2L6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

phusroyal/ViHOS

--- annotations_creators: - crowdsourced license: mit multilinguality: - monolingual source_datasets: - original task_ids: - hate-speech-detection task_categories: - text-classification - token-classification language: - vi pretty_name: ViHOS - Vietnamese Hate and Offensive Spans Dataset size_categories: - 10K<n<100K configs: - config_name: default data_files: - split: train_sequence_labeling path: - "train_sequence_labeling/syllable/train_BIO_syllable.csv" - "train_sequence_labeling/syllable/dev_BIO_syllable.csv" - "train_sequence_labeling/syllable/test_BIO_syllable.csv" - "train_sequence_labeling/word/train_BIO_syllable.csv" - "train_sequence_labeling/word/dev_BIO_syllable.csv" - "train_sequence_labeling/word/test_BIO_syllable.csv" - split: train_span_extraction path: - 'train_span_extraction/train.csv' - 'train_span_extraction/dev.csv' - split: test path: "test/test.csv" --- **Disclaimer**: This project contains real comments that could be considered profane, offensive, or abusive. # Dataset Card for "ViHOS - Vietnamese Hate and Offensive Spans Dataset" ## Dataset Description - **Repository:** [ViHOS](https://github.com/phusroyal/ViHOS) - **Paper:** [EACL-ViHOS](https://aclanthology.org/2023.eacl-main.47/) - **Total amount of disk used:** 2.6 MB ## Dataset Motivation The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (**Vi**etnamese **H**ate and **O**ffensive **S**pans) dataset, the first human-annotated corpus containing 26k spans on 11k online comments. Our goal is to create a dataset that contains comprehensive hate and offensive thoughts, meanings, or opinions within the comments rather than just a lexicon of hate and offensive terms. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Futhermore, our solutions to deal with *nine different online foul linguistic phenomena* are also provided in the [*paper*](https://aclanthology.org/2023.eacl-main.47/) (e.g. Teencodes; Metaphors, metonymies; Hyponyms; Puns...). We hope that this dataset will be useful for researchers and practitioners in the field of hate speech detection in general and hate spans detection in particular. ## Dataset Summary ViHOS contains 26,476 human-annotated spans on 11,056 comments (5,360 comments have hate and offensive spans, and 5,696 comments do not) It is splitted into train, dev, and test set with following information: 1. Train set: 8,844 comments 2. Dev set: 1,106 comments 3. Test set: 1,106 comments ## Data Instance An span extraction-based (see Data Structure for more details) example of 'test' looks as follows: ``` { "content": "Thối CC chỉ không ngửi đuợc thôi", 'index_spans': "[0, 1, 2, 3, 5, 6]" } ``` An sequence labeling-based (see Data Structure for more details) example of 'test' looks as follows: ``` { "content": "Thối CC chỉ không ngửi đuợc thôi", 'index_spans': ["B-T", "I-T", "O", "O", "O", "O", "O"] } ``` ## Data Structure Here is our data folder structure! ``` . └── data/ ├── train_sequence_labeling/ │ ├── syllable/ │ │ ├── dev_BIO_syllable.csv │ │ ├── test_BIO_syllable.csv │ │ └── train_BIO_syllable.csv │ └── word/ │ ├── dev_BIO_Word.csv │ ├── test_BIO_Word.csv │ └── train_BIO_Word.csv ├── train_span_extraction/ │ ├── dev.csv │ └── train.csv └── test/ └── test.csv ``` ### Sequence labeling-based version #### Syllable Description: - This folder contains the data for the sequence labeling-based version of the task. The data is divided into two files: train, and dev. Each file contains the following columns: - **index**: The id of the word. - **word**: Words in the sentence after the processing of tokenization using [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) tokenizer followed by underscore tokenization. The reason for this is that some words are in bad format: e.g. "điện.thoại của tôi" is split into ["điện.thoại", "của", "tôi"] instead of ["điện", "thoại", "của", "tôi"] if we use space tokenization, which is not in the right format of Syllable. As that, we used VnCoreNLP to tokenize first and then split words into tokens. e.g. "điện.thoại của tôi" ---(VnCoreNLP)---> ["điện_thoại", "của", "tôi"] ---(split by "_")---> ["điện", "thoại", "của", "tôi"]. - **tag**: The tag of the word. The tag is either B-T (beginning of a word), I-T (inside of a word), or O (outside of a word). - The train_BIO_syllable and dev_BIO_syllable file are used for training and validation for XLMR model, respectively. - The test_BIO_syllable file is used for reference only. It is not used for testing the model. **Please use the test.csv file in the Testdata folder for testing the model.** #### Word Description: - This folder contains the data for the sequence labeling-based version of the task. The data is divided into two files: train, and dev. Each file contains the following columns: - **index**: The id of the word. - **word**: Words in the sentence after the processing of tokenization using [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) tokenizer - **tag**: The tag of the word. The tag is either B-T (beginning of a word), I-T (inside of a word), or O (outside of a word). - The train_BIO_Word and dev_BIO_Word file are used for training and validation for PhoBERT model, respectively. - The test_BIO_Word file is used for reference only. It is not used for testing the model. **Please use the test.csv file in the data/test folder for testing the model.** ### Span Extraction-based version Description: - This folder contains the data for the span extraction-based version of the task. The data is divided into two files: train and dev. Each file contains the following columns: - **content**: The content of the sentence. - **span_ids**: The index of the hate and offensive spans in the sentence. The index is in the format of [start, end] where start is the index of the first character of the hate and offensive span and end is the index of the last character of the hate and offensive span. - The train and dev file are used for training and validation for BiLSTM-CRF model, respectively. ### Citation Information ``` @inproceedings{hoang-etal-2023-vihos, title = "{V}i{HOS}: Hate Speech Spans Detection for {V}ietnamese", author = "Hoang, Phu Gia and Luu, Canh Duc and Tran, Khanh Quoc and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.eacl-main.47", doi = "10.18653/v1/2023.eacl-main.47", pages = "652--669", abstract = "The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagged comments filtered by classification systems. To help address this issue, we present the ViHOS (Vietnamese Hate and Offensive Spans) dataset, the first human-annotated corpus containing 26k spans on 11k comments. We also provide definitions of hateful and offensive spans in Vietnamese comments as well as detailed annotation guidelines. Besides, we conduct experiments with various state-of-the-art models. Specifically, XLM-R{\_}Large achieved the best F1-scores in Single span detection and All spans detection, while PhoBERT{\_}Large obtained the highest in Multiple spans detection. Finally, our error analysis demonstrates the difficulties in detecting specific types of spans in our data for future research. Our dataset is released on GitHub.", } ```

felipebandeira/invoiceupload1

--- license: mit dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 234466949.0 num_examples: 425 - name: test num_bytes: 15053216.0 num_examples: 26 - name: validation num_bytes: 26678659.0 num_examples: 50 download_size: 197788456 dataset_size: 276198824.0 ---

