davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA	--- pretty_name: Evaluation run of adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA)\ \ 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_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T16:09:42.767487](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA/blob/main/results_2024-01-27T16-09-42.767487.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\": 0.7413605787370283,\n\ \ \"acc_stderr\": 0.02895069135836259,\n \"acc_norm\": 0.7476488274301629,\n\ \ \"acc_norm_stderr\": 0.029481162291123596,\n \"mc1\": 0.40758873929008566,\n\ \ \"mc1_stderr\": 0.017201949234553107,\n \"mc2\": 0.5708422092704679,\n\ \ \"mc2_stderr\": 0.015184723749426742\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759091,\n\ \ \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.01384746051889298\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6420035849432384,\n\ \ \"acc_stderr\": 0.0047843129724954,\n \"acc_norm\": 0.8388767177853017,\n\ \ \"acc_norm_stderr\": 0.003668932629672556\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.868421052631579,\n \"acc_stderr\": 0.027508689533549915,\n\ \ \"acc_norm\": 0.868421052631579,\n \"acc_norm_stderr\": 0.027508689533549915\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7962264150943397,\n \"acc_stderr\": 0.024790784501775402,\n\ \ \"acc_norm\": 0.7962264150943397,\n \"acc_norm_stderr\": 0.024790784501775402\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.02628055093284806,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.02628055093284806\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|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-college_medicine\|5\": {\n \"acc\": 0.7167630057803468,\n\ \ \"acc_stderr\": 0.034355680560478746,\n \"acc_norm\": 0.7167630057803468,\n\ \ \"acc_norm_stderr\": 0.034355680560478746\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367406,\n\ \ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367406\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7531914893617021,\n \"acc_stderr\": 0.02818544130123409,\n\ \ \"acc_norm\": 0.7531914893617021,\n \"acc_norm_stderr\": 0.02818544130123409\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5263157894736842,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7310344827586207,\n \"acc_stderr\": 0.036951833116502325,\n\ \ \"acc_norm\": 0.7310344827586207,\n \"acc_norm_stderr\": 0.036951833116502325\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.6666666666666666,\n \"acc_stderr\": 0.0242785680243077,\n \"acc_norm\"\ : 0.6666666666666666,\n \"acc_norm_stderr\": 0.0242785680243077\n },\n\ \ \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5238095238095238,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8870967741935484,\n\ \ \"acc_stderr\": 0.01800360332586361,\n \"acc_norm\": 0.8870967741935484,\n\ \ \"acc_norm_stderr\": 0.01800360332586361\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6502463054187192,\n \"acc_stderr\": 0.03355400904969566,\n\ \ \"acc_norm\": 0.6502463054187192,\n \"acc_norm_stderr\": 0.03355400904969566\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\"\ : 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527029,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527029\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.8051282051282052,\n \"acc_stderr\": 0.020083167595181393,\n\ \ \"acc_norm\": 0.8051282051282052,\n \"acc_norm_stderr\": 0.020083167595181393\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3888888888888889,\n \"acc_stderr\": 0.029723278961476668,\n \ \ \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.029723278961476668\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8403361344537815,\n \"acc_stderr\": 0.023793353997528802,\n\ \ \"acc_norm\": 0.8403361344537815,\n \"acc_norm_stderr\": 0.023793353997528802\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.5033112582781457,\n \"acc_stderr\": 0.04082393379449654,\n \"\ acc_norm\": 0.5033112582781457,\n \"acc_norm_stderr\": 0.04082393379449654\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9119266055045872,\n \"acc_stderr\": 0.01215074371948165,\n \"\ acc_norm\": 0.9119266055045872,\n \"acc_norm_stderr\": 0.01215074371948165\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6527777777777778,\n \"acc_stderr\": 0.032468872436376486,\n \"\ acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.032468872436376486\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8970588235294118,\n \"acc_stderr\": 0.02132833757080437,\n \"\ acc_norm\": 0.8970588235294118,\n \"acc_norm_stderr\": 0.02132833757080437\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065498,\n \ \ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065498\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7713004484304933,\n\ \ \"acc_stderr\": 0.028188240046929203,\n \"acc_norm\": 0.7713004484304933,\n\ \ \"acc_norm_stderr\": 