justinsiow/UECFOOD100

--- license: apache-2.0 ---

kalhosni/CustomerChurnTelecom

--- license: apache-2.0 ---

esue/p_dataset

--- license: mit ---

open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16

--- pretty_name: Evaluation run of OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16](https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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 agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T03:42:28.997128](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16/blob/main/results_2023-10-28T03-42-28.997128.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.360633389261745,\n\ \ \"em_stderr\": 0.004917536525106699,\n \"f1\": 0.4180935402684579,\n\ \ \"f1_stderr\": 0.004778710905980245,\n \"acc\": 0.5268440191410464,\n\ \ \"acc_stderr\": 0.012939810741097795\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.360633389261745,\n \"em_stderr\": 0.004917536525106699,\n\ \ \"f1\": 0.4180935402684579,\n \"f1_stderr\": 0.004778710905980245\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3457164518574678,\n \ \ \"acc_stderr\": 0.013100422990441578\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7079715864246251,\n \"acc_stderr\": 0.012779198491754013\n\ \ }\n}\n```" repo_url: https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|arc:challenge|25_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-03T23-40-22.620996.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T21_47_43.594265 path: - '**/details_harness|drop|3_2023-10-27T21-47-43.594265.parquet' - split: 2023_10_28T03_42_28.997128 path: - '**/details_harness|drop|3_2023-10-28T03-42-28.997128.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T03-42-28.997128.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T21_47_43.594265 path: - '**/details_harness|gsm8k|5_2023-10-27T21-47-43.594265.parquet' - split: 2023_10_28T03_42_28.997128 path: - '**/details_harness|gsm8k|5_2023-10-28T03-42-28.997128.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T03-42-28.997128.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hellaswag|10_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-03T23-40-22.620996.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-management|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T23-40-22.620996.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_03T23_40_22.620996 path: - '**/details_harness|truthfulqa:mc|0_2023-10-03T23-40-22.620996.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-03T23-40-22.620996.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T21_47_43.594265 path: - '**/details_harness|winogrande|5_2023-10-27T21-47-43.594265.parquet' - split: 2023_10_28T03_42_28.997128 path: - '**/details_harness|winogrande|5_2023-10-28T03-42-28.997128.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T03-42-28.997128.parquet' - config_name: results data_files: - split: 2023_10_03T23_40_22.620996 path: - results_2023-10-03T23-40-22.620996.parquet - split: 2023_10_27T21_47_43.594265 path: - results_2023-10-27T21-47-43.594265.parquet - split: 2023_10_28T03_42_28.997128 path: - results_2023-10-28T03-42-28.997128.parquet - split: latest path: - results_2023-10-28T03-42-28.997128.parquet --- # Dataset Card for Evaluation run of OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16 - **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 [OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16](https://huggingface.co/OpenBuddy/openbuddy-codellama2-34b-v11.1-bf16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T03:42:28.997128](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-codellama2-34b-v11.1-bf16/blob/main/results_2023-10-28T03-42-28.997128.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.360633389261745, "em_stderr": 0.004917536525106699, "f1": 0.4180935402684579, "f1_stderr": 0.004778710905980245, "acc": 0.5268440191410464, "acc_stderr": 0.012939810741097795 }, "harness|drop|3": { "em": 0.360633389261745, "em_stderr": 0.004917536525106699, "f1": 0.4180935402684579, "f1_stderr": 0.004778710905980245 }, "harness|gsm8k|5": { "acc": 0.3457164518574678, "acc_stderr": 0.013100422990441578 }, "harness|winogrande|5": { "acc": 0.7079715864246251, "acc_stderr": 0.012779198491754013 } } ``` ### 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]

ineoApp/facture_ds_01

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: bboxes sequence: sequence: int64 - name: ner_tags sequence: class_label: names: '0': O '1': numero facture '2': fournisseur '3': date facture '4': date limite '5': montant ht '6': montant ttc '7': tva '8': prix tva '9': addresse '10': reference '11': art1 designation '12': art1 quantite '13': art1 prix unit '14': art1 tva '15': art1 montant ht '16': art2 designation '17': art2 quantite '18': art2 prix unit '19': art2 tva '20': art2 montant ht - name: tokens sequence: string splits: - name: train num_bytes: 14736563.333333334 num_examples: 14 - name: test num_bytes: 4210446.666666667 num_examples: 4 download_size: 6308297 dataset_size: 18947010.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

namespace-Pt/msmarco

--- configs: - config_name: default data_files: - split: dev path: data/dev-* dataset_info: features: - name: query dtype: string - name: positive sequence: string splits: - name: dev num_bytes: 2962960 num_examples: 6980 download_size: 1925216 dataset_size: 2962960 --- # Dataset Card for "msmarco" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

akkasi/clmet

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: labels sequence: float64 - name: label2idx dtype: string - name: idx2label dtype: string splits: - name: train num_bytes: 149061943 num_examples: 266 - name: test num_bytes: 50034891 num_examples: 67 download_size: 117110210 dataset_size: 199096834 --- # Dataset Card for "clmet_new" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

youngs1998/DeepSpace_KE

--- license: mit language: - zh size_categories: - 1K<n<10K ---

smudkavi/indic_language_corpus

--- license: mit ---

HydraLM/partitioned_v3_standardized_01

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 15176523.9300594 num_examples: 28224 download_size: 9592708 dataset_size: 15176523.9300594 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_01" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-source-metrics/preprocessed_stars

--- dataset_info: features: - name: transformers dtype: int64 - name: peft dtype: int64 - name: evaluate dtype: int64 - name: huggingface_hub dtype: int64 - name: accelerate dtype: int64 - name: datasets dtype: int64 - name: optimum dtype: int64 - name: pytorch_image_models dtype: int64 - name: gradio dtype: int64 - name: tokenizers dtype: int64 - name: diffusers dtype: int64 - name: safetensors dtype: int64 - name: sentence_transformers dtype: int64 - name: candle dtype: int64 - name: text_generation_inference dtype: int64 - name: chat_ui dtype: int64 - name: hub_docs dtype: int64 - name: openai_python dtype: int64 - name: stable_diffusion_webui dtype: int64 - name: langchain dtype: int64 - name: pytorch dtype: int64 - name: tensorflow dtype: int64 - name: day dtype: string splits: - name: raw num_bytes: 159366994 num_examples: 786512 - name: wow num_bytes: 746681 num_examples: 3685 download_size: 16972346 dataset_size: 160113675 configs: - config_name: default data_files: - split: raw path: data/raw-* - split: wow path: data/wow-* --- # Dataset Card for "preprocessed_stars" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Siddharthr30/multilabel_sentiment_analysis

--- 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: text dtype: string - name: labels sequence: int64 splits: - name: train num_bytes: 1183783 num_examples: 2260 - name: validation num_bytes: 334615 num_examples: 642 - name: test num_bytes: 335307 num_examples: 643 download_size: 86464 dataset_size: 1853705 --- # Dataset Card for "multilabel_sentiment_analysis" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lhallee/ec_feature_ranking

--- dataset_info: features: - name: Entry dtype: string - name: EC number dtype: string - name: Sequence dtype: string - name: 1st dtype: int64 - name: Class dtype: int64 - name: group dtype: int64 splits: - name: train num_bytes: 232772386 num_examples: 530876 download_size: 219414643 dataset_size: 232772386 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ec_feature_ranking" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

datahrvoje/twitter_dataset_1712997900

--- 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: 25665 num_examples: 56 download_size: 13184 dataset_size: 25665 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/yoneme_mei_lovelivesuperstar

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yoneme_mei/米女メイ/요네메메이 (Love Live! Superstar!!) This is the dataset of yoneme_mei/米女メイ/요네메메이 (Love Live! Superstar!!), containing 200 images and their tags. The core tags of this character are `red_hair, blue_eyes, bangs, hair_bun, long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 200 | 288.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 200 | 144.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 444 | 302.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 200 | 247.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 444 | 482.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yoneme_mei_lovelivesuperstar/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/yoneme_mei_lovelivesuperstar', 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 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, collarbone, short_sleeves, sidelocks, single_side_bun, smile, upper_body, birthday, blush, shiny_hair, single_hair_bun, dress, necktie | | 1 | 17 | ![](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, solo, yuigaoka_school_uniform, blue_jacket, grey_dress, looking_at_viewer, collared_shirt, white_shirt, long_sleeves, white_background, blush, simple_background, hair_between_eyes, open_jacket, pinafore_dress, closed_mouth, brown_footwear, loafers, medium_hair, smile | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | blush, yuigaoka_school_uniform, 2girls, shiny_hair, upper_body, birthday, double_bun, sidelocks, solo_focus, collared_shirt, jacket, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | collarbone | short_sleeves | sidelocks | single_side_bun | smile | upper_body | birthday | blush | shiny_hair | single_hair_bun | dress | necktie | yuigaoka_school_uniform | blue_jacket | grey_dress | collared_shirt | white_shirt | long_sleeves | white_background | simple_background | hair_between_eyes | open_jacket | pinafore_dress | closed_mouth | brown_footwear | loafers | medium_hair | 2girls | double_bun | solo_focus | jacket | open_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------|:----------------|:------------|:------------------|:--------|:-------------|:-----------|:--------|:-------------|:------------------|:--------|:----------|:--------------------------|:--------------|:-------------|:-----------------|:--------------|:---------------|:-------------------|:--------------------|:--------------------|:--------------|:-----------------|:---------------|:-----------------|:----------|:--------------|:---------|:-------------|:-------------|:---------|:-------------| | 0 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 1 | 17 | ![](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 | | | | | | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | | | | | | X | | | X | X | X | X | | | | X | | | X | | | | | | | | | | | | X | X | X | X | X |