0.028188240046929203\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515375,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515375\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622804,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622804\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8796296296296297,\n\ \ \"acc_stderr\": 0.03145703854306251,\n \"acc_norm\": 0.8796296296296297,\n\ \ \"acc_norm_stderr\": 0.03145703854306251\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n\ \ \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5446428571428571,\n\ \ \"acc_stderr\": 0.04726835553719098,\n \"acc_norm\": 0.5446428571428571,\n\ \ \"acc_norm_stderr\": 0.04726835553719098\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.0339329572976101,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.0339329572976101\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.01553751426325388,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.01553751426325388\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8876117496807152,\n\ \ \"acc_stderr\": 0.011294541351216554,\n \"acc_norm\": 0.8876117496807152,\n\ \ \"acc_norm_stderr\": 0.011294541351216554\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8208092485549133,\n \"acc_stderr\": 0.020647590029679332,\n\ \ \"acc_norm\": 0.8208092485549133,\n \"acc_norm_stderr\": 0.020647590029679332\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.7150837988826816,\n\ \ \"acc_stderr\": 0.015096222302469802,\n \"acc_norm\": 0.7150837988826816,\n\ \ \"acc_norm_stderr\": 0.015096222302469802\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8464052287581699,\n \"acc_stderr\": 0.02064559791041877,\n\ \ \"acc_norm\": 0.8464052287581699,\n \"acc_norm_stderr\": 0.02064559791041877\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\": 0.8148148148148148,\n \"acc_stderr\": 0.021613809395224812,\n\ \ \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.021613809395224812\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6170212765957447,\n \"acc_stderr\": 0.028999080904806185,\n \ \ \"acc_norm\": 0.6170212765957447,\n \"acc_norm_stderr\": 0.028999080904806185\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5827900912646675,\n\ \ \"acc_stderr\": 0.012593959992906427,\n \"acc_norm\": 0.5827900912646675,\n\ \ \"acc_norm_stderr\": 0.012593959992906427\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.023709788253811766,\n \ \ \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.023709788253811766\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8088235294117647,\n \"acc_stderr\": 0.015908290136278043,\n \ \ \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.015908290136278043\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\": 0.8285714285714286,\n \"acc_stderr\": 0.02412746346265016,\n\ \ \"acc_norm\": 0.8285714285714286,\n \"acc_norm_stderr\": 0.02412746346265016\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.9104477611940298,\n\ \ \"acc_stderr\": 0.02019067053502792,\n \"acc_norm\": 0.9104477611940298,\n\ \ \"acc_norm_stderr\": 0.02019067053502792\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.0261682213446623,\n\ \ \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.0261682213446623\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.40758873929008566,\n\ \ \"mc1_stderr\": 0.017201949234553107,\n \"mc2\": 0.5708422092704679,\n\ \ \"mc2_stderr\": 0.015184723749426742\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.011508957690722764\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5549658832448825,\n \ \ \"acc_stderr\": 0.0136890115674142\n }\n}\n```" repo_url: https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA 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_27T16_09_42.767487 path: - '/details_harness\|arc:challenge\|25_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-27T16-09-42.767487.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|gsm8k\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hellaswag\|10_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T16-09-42.767487.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T16-09-42.767487.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '/details_harness\|winogrande\|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-27T16-09-42.767487.parquet' - config_name: results data_files: - split: 2024_01_27T16_09_42.767487 path: - results_2024-01-27T16-09-42.767487.parquet - split: latest path: - results_2024-01-27T16-09-42.767487.parquet --- # Dataset Card for Evaluation run of adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA) 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_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T16:09:42.767487](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA/blob/main/results_2024-01-27T16-09-42.767487.