AdapterOcean/med_alpaca_standardized_cluster_87

--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 102798830 num_examples: 10573 download_size: 30260860 dataset_size: 102798830 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_87" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AnimaLab/bias-test-gpt-sentences

--- license: apache-2.0 language: - en pretty_name: BiasTestGPT size_categories: - 10K<n<100K --- # Dataset Card for "BiasTestGPT: Generated Test Sentences" Dataset of sentences for bias testing in open-sourced Pretrained Language Models generated using ChatGPT and other generative Language Models. This dataset is used and actively populated by the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs). - [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs) - [Dataset with Bias Specifications](https://huggingface.co/datasets/AnimaLab/bias-test-gpt-biases) - [Project Landing Page](https://biastest-animalab.github.io/) ## Dataset Structure The dataset is structured as a set of CSV files with names corresponding to the social group term for which the test sentences were generated. Each separate file contains the sentences generated with the instruction of connecting this social group term to other attribute terms. ### Data Instances Examples of 'generated sentences' from the dataset in CSV format look as follows (not all columns are shown). | sentence | alt_sentence | org_grp_term | att_term | grp_term1 | grp_term2 | type | gen_model | |----------|--------------|--------------|----------|-----------|-----------|------|----------:| | Adam felt a sense of freedom as he left his old job and pursued his passion. | Jane felt a sense of freedom as she left her old job and pursued her passion. | Adam | freedom | Adam | Jane | paper | gpt-3.5 | | My husband and I are excited to start a family together. | My wife and I are excited to start a family together. | husband | family | husband | wife | tool | gpt-3.5 | My daughter loves to experiment with different ingredients when she's cooking. | My son loves to experiment with different ingredients when he's cooking. | daughter | cooking | daughter | son | paper | gpt-3.5 | | A woman solves complex math problems with ease | A man solves complex math problems with ease | woman | math | woman | man | tool | gpt-3.5 ### Data Fields Here we describe the data fields in the dataset. These are the same across all the splits. #### CSV columns - **'sentence'**: a 'string' feature - PLM generated test sentence that includes 'grp_term1' and 'att_term' - **'alt_sentence'**: a 'string' feature - PLM generated alternative version of the test sentence that includes 'grp_term2' and 'att_term' - **'org_grp_term'**: a `string' feature - a social group term for which the sentence was generated. - **'att_term'**: a 'string' feature - an attribute term for which the sentence was created. - **'template'**: a 'string' feature - a templated version of the sentence with social group replaced by [T] - **'alt_template'**: a 'string' feature - a templated version of the sentence with social group replaced by [T] and other token differences replaced by [R] - **'grp_term1'** - a 'string' feature - a term from social group 1 used in *'sentence'* - **'grp_term2'** - a 'string' feature - a term from social group 2 used in *'alt_sentence'* - **'grp_refs'** - a 'list' feature - a list of differences between the *'sentence'* and *'alt_sentence'* apart of group_term. Each item is a tuple with paired versions of tokens from 'sentence' and 'alt_sentnece'. - **'label_1'** - a 'string' feature - whether filling in the template with **group term 1** is considered to produce a 'stereotype' or 'anti-stereotype' - **'label_2'** - a 'string' feature - whether filling in the template with **group term 2** is considered to produce a 'stereotype' or 'anti-stereotype' - **'bias_spec'** - a 'string' feature - the name of the bias specification for which the sentence was generated - **'type'**: a 'string' feature - the source of the generation; `paper' indicates the sentence was used in the analysis in the paper, another value indicates the sentence generated using the HuggingFace tool - **'gen_model'**: a 'string' feature - the name of the generator model used ### Data Splits The repository contains 14k+ sentences generated using ChatGPT and another very large PLM. The analysis in the paper was conducted using the sentences from ChatGPT only. Additional test sentences have been added afterward as a result of interaction with the tool. We note that the number of sentences is constantly growing as it is being populated by the interactions with the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs). | Type | Meaning | Train | |--------|---------|------:| | paper | Test sentences used in the analysis in the paper | 9k+ | | tool | Novel test sentences added to the dataset based on interactions with the [bias test tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs) | 500+ |

dqymaggie/brighten-300-dataset

--- dataset_info: features: - name: instruction dtype: string - name: input_image dtype: image - name: ground_truth_image dtype: image splits: - name: train num_bytes: 4739155776.0 num_examples: 300 download_size: 4615985191 dataset_size: 4739155776.0 --- # Dataset Card for "brighten-300-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TinyPixel/claude_multiround_chat_1k

--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 17754888 num_examples: 1609 download_size: 9514689 dataset_size: 17754888 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "claude_multiround_chat_1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zeroshot/arxiv-biology

--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc0-1.0 multilinguality: - monolingual --- ![;)](https://media.giphy.com/media/xd9HUXswWPY1EEJ80a/giphy.gif) ### Dataset Curators The original data is maintained by [ArXiv](https://arxiv.org/) ### Licensing Information The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/) ### Citation Information ``` @misc{clement2019arxiv, title={On the Use of ArXiv as a Dataset}, author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi}, year={2019}, eprint={1905.00075}, archivePrefix={arXiv}, primaryClass={cs.IR} } ```

faizalbs777/research

--- license: mit task_categories: - text-generation - summarization - table-question-answering --- # QTSumm Dataset The **QTSumm** dataset is a large-scale dataset for the task of **query-focused summarization over tabular data**. It contains 7,111 human-annotated query-summary pairs over 2,934 tables covering diverse topics. To solve this task, a text generation system has to perform **human-like reasoning and analysis** over the given table to generate a tailored summary. ## Citation ``` @misc{zhao2023qtsumm, title={QTSumm: Query-Focused Summarization over Tabular Data}, author={Yilun Zhao and Zhenting Qi and Linyong Nan and Boyu Mi and Yixin Liu and Weijin Zou and Simeng Han and Ruizhe Chen and Xiangru Tang and Yumo Xu and Arman Cohan and Dragomir Radev}, year={2023}, eprint={2305.14303}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

RFDweeb/Samplekeybpm

--- license: unknown ---

livinNector/indic_corp

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11971668653 num_examples: 31542969 download_size: 4821559421 dataset_size: 11971668653 --- # Dataset Card for "indic_corp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pccl-org/formal-logic-simple-order-new-objects-paired-thicker-2000

--- dataset_info: features: - name: greater_than dtype: string - name: less_than dtype: string - name: paired_example sequence: sequence: string - name: correct_example sequence: string - name: incorrect_example sequence: string - name: distance dtype: int64 - name: index dtype: int64 - name: index_in_distance dtype: int64 splits: - name: train num_bytes: 513562624 num_examples: 1997003 download_size: 162420554 dataset_size: 513562624 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_160m_thr_1.0_seed_3

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43586042 num_examples: 18929 - name: epoch_1 num_bytes: 44146512 num_examples: 18929 - name: epoch_2 num_bytes: 44256387 num_examples: 18929 - name: epoch_3 num_bytes: 44308733 num_examples: 18929 - name: epoch_4 num_bytes: 44353098 num_examples: 18929 - name: epoch_5 num_bytes: 44378040 num_examples: 18929 - name: epoch_6 num_bytes: 44403487 num_examples: 18929 - name: epoch_7 num_bytes: 44415332 num_examples: 18929 - name: epoch_8 num_bytes: 44425863 num_examples: 18929 - name: epoch_9 num_bytes: 44443535 num_examples: 18929 - name: epoch_10 num_bytes: 44439562 num_examples: 18929 - name: epoch_11 num_bytes: 44440304 num_examples: 18929 - name: epoch_12 num_bytes: 44443403 num_examples: 18929 - name: epoch_13 num_bytes: 44446694 num_examples: 18929 - name: epoch_14 num_bytes: 44449228 num_examples: 18929 - name: epoch_15 num_bytes: 44448355 num_examples: 18929 - name: epoch_16 num_bytes: 44449749 num_examples: 18929 - name: epoch_17 num_bytes: 44448622 num_examples: 18929 - name: epoch_18 num_bytes: 44452122 num_examples: 18929 - name: epoch_19 num_bytes: 44453828 num_examples: 18929 - name: epoch_20 num_bytes: 44455832 num_examples: 18929 - name: epoch_21 num_bytes: 44455503 num_examples: 18929 - name: epoch_22 num_bytes: 44455394 num_examples: 18929 - name: epoch_23 num_bytes: 44455257 num_examples: 18929 - name: epoch_24 num_bytes: 44456872 num_examples: 18929 - name: epoch_25 num_bytes: 44456475 num_examples: 18929 - name: epoch_26 num_bytes: 44457961 num_examples: 18929 - name: epoch_27 num_bytes: 44456736 num_examples: 18929 - name: epoch_28 num_bytes: 44457605 num_examples: 18929 - name: epoch_29 num_bytes: 44460162 num_examples: 18929 download_size: 1401205198 dataset_size: 1331756693 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