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": 0.7413605787370283, "acc_stderr": 0.02895069135836259, "acc_norm": 0.7476488274301629, "acc_norm_stderr": 0.029481162291123596, "mc1": 0.40758873929008566, "mc1_stderr": 0.017201949234553107, "mc2": 0.5708422092704679, "mc2_stderr": 0.015184723749426742 }, "harness\|arc:challenge\|25": { "acc": 0.6262798634812287, "acc_stderr": 0.014137708601759091, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.01384746051889298 }, "harness\|hellaswag\|10": { "acc": 0.6420035849432384, "acc_stderr": 0.0047843129724954, "acc_norm": 0.8388767177853017, "acc_norm_stderr": 0.003668932629672556 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7037037037037037, "acc_stderr": 0.03944624162501116, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.03944624162501116 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.868421052631579, "acc_stderr": 0.027508689533549915, "acc_norm": 0.868421052631579, "acc_norm_stderr": 0.027508689533549915 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7962264150943397, "acc_stderr": 0.024790784501775402, "acc_norm": 0.7962264150943397, "acc_norm_stderr": 0.024790784501775402 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02628055093284806, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02628055093284806 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.034355680560478746, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.034355680560478746 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5196078431372549, "acc_stderr": 0.04971358884367406, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.04971358884367406 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7531914893617021, "acc_stderr": 0.02818544130123409, "acc_norm": 0.7531914893617021, "acc_norm_stderr": 0.02818544130123409 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7310344827586207, "acc_stderr": 0.036951833116502325, "acc_norm": 0.7310344827586207, "acc_norm_stderr": 0.036951833116502325 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.0242785680243077, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.0242785680243077 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5238095238095238, "acc_stderr": 0.04467062628403273, "acc_norm": 0.5238095238095238, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8870967741935484, "acc_stderr": 0.01800360332586361, "acc_norm": 0.8870967741935484, "acc_norm_stderr": 0.01800360332586361 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6502463054187192, "acc_stderr": 0.03355400904969566, "acc_norm": 0.6502463054187192, "acc_norm_stderr": 0.03355400904969566 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527029, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527029 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8051282051282052, "acc_stderr": 0.020083167595181393, "acc_norm": 0.8051282051282052, "acc_norm_stderr": 0.020083167595181393 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.029723278961476668, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.029723278961476668 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8403361344537815, "acc_stderr": 0.023793353997528802, "acc_norm": 0.8403361344537815, "acc_norm_stderr": 0.023793353997528802 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.5033112582781457, "acc_stderr": 0.04082393379449654, "acc_norm": 0.5033112582781457, "acc_norm_stderr": 0.04082393379449654 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9119266055045872, "acc_stderr": 0.01215074371948165, "acc_norm": 0.9119266055045872, "acc_norm_stderr": 0.01215074371948165 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6527777777777778, "acc_stderr": 0.032468872436376486, "acc_norm": 0.6527777777777778, "acc_norm_stderr": 0.032468872436376486 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8970588235294118, "acc_stderr": 0.02132833757080437, "acc_norm": 0.8970588235294118, "acc_norm_stderr": 0.02132833757080437 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8987341772151899, "acc_stderr": 0.019637720526065498, "acc_norm": 0.8987341772151899, "acc_norm_stderr": 0.019637720526065498 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7713004484304933, "acc_stderr": 0.028188240046929203, "acc_norm": 0.7713004484304933, "acc_norm_stderr": 0.028188240046929203 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8549618320610687, "acc_stderr": 0.030884661089515375, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.030884661089515375 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8842975206611571, "acc_stderr": 0.029199802455622804, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622804 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8796296296296297, "acc_stderr": 0.03145703854306251, "acc_norm": 0.8796296296296297, "acc_norm_stderr": 0.03145703854306251 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8588957055214724, "acc_stderr": 0.027351605518389752, "acc_norm": 0.8588957055214724, "acc_norm_stderr": 0.027351605518389752 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5446428571428571, "acc_stderr": 0.04726835553719098, "acc_norm": 0.5446428571428571, "acc_norm_stderr": 0.04726835553719098 