open-llm-leaderboard/details_kyujinpy__SOLAR-Platypus-10.7B-v1

--- pretty_name: Evaluation run of kyujinpy/SOLAR-Platypus-10.7B-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kyujinpy/SOLAR-Platypus-10.7B-v1](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v1)\ \ 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__SOLAR-Platypus-10.7B-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T16:18:16.203947](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__SOLAR-Platypus-10.7B-v1/blob/main/results_2023-12-16T16-18-16.203947.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\": 0.5995716192292146,\n\ \ \"acc_stderr\": 0.03274801514976459,\n \"acc_norm\": 0.6080034028429626,\n\ \ \"acc_norm_stderr\": 0.033508703676958934,\n \"mc1\": 0.35006119951040393,\n\ \ \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5157940312549367,\n\ \ \"mc2_stderr\": 0.01467999948196073\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5784982935153583,\n \"acc_stderr\": 0.014430197069326023,\n\ \ \"acc_norm\": 0.6168941979522184,\n \"acc_norm_stderr\": 0.014206472661672877\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6436964748058156,\n\ \ \"acc_stderr\": 0.004779276329704051,\n \"acc_norm\": 0.8422624975104561,\n\ \ \"acc_norm_stderr\": 0.003637497708934033\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\ \ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\ \ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013317,\n\ \ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013317\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.67,\n\ \ \"acc_stderr\": 0.047258156262526066,\n \"acc_norm\": 0.67,\n \ \ \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6490566037735849,\n \"acc_stderr\": 0.02937364625323469,\n\ \ \"acc_norm\": 0.6490566037735849,\n \"acc_norm_stderr\": 0.02937364625323469\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.03773809990686934,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.03773809990686934\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\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\ : 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n\ \ \"acc_stderr\": 0.03714325906302064,\n \"acc_norm\": 0.6127167630057804,\n\ \ \"acc_norm_stderr\": 0.03714325906302064\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207762,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207762\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5234042553191489,\n \"acc_stderr\": 0.03265019475033582,\n\ \ \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.03265019475033582\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406772,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406772\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7193548387096774,\n\ \ \"acc_stderr\": 0.025560604721022884,\n \"acc_norm\": 0.7193548387096774,\n\ \ \"acc_norm_stderr\": 0.025560604721022884\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4187192118226601,\n \"acc_stderr\": 0.034711928605184676,\n\ \ \"acc_norm\": 0.4187192118226601,\n \"acc_norm_stderr\": 0.034711928605184676\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047711,\n\ \ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047711\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790486,\n \"\ acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790486\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.0261484834691533,\n\ \ \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.0261484834691533\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6025641025641025,\n \"acc_stderr\": 0.024811920017903836,\n\ \ \"acc_norm\": 0.6025641025641025,\n \"acc_norm_stderr\": 0.024811920017903836\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3,\n \"acc_stderr\": 0.027940457136228402,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.027940457136228402\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\"\ : {\n \"acc\": 0.5672268907563025,\n \"acc_stderr\": 0.032183581077426124,\n\ \ \"acc_norm\": 0.5672268907563025,\n \"acc_norm_stderr\": 0.032183581077426124\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7926605504587156,\n \"acc_stderr\": 0.017381415563608678,\n \"\ acc_norm\": 0.7926605504587156,\n \"acc_norm_stderr\": 0.017381415563608678\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.42592592592592593,\n \"acc_stderr\": 0.03372343271653063,\n \"\ acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.03372343271653063\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.02615686752393104,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.02615686752393104\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8227848101265823,\n \"acc_stderr\": 0.024856364184503224,\n \ \ \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.024856364184503224\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575498,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575498\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n\ \ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6694214876033058,\n \"acc_stderr\": 0.04294340845212094,\n \"\ acc_norm\": 0.6694214876033058,\n \"acc_norm_stderr\": 0.04294340845212094\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6748466257668712,\n \"acc_stderr\": 0.036803503712864595,\n\ \ \"acc_norm\": 0.6748466257668712,\n \"acc_norm_stderr\": 0.036803503712864595\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.042450224863844935,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.042450224863844935\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\ \ \"acc_stderr\": 0.02390232554956039,\n \"acc_norm\": 0.8418803418803419,\n\ \ \"acc_norm_stderr\": 0.02390232554956039\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8109833971902938,\n\ \ \"acc_stderr\": 0.014000791294407004,\n \"acc_norm\": 0.8109833971902938,\n\ \ \"acc_norm_stderr\": 0.014000791294407004\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6213872832369942,\n \"acc_stderr\": 0.02611374936131034,\n\ \ \"acc_norm\": 0.6213872832369942,\n \"acc_norm_stderr\": 0.02611374936131034\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2737430167597765,\n\ \ \"acc_stderr\": 0.014912413096372435,\n \"acc_norm\": 0.2737430167597765,\n\ \ \"acc_norm_stderr\": 0.014912413096372435\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6601307189542484,\n \"acc_stderr\": 0.027121956071388856,\n\ \ \"acc_norm\": 0.6601307189542484,\n \"acc_norm_stderr\": 0.027121956071388856\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6591639871382636,\n\ \ \"acc_stderr\": 0.026920841260776165,\n \"acc_norm\": 0.6591639871382636,\n\ \ \"acc_norm_stderr\": 0.026920841260776165\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.024748624490537382,\n\ \ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.024748624490537382\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \ \ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.42242503259452413,\n\ \ \"acc_stderr\": 0.01261560047573492,\n \"acc_norm\": 0.42242503259452413,\n\ \ \"acc_norm_stderr\": 0.01261560047573492\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5845588235294118,\n \"acc_stderr\": 0.029935342707877746,\n\ \ \"acc_norm\": 0.5845588235294118,\n \"acc_norm_stderr\": 0.029935342707877746\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6045751633986928,\n \"acc_stderr\": 0.019780465954777515,\n \ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.019780465954777515\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\": 0.6816326530612244,\n \"acc_stderr\": 0.02982253379398208,\n\ \ \"acc_norm\": 0.6816326530612244,\n \"acc_norm_stderr\": 0.02982253379398208\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263686,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263686\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35006119951040393,\n\ \ \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5157940312549367,\n\ \ \"mc2_stderr\": 0.01467999948196073\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8279400157853196,\n \"acc_stderr\": 0.010607731615247007\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1106899166034875,\n \ \ \"acc_stderr\": 0.008642172551392492\n }\n}\n```" repo_url: https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v1 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_16T16_18_16.203947 path: - '**/details_harness|arc:challenge|25_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-16T16-18-16.203947.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|gsm8k|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hellaswag|10_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T16-18-16.203947.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T16-18-16.203947.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T16-18-16.203947.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T16_18_16.203947 path: - '**/details_harness|winogrande|5_2023-12-16T16-18-16.203947.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-16T16-18-16.203947.parquet' - config_name: results data_files: - split: 2023_12_16T16_18_16.203947 path: - results_2023-12-16T16-18-16.203947.parquet - split: latest path: - results_2023-12-16T16-18-16.203947.parquet --- # Dataset Card for Evaluation run of kyujinpy/SOLAR-Platypus-10.7B-v1  Dataset automatically created during the evaluation run of model [kyujinpy/SOLAR-Platypus-10.7B-v1](https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v1) 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__SOLAR-Platypus-10.7B-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T16:18:16.203947](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__SOLAR-Platypus-10.7B-v1/blob/main/results_2023-12-16T16-18-16.203947.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": 0.5995716192292146, "acc_stderr": 0.03274801514976459, "acc_norm": 0.6080034028429626, "acc_norm_stderr": 0.033508703676958934, "mc1": 0.35006119951040393, "mc1_stderr": 0.01669794942015103, "mc2": 0.5157940312549367, "mc2_stderr": 0.01467999948196073 }, "harness|arc:challenge|25": { "acc": 0.5784982935153583, "acc_stderr": 0.014430197069326023, "acc_norm": 0.6168941979522184, "acc_norm_stderr": 0.014206472661672877 }, "harness|hellaswag|10": { "acc": 0.6436964748058156, "acc_stderr": 0.004779276329704051, "acc_norm": 0.8422624975104561, "acc_norm_stderr": 0.003637497708934033 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6447368421052632, "acc_stderr": 0.03894734487013317, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013317 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.67, "acc_stderr": 0.047258156262526066, "acc_norm": 0.67, "acc_norm_stderr": 0.047258156262526066 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6490566037735849, "acc_stderr": 0.02937364625323469, "acc_norm": 0.6490566037735849, "acc_norm_stderr": 0.02937364625323469 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.03773809990686934, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.03773809990686934 }, "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": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302064, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302064 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207762, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207762 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909281, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909281 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5234042553191489, "acc_stderr": 0.03265019475033582, "acc_norm": 0.5234042553191489, "acc_norm_stderr": 0.03265019475033582 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.041546596717075474, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.041546596717075474 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.025446365634406772, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.025446365634406772 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7193548387096774, "acc_stderr": 0.025560604721022884, "acc_norm": 0.7193548387096774, "acc_norm_stderr": 0.025560604721022884 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4187192118226601, "acc_stderr": 0.034711928605184676, "acc_norm": 0.4187192118226601, "acc_norm_stderr": 0.034711928605184676 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.793939393939394, "acc_stderr": 0.03158415324047711, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.03158415324047711 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.029620227874790486, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.029620227874790486 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.844559585492228, "acc_stderr": 0.0261484834691533, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.0261484834691533 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6025641025641025, "acc_stderr": 0.024811920017903836, "acc_norm": 0.6025641025641025, "acc_norm_stderr": 0.024811920017903836 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3, "acc_stderr": 0.027940457136228402, "acc_norm": 0.3, "acc_norm_stderr": 0.027940457136228402 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5672268907563025, "acc_stderr": 0.032183581077426124, "acc_norm": 0.5672268907563025, "acc_norm_stderr": 0.032183581077426124 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7926605504587156, "acc_stderr": 0.017381415563608678, "acc_norm": 0.7926605504587156, "acc_norm_stderr": 0.017381415563608678 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.42592592592592593, "acc_stderr": 0.03372343271653063, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.03372343271653063 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.02615686752393104, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.02615686752393104 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8227848101265823, "acc_stderr": 0.024856364184503224, "acc_norm": 0.8227848101265823, "acc_norm_stderr": 0.024856364184503224 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6771300448430493, "acc_stderr": 0.03138147637575498, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575498 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.648854961832061, "acc_stderr": 0.04186445163013751, "acc_norm": 0.648854961832061, "acc_norm_stderr": 0.04186445163013751 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6694214876033058, "acc_stderr": 0.04294340845212094, "acc_norm": 0.6694214876033058, "acc_norm_stderr": 0.04294340845212094 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6748466257668712, "acc_stderr": 0.036803503712864595, "acc_norm": 0.6748466257668712, "acc_norm_stderr": 0.036803503712864595 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.042450224863844935, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.042450224863844935 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8418803418803419, "acc_stderr": 0.02390232554956039, "acc_norm": 0.8418803418803419, "acc_norm_stderr": 0.02390232554956039 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8109833971902938, "acc_stderr": 0.014000791294407004, "acc_norm": 0.8109833971902938, "acc_norm_stderr": 0.014000791294407004 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6213872832369942, "acc_stderr": 0.02611374936131034, "acc_norm": 0.6213872832369942, "acc_norm_stderr": 0.02611374936131034 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2737430167597765, "acc_stderr": 0.014912413096372435, "acc_norm": 0.2737430167597765, "acc_norm_stderr": 0.014912413096372435 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6601307189542484, "acc_stderr": 0.027121956071388856, "acc_norm": 0.6601307189542484, "acc_norm_stderr": 0.027121956071388856 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6591639871382636, "acc_stderr": 0.026920841260776165, "acc_norm": 0.6591639871382636, "acc_norm_stderr": 0.026920841260776165 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7283950617283951, "acc_stderr": 0.024748624490537382, "acc_norm": 0.7283950617283951, "acc_norm_stderr": 0.024748624490537382 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4645390070921986, "acc_stderr": 0.02975238965742705, "acc_norm": 0.4645390070921986, "acc_norm_stderr": 0.02975238965742705 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.42242503259452413, "acc_stderr": 0.01261560047573492, "acc_norm": 0.42242503259452413, "acc_norm_stderr": 0.01261560047573492 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5845588235294118, "acc_stderr": 0.029935342707877746, "acc_norm": 0.5845588235294118, "acc_norm_stderr": 0.029935342707877746 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6045751633986928, "acc_stderr": 0.019780465954777515, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.019780465954777515 }, "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": 0.6816326530612244, "acc_stderr": 0.02982253379398208, "acc_norm": 0.6816326530612244, "acc_norm_stderr": 0.02982253379398208 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801302, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801302 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.035887028128263686, "acc_norm": 0.85, "acc_norm_stderr": 0.035887028128263686 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness|truthfulqa:mc|0": { "mc1": 0.35006119951040393, "mc1_stderr": 0.01669794942015103, "mc2": 0.5157940312549367, "mc2_stderr": 0.01467999948196073 }, "harness|winogrande|5": { "acc": 0.8279400157853196, "acc_stderr": 0.010607731615247007 }, "harness|gsm8k|5": { "acc": 0.1106899166034875, "acc_stderr": 0.008642172551392492 } } ``` ## 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]

kukis/itsuvoice

--- license: openrail ---

ovieyra21/mabama-v5

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 61744267.0 num_examples: 48 download_size: 60925153 dataset_size: 61744267.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ignacioct/instruction_example_qualityscorer

--- dataset_info: features: - name: instruction dtype: string - name: completion dtype: string - name: generation dtype: string - name: model_name dtype: string - name: meta struct: - name: category dtype: string - name: completion dtype: string - name: prompt dtype: string - name: source dtype: string - name: subcategory dtype: string splits: - name: train num_bytes: 1356 num_examples: 1 download_size: 13037 dataset_size: 1356 configs: - config_name: default data_files: - split: train path: data/train-* ---

Empolyon2/PokemonDataset

--- license: apache-2.0 task_categories: - image-classification language: - en tags: - text - image size_categories: - 1K<n<10K pretty_name: PokemonDataset --- --- TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging --- # Dataset Card for Pokemon Gen 1 ## Dataset Description - **Short Description:** This dataset comprises images along with corresponding textual prompts. It contains 149 subfolders, each representing a unique category, with multiple images. Each category is associated with specific prompts, as detailed in an accompanying Excel sheet. - **Purpose:** The dataset is designed for training models that can understand and generate Pokemon images based on textual prompts. - **Data Collection and Processing:** Images were sourced from [source of images]. Textual prompts were created to accurately describe or relate to the images. Images were processed for resizing, removing bad data, normalization, augmentation, and enhancement. ## Dataset Structure - **Data Instances:** A typical data instance consists of a textual prompt and a corresponding image path. - **Data Fields:** - `prompt`: A string containing the textual description or cue associated with the image. - `image_file`: The path to the image file related to the prompt. - **Data Splits:** The dataset is not explicitly split. All instances are part of a single batch. Users can create training, validation, and test splits as needed. ## Dataset Creation - **Creators:** This dataset was created by Kerem Topalismailoglu. - **Motivation:** APS360. ## Additional Information - **Curation Rationale:** The dataset was curated to cover a diverse range of images and corresponding descriptive prompts. - **Source Data:** The images were sourced from [source], ensuring a wide variety of visual content. - **Annotations:** The dataset does not include additional annotations beyond the image-prompt pairs. ## Usage - **Using the Dataset with Hugging Face:** ```python from datasets import load_dataset dataset = load_dataset("path_to_my_dataset") ``` ## Dataset Card Creation - **Who Created the Dataset Card:** [Your Name/Organization] ## Citation - **Citations:** [Include any relevant citations for the dataset or sources of the images.]

thangved/zitwaste

--- license: openrail ---

gcjavi/dataviewer-test-v3

--- dataset_info: - config_name: clean features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: gender dtype: string splits: - name: train num_bytes: 93462.0 num_examples: 3 - name: test num_bytes: 31804.0 num_examples: 1 download_size: 259706 dataset_size: 125266.0 - config_name: other features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: gender dtype: string splits: - name: train num_bytes: 93472.0 num_examples: 3 - name: test num_bytes: 31799.0 num_examples: 1 download_size: 129865 dataset_size: 125271.0 configs: - config_name: clean data_files: - split: train path: clean/train-* - split: test path: clean/test-* - config_name: other data_files: - split: train path: other/train-* - split: test path: other/test-* ---

rsouza17/modelo-ia-voz-rei2

--- license: openrail ---

Joe02/Sian_refs

--- license: other ---

FirstLast/reddit_tngrsnew

--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 448389 num_examples: 1973 download_size: 259371 dataset_size: 448389 configs: - config_name: default data_files: - split: train path: data/train-* ---

wookyungseo/koAlapaca-test

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 13188020 num_examples: 49620 download_size: 7262051 dataset_size: 13188020 configs: - config_name: default data_files: - split: train path: data/train-* ---

hayesyang/un_corpus_seed

--- dataset_info: features: - name: id dtype: int64 - name: url dtype: string splits: - name: train num_bytes: 258559 num_examples: 3733 download_size: 93162 dataset_size: 258559 --- # Dataset Card for "un_corpus_seed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/kaga_kantaicollection

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kaga/加賀 (Kantai Collection) This is the dataset of kaga/加賀 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `brown_hair, side_ponytail, brown_eyes, short_hair, long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 458.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 317.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1142 | 636.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 427.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1142 | 812.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kaga_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kaga_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 45 | ![](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, bow_(weapon), muneate, solo, yugake, arrow_(projectile), single_glove, tasuki, black_thighhighs, flight_deck, quiver, hakama_short_skirt, looking_at_viewer | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_thighhighs, japanese_clothes, muneate, skirt, solo, looking_at_viewer, sitting, tasuki, white_background | | 2 | 19 | ![](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, hakama_short_skirt, solo, tasuki, looking_at_viewer, muneate, blue_hakama, simple_background, black_thighhighs, white_background | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, black_thighhighs, cleavage, japanese_clothes, large_breasts, looking_at_viewer, skirt, solo, blush, off_shoulder, wariza, bare_shoulders, medium_breasts | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, japanese_clothes, solo, muneate, looking_at_viewer, upper_body | | 5 | 7 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, japanese_clothes, looking_at_viewer, simple_background, solo, tasuki, upper_body, muneate, white_background, hair_between_eyes, alternate_hairstyle, hair_down | | 6 | 10 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, artist_name, blue_hakama, chibi, hair_between_eyes, hakama_short_skirt, solo, tasuki, blush, black_thighhighs, seiza, minigirl, eating, food, holding | | 7 | 10 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, artist_name, chibi, hair_between_eyes, japanese_clothes, open_mouth, tasuki, solo, :d, blush, closed_eyes | | 8 | 11 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, solo, hair_between_eyes, large_breasts, collarbone, looking_at_viewer, alternate_costume, blush, simple_background, white_background, long_sleeves, closed_mouth, upper_body, blue_sweater, cleavage | | 9 | 20 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, solo, alternate_costume, looking_at_viewer, blue_kimono, hair_flower, obi, floral_print, blush, upper_body, oil-paper_umbrella | | 10 | 5 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, black_dress, blush, enmaided, looking_at_viewer, solo, white_apron, hair_between_eyes, maid_apron, maid_headdress, cowboy_shot, large_breasts, closed_mouth, frills, long_sleeves, puffy_short_sleeves, simple_background, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bow_(weapon) | muneate | solo | yugake | arrow_(projectile) | single_glove | tasuki | black_thighhighs | flight_deck | quiver | hakama_short_skirt | looking_at_viewer | japanese_clothes | skirt | sitting | white_background | blue_hakama | simple_background | cleavage | large_breasts | blush | off_shoulder | wariza | bare_shoulders | medium_breasts | upper_body | hair_between_eyes | alternate_hairstyle | hair_down | artist_name | chibi | seiza | minigirl | eating | food | holding | open_mouth | :d | closed_eyes | collarbone | alternate_costume | long_sleeves | closed_mouth | blue_sweater | blue_kimono | hair_flower | obi | floral_print | oil-paper_umbrella | black_dress | enmaided | white_apron | maid_apron | maid_headdress | cowboy_shot | frills | puffy_short_sleeves | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:----------|:-------|:---------|:---------------------|:---------------|:---------|:-------------------|:--------------|:---------|:---------------------|:--------------------|:-------------------|:--------|:----------|:-------------------|:--------------|:--------------------|:-----------|:----------------|:--------|:---------------|:---------|:-----------------|:-----------------|:-------------|:--------------------|:----------------------|:------------|:--------------|:--------|:--------|:-----------|:---------|:-------|:----------|:-------------|:-----|:--------------|:-------------|:--------------------|:---------------|:---------------|:---------------|:--------------|:--------------|:------|:---------------|:---------------------|:--------------|:-----------|:--------------|:-------------|:-----------------|:--------------|:---------|:----------------------| | 0 | 45 | ![](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 | 10 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 19 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | X | | | | | | | | | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 7 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | X | X | | | | X | | | | | X | X | | | X | | X | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 10 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | X | | | | X | X | | | X | | | | | | X | | | | X | | | | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 7 | 10 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | X | | | | X | | | | | | X | | | | | | | | X | | | | | | X | | | X | X | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | 8 | 11 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | X | | | | | | | | | X | | | | X | | X | X | X | X | | | | | X | X | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | 9 | 20 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | X | | | | | | | | | X | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | | | | | | | | | | 10 | 5 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | | | X | | | | | | | | | X | | | | X | | X | | X | X | | | | | | X | | | | | | | | | | | | | | | X | X | | | | | | | X | X | X | X | X | X | X | X |

may-ohta/MUST-C

--- license: other ---

sled-umich/TRIP

--- annotations_creators: - expert-generated language: - en language_creators: - crowdsourced license: [] multilinguality: - monolingual pretty_name: 'TRIP: Tiered Reasoning for Intuitive Physics' size_categories: - 1K<n<10K source_datasets: - original tags: [] task_categories: - text-classification task_ids: - natural-language-inference --- # [TRIP - Tiered Reasoning for Intuitive Physics](https://aclanthology.org/2021.findings-emnlp.422/) Official dataset for [Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding](https://aclanthology.org/2021.findings-emnlp.422/). Shane Storks, Qiaozi Gao, Yichi Zhang, Joyce Chai. EMNLP Findings, 2021. For our official model and experiment code, please check [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU). ## Overview ![image](trip_sample.png) We introduce Tiered Reasoning for Intuitive Physics (TRIP), a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process. It includes dense annotations for each story capturing multiple tiers of reasoning beyond the end task. From these annotations, we propose a tiered evaluation, where given a pair of highly similar stories (differing only by one sentence which makes one of the stories implausible), systems must jointly identify (1) the plausible story, (2) a pair of conflicting sentences in the implausible story, and (3) the underlying physical states in those sentences causing the conflict. The goal of TRIP is to enable a systematic evaluation of machine coherence toward the end task prediction of plausibility. In particular, we evaluate whether a high-level plausibility prediction can be verified based on lower-level understanding, for example, physical state changes that would support the prediction. ## Download ```python from datasets import load_dataset dataset = load_dataset("sled-umich/TRIP") ``` * [HuggingFace-Dataset](https://huggingface.co/datasets/sled-umich/TRIP) * [GitHub](https://github.com/sled-group/Verifiable-Coherent-NLU) ## Cite ```bibtex @misc{storks2021tiered, title={Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding}, author={Shane Storks and Qiaozi Gao and Yichi Zhang and Joyce Chai}, year={2021}, booktitle={Findings of the Association for Computational Linguistics: EMNLP 2021}, location={Punta Cana, Dominican Republic}, publisher={Association for Computational Linguistics}, } ```

AinzOoalGowns/Testdataset

--- license: apache-2.0 ---

SumayyaAli/accu_qa_dataset

--- task_categories: - question-answering language: - en tags: - medical pretty_name: accupuncture qa dataset size_categories: - n<1K ---

vwxyzjn/cai-conversation-dev1705622085

--- dataset_info: features: - name: init_prompt dtype: string - name: init_response dtype: string - name: critic_prompt dtype: string - name: critic_response dtype: string - name: revision_prompt dtype: string - name: revision_response dtype: string - name: prompt dtype: string - name: messages sequence: string - name: chosen sequence: string - name: rejected sequence: string splits: - name: train_sft num_bytes: 80685581 num_examples: 21268 - name: train_prefs num_bytes: 80873453 num_examples: 21269 - name: test_sft num_bytes: 4369948 num_examples: 1156 - name: test_prefs num_bytes: 4440767 num_examples: 1156 download_size: 74867178 dataset_size: 170369749 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: train_prefs path: data/train_prefs-* - split: test_sft path: data/test_sft-* - split: test_prefs path: data/test_prefs-* --- # Dataset Card for "cai-conversation-dev1705622085" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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

--- license: gpl-3.0 dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: id dtype: string splits: - name: train num_bytes: 66970712.20824251 num_examples: 280033 - name: test num_bytes: 28701938.79175749 num_examples: 120015 download_size: 54828654 dataset_size: 95672651.0 ---

tasksource/arct2

--- license: apache-2.0 task_categories: - text-classification language: - en --- https://github.com/IKMLab/arct2 ```bib @inproceedings{niven-kao-2019-probing, title = "Probing Neural Network Comprehension of Natural Language Arguments", author = "Niven, Timothy and Kao, Hung-Yu", booktitle = "Proceedings of the 57th Conference of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1459", pages = "4658--4664", abstract = "We are surprised to find that BERT{'}s peak performance of 77{\%} on the Argument Reasoning Comprehension Task reaches just three points below the average untrained human baseline. However, we show that this result is entirely accounted for by exploitation of spurious statistical cues in the dataset. We analyze the nature of these cues and demonstrate that a range of models all exploit them. This analysis informs the construction of an adversarial dataset on which all models achieve random accuracy. Our adversarial dataset provides a more robust assessment of argument comprehension and should be adopted as the standard in future work.", } ```

brema76/political_personalization_it

--- license: mit --- Lexicon of words for investigating the political personalization phenomenon in Italian language List of 3,303 personalizing words in Italian language, annotated with the corresponding sentiment classification as referred to political offices. Words are group by category: Moral and behavioral, Physical, Social and economic. Citation info and BibTeX entry <a href="" target="_blank"></a> ```bibtex @article{Bru2023, title={Combining NLP techniques and statistical modeling to analyze gender gaps in the mediated personalization of politics}, author={Brugnoli, Emanuele and Simone, Rosaria and Delmastro, Marco}, journal={}, year={2023}, volume={} } ```

nlplabtdtu/edu-crawl-with-date

--- dataset_info: features: - name: title dtype: string - name: url dtype: string - name: body dtype: string - name: date dtype: string - name: flt_dates sequence: string splits: - name: train num_bytes: 1070649713 num_examples: 278902 download_size: 387393861 dataset_size: 1070649713 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "edu-crawl-with-date" Data crawl education với dữ liệu thời gian (tháng/năm) Dữ liệu thời gian được cập nhật theo cách sau: - chiết xuất từ văn bản - crawl lại một số trang (hiếm) Hiện tại có: 190692 dòng có dữ liệu thời gian ~= 68.37 % [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

SatishFaction/Test_DataSet1

--- license: cc0-1.0 --- This is a test for creating a dataset

open-phi/wile-e

--- dataset_info: features: - name: topic dtype: string - name: model dtype: string - name: concepts sequence: string - name: outline sequence: string - name: markdown dtype: string splits: - name: train num_bytes: 108171787 num_examples: 933 download_size: 41387101 dataset_size: 108171787 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wile-e" [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/8ebe0fb3

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 186 num_examples: 10 download_size: 1339 dataset_size: 186 --- # Dataset Card for "8ebe0fb3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

apailang/mini-dataset-978

--- dataset_info: features: - name: instruction dtype: string - name: input_content dtype: string - name: expected_output dtype: string splits: - name: train num_bytes: 825340 num_examples: 978 download_size: 229601 dataset_size: 825340 configs: - config_name: default data_files: - split: train path: data/train-* ---

bhavnicksm/sentihood

--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: SentiHood Dataset size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - multi-class-classification - natural-language-inference --- # Dataset Card for [SentiHood] ## 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 - **Paper:** https://arxiv.org/abs/1610.03771 - **Leaderboard:** https://paperswithcode.com/sota/aspect-based-sentiment-analysis-on-sentihood ### Dataset Summary Created as a part of the paper "SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods" by Saeidi et al. #### Abstract In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis that assumes a single entity per document and targeted sentiment analysis that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighborhoods are discussed by users. In this context units of text often mention several aspects of one or more neighborhoods. This is the first time that a generic social media platform in this case a QA platform, is used for fine-grained opinion mining. Text coming from QA platforms is far less constrained compared to text from review-specific platforms on which current datasets are based. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Monolingual (only English) ## 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 Thanks to [@Bhavnicksm](https://github.com/Bhavnicksm) for adding this dataset.

pythera/vietnamese-mlmcorpus

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 41663206615.575096 num_examples: 45009627 download_size: 23630062762 dataset_size: 41663206615.575096 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vietnamese-mlmcorpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Mihaiii__Bucharest-0.1

--- pretty_name: Evaluation run of Mihaiii/Bucharest-0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Mihaiii/Bucharest-0.1](https://huggingface.co/Mihaiii/Bucharest-0.1) 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_Mihaiii__Bucharest-0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-14T00:16:59.594031](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Bucharest-0.1/blob/main/results_2024-02-14T00-16-59.594031.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\": 0.661247276384782,\n\ \ \"acc_stderr\": 0.03141201491493503,\n \"acc_norm\": 0.6641358272243135,\n\ \ \"acc_norm_stderr\": 0.03203652707247171,\n \"mc1\": 0.3243574051407589,\n\ \ \"mc1_stderr\": 0.01638797677964794,\n \"mc2\": 0.4793790433538082,\n\ \ \"mc2_stderr\": 0.014619267505513112\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6032423208191127,\n \"acc_stderr\": 0.014296513020180642,\n\ \ \"acc_norm\": 0.6535836177474402,\n \"acc_norm_stderr\": 0.013905011180063232\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6644094801832304,\n\ \ \"acc_stderr\": 0.00471231451195098,\n \"acc_norm\": 0.854511053574985,\n\ \ \"acc_norm_stderr\": 0.003518725257365604\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810535,\n\ \ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810535\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.71,\n\ \ \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n \ \ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\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\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.55,\n \"acc_norm_stderr\": 0.05\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\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267438,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267438\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419036,\n\ \ \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419036\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4365079365079365,\n \"acc_stderr\": 0.02554284681740049,\n \"\ acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.02554284681740049\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8161290322580645,\n\ \ \"acc_stderr\": 0.02203721734026784,\n \"acc_norm\": 0.8161290322580645,\n\ \ \"acc_norm_stderr\": 0.02203721734026784\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\"\ : {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656209,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656209\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\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678185,\n\ \ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678185\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\": 0.7016806722689075,\n \"acc_stderr\": 0.02971914287634286,\n \ \ \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.02971914287634286\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8623853211009175,\n \"acc_stderr\": 0.014770105878649395,\n \"\ acc_norm\": 0.8623853211009175,\n \"acc_norm_stderr\": 0.014770105878649395\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6018518518518519,\n \"acc_stderr\": 0.033384734032074016,\n \"\ acc_norm\": 0.6018518518518519,\n \"acc_norm_stderr\": 0.033384734032074016\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8649789029535865,\n \"acc_stderr\": 0.022245776632003694,\n \ \ \"acc_norm\": 0.8649789029535865,\n \"acc_norm_stderr\": 0.022245776632003694\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7130044843049327,\n\ \ \"acc_stderr\": 0.030360379710291947,\n \"acc_norm\": 0.7130044843049327,\n\ \ \"acc_norm_stderr\": 0.030360379710291947\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\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\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.034926064766237906,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.034926064766237906\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\ \ \"acc_stderr\": 0.013625556907993466,\n \"acc_norm\": 0.8237547892720306,\n\ \ \"acc_norm_stderr\": 0.013625556907993466\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\": 0.3888268156424581,\n\ \ \"acc_stderr\": 0.016303899530796123,\n \"acc_norm\": 0.3888268156424581,\n\ \ \"acc_norm_stderr\": 0.016303899530796123\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7810457516339869,\n \"acc_stderr\": 0.02367908986180772,\n\ \ \"acc_norm\": 0.7810457516339869,\n \"acc_norm_stderr\": 0.02367908986180772\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.026003301117885142,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.026003301117885142\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.02399350170904211,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.02399350170904211\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\ : {\n \"acc\": 0.4941329856584094,\n \"acc_stderr\": 0.012769356925216526,\n\ \ \"acc_norm\": 0.4941329856584094,\n \"acc_norm_stderr\": 0.012769356925216526\n\ \ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\ : 0.75,\n \"acc_stderr\": 0.026303648393696036,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.026303648393696036\n },\n \"harness|hendrycksTest-professional_psychology|5\"\ : {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.018635594034423976,\n\ \ \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.018635594034423976\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7591836734693878,\n \"acc_stderr\": 0.02737294220178816,\n\ \ \"acc_norm\": 0.7591836734693878,\n \"acc_norm_stderr\": 0.02737294220178816\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\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3243574051407589,\n\ \ \"mc1_stderr\": 0.01638797677964794,\n \"mc2\": 0.4793790433538082,\n\ \ \"mc2_stderr\": 0.014619267505513112\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8216258879242304,\n \"acc_stderr\": 0.010759352014855922\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5708870356330553,\n \ \ \"acc_stderr\": 0.013633369425647232\n }\n}\n```" repo_url: https://huggingface.co/Mihaiii/Bucharest-0.1 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_14T00_16_59.594031 path: - '**/details_harness|arc:challenge|25_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-14T00-16-59.594031.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|gsm8k|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hellaswag|10_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-14T00-16-59.594031.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-management|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-14T00-16-59.594031.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|truthfulqa:mc|0_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-14T00-16-59.594031.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_14T00_16_59.594031 path: - '**/details_harness|winogrande|5_2024-02-14T00-16-59.594031.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-14T00-16-59.594031.parquet' - config_name: results data_files: - split: 2024_02_14T00_16_59.594031 path: - results_2024-02-14T00-16-59.594031.parquet - split: latest path: - results_2024-02-14T00-16-59.594031.parquet --- # Dataset Card for Evaluation run of Mihaiii/Bucharest-0.1  Dataset automatically created during the evaluation run of model [Mihaiii/Bucharest-0.1](https://huggingface.co/Mihaiii/Bucharest-0.1) 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_Mihaiii__Bucharest-0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-14T00:16:59.594031](https://huggingface.co/datasets/open-llm-leaderboard/details_Mihaiii__Bucharest-0.1/blob/main/results_2024-02-14T00-16-59.594031.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": 0.661247276384782, "acc_stderr": 0.03141201491493503, "acc_norm": 0.6641358272243135, "acc_norm_stderr": 0.03203652707247171, "mc1": 0.3243574051407589, "mc1_stderr": 0.01638797677964794, "mc2": 0.4793790433538082, "mc2_stderr": 0.014619267505513112 }, "harness|arc:challenge|25": { "acc": 0.6032423208191127, "acc_stderr": 0.014296513020180642, "acc_norm": 0.6535836177474402, "acc_norm_stderr": 0.013905011180063232 }, "harness|hellaswag|10": { "acc": 0.6644094801832304, "acc_stderr": 0.00471231451195098, "acc_norm": 0.854511053574985, "acc_norm_stderr": 0.003518725257365604 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7631578947368421, "acc_stderr": 0.03459777606810535, "acc_norm": 0.7631578947368421, "acc_norm_stderr": 0.03459777606810535 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "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": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "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": 0.6936416184971098, "acc_stderr": 0.03514942551267438, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267438 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419036, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419036 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4365079365079365, "acc_stderr": 0.02554284681740049, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.02554284681740049 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8161290322580645, "acc_stderr": 0.02203721734026784, "acc_norm": 0.8161290322580645, "acc_norm_stderr": 0.02203721734026784 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.806060606060606, "acc_stderr": 0.03087414513656209, "acc_norm": 0.806060606060606, "acc_norm_stderr": 0.03087414513656209 }, "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": 0.927461139896373, "acc_stderr": 0.018718998520678185, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.018718998520678185 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, 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"acc_norm_stderr": 0.033384734032074016 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8649789029535865, "acc_stderr": 0.022245776632003694, "acc_norm": 0.8649789029535865, "acc_norm_stderr": 0.022245776632003694 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7130044843049327, "acc_stderr": 0.030360379710291947, "acc_norm": 0.7130044843049327, "acc_norm_stderr": 0.030360379710291947 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, 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