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8640776699029126, "acc_stderr": 0.0339329572976101, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.0339329572976101 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9401709401709402, "acc_stderr": 0.01553751426325388, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.01553751426325388 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8876117496807152, "acc_stderr": 0.011294541351216554, "acc_norm": 0.8876117496807152, "acc_norm_stderr": 0.011294541351216554 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8208092485549133, "acc_stderr": 0.020647590029679332, "acc_norm": 0.8208092485549133, "acc_norm_stderr": 0.020647590029679332 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7150837988826816, "acc_stderr": 0.015096222302469802, "acc_norm": 0.7150837988826816, "acc_norm_stderr": 0.015096222302469802 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8464052287581699, "acc_stderr": 0.02064559791041877, "acc_norm": 0.8464052287581699, "acc_norm_stderr": 0.02064559791041877 }, "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": 0.8148148148148148, "acc_stderr": 0.021613809395224812, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.021613809395224812 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6170212765957447, "acc_stderr": 0.028999080904806185, "acc_norm": 0.6170212765957447, "acc_norm_stderr": 0.028999080904806185 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5827900912646675, "acc_stderr": 0.012593959992906427, "acc_norm": 0.5827900912646675, "acc_norm_stderr": 0.012593959992906427 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8125, "acc_stderr": 0.023709788253811766, "acc_norm": 0.8125, "acc_norm_stderr": 0.023709788253811766 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8088235294117647, "acc_stderr": 0.015908290136278043, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.015908290136278043 }, "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": 0.8285714285714286, "acc_stderr": 0.02412746346265016, "acc_norm": 0.8285714285714286, "acc_norm_stderr": 0.02412746346265016 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.9104477611940298, "acc_stderr": 0.02019067053502792, "acc_norm": 0.9104477611940298, "acc_norm_stderr": 0.02019067053502792 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8654970760233918, "acc_stderr": 0.0261682213446623, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.0261682213446623 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.40758873929008566, "mc1_stderr": 0.017201949234553107, "mc2": 0.5708422092704679, "mc2_stderr": 0.015184723749426742 }, "harness\|winogrande\|5": { "acc": 0.7868981846882399, "acc_stderr": 0.011508957690722764 }, "harness\|gsm8k\|5": { "acc": 0.5549658832448825, "acc_stderr": 0.0136890115674142 } } ``` ## 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]
bubl-ai/story_with_synthetic_test_set	--- license: mit ---
freshpearYoon/val_free_6	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 8745352416 num_examples: 9105 download_size: 1361253032 dataset_size: 8745352416 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/k3_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of k3/K3/K3 (Girls' Frontline) This is the dataset of k3/K3/K3 (Girls' Frontline), containing 24 images and their tags. The core tags of this character are `breasts, hairband, large_breasts, grey_hair, long_hair, grey_eyes, headband, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:----------\|:-------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 24 \| 24.80 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 24 \| 14.14 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 53 \| 29.25 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 24 \| 21.82 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 53 \| 41.24 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/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/k3_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 24 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, open_mouth, cleavage, collarbone, blush, looking_at_viewer, navel, sports_bra, simple_background, smile, jacket, sweat, pants, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| open_mouth \| cleavage \| collarbone \| blush \| looking_at_viewer \| navel \| sports_bra \| simple_background \| smile \| jacket \| sweat \| pants \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:-------------\|:-----------\|:-------------\|:--------\|:--------------------\|:--------\|:-------------\|:--------------------\|:--------\|:---------\|:--------\|:--------\|:-------------------\| \| 0 \| 24 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
result-kand2-sdxl-wuerst-karlo/86947388	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1325 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "86947388" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
huggingface/autotrain-data-4v9x-3cwh-jsa8	Invalid username or password.
autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-3c512f6e-12265640	--- type: predictions tags: - autotrain - evaluation datasets: - Blaise-g/SumPubmed eval_info: task: summarization model: Blaise-g/led_large_baseline_pubmed metrics: ['bertscore'] dataset_name: Blaise-g/SumPubmed dataset_config: Blaise-g--SumPubmed dataset_split: test col_mapping: text: text target: abstract --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: Blaise-g/led_large_baseline_pubmed * Dataset: Blaise-g/SumPubmed * Config: Blaise-g--SumPubmed * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model.
temiy/cat-simple	--- license: apache-2.0 ---
yuejunasia/haaudio	--- license: other --- 这是一个ha测试数据集!
bigbio/psytar	--- language: - en bigbio_language: - English license: cc-by-4.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_4p0 pretty_name: PsyTAR homepage: https://www.askapatient.com/research/pharmacovigilance/corpus-ades-psychiatric-medications.asp bigbio_pubmed: False bigbio_public: False bigbio_tasks: - NAMED_ENTITY_RECOGNITION - TEXT_CLASSIFICATION --- # Dataset Card for PsyTAR ## Dataset Description - Homepage: https://www.askapatient.com/research/pharmacovigilance/corpus-ades-psychiatric-medications.asp - Pubmed: False - Public: False - Tasks: NER,TXTCLASS The "Psychiatric Treatment Adverse Reactions" (PsyTAR) dataset contains 891 drugs reviews posted by patients on "askapatient.com", about the effectiveness and adverse drug events associated with Zoloft, Lexapro, Cymbalta, and Effexor XR. This dataset can be used for (multi-label) sentence classification of Adverse Drug Reaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug Indications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF) and Others, as well as for recognition of 5 different types of named entity (in the categories ADRs, WDs, SSIs and DIs) ## Citation Information ``` @article{Zolnoori2019, author = {Maryam Zolnoori and Kin Wah Fung and Timothy B. Patrick and Paul Fontelo and Hadi Kharrazi and Anthony Faiola and Yi Shuan Shirley Wu and Christina E. Eldredge and Jake Luo and Mike Conway and Jiaxi Zhu and Soo Kyung Park and Kelly Xu and Hamideh Moayyed and Somaieh Goudarzvand}, title = {A systematic approach for developing a corpus of patient reported adverse drug events: A case study for {SSRI} and {SNRI} medications}, journal = {Journal of Biomedical Informatics}, volume = {90}, year = {2019}, url = {https://doi.org/10.1016/j.jbi.2018.12.005}, doi = {10.1016/j.jbi.2018.12.005}, } ```
DeepFoldProtein/Foldseek_over70_proteome_UniDoc_test	--- dataset_info: features: - name: uniprotAccession dtype: string - name: domain sequence: sequence: int64 - name: ndom dtype: int64 - name: taxId dtype: string - name: uniprotSequence dtype: string splits: - name: train num_bytes: 4002 num_examples: 12 download_size: 7272 dataset_size: 4002 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvarochelo/dataset_nautical	--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 908645884.0 num_examples: 239 download_size: 875628182 dataset_size: 908645884.0 --- # Dataset Card for "dataset_nautical" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/find_first_sent_train_50_eval_10_recite	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 170837 num_examples: 110 - name: validation num_bytes: 15661 num_examples: 10 download_size: 0 dataset_size: 186498 --- # Dataset Card for "find_first_sent_train_50_eval_10_recite" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nlp-brin-id/pos_pairs	--- license: apache-2.0 ---
wwydmanski/metagenomic_curated	--- license: artistic-2.0 --- # Metagenomic curated data This is a python repack of the curated data from the [Metagenomic Data Repository](https://waldronlab.io/curatedMetagenomicData/). Please refer to the [study list](https://experimenthub.bioconductor.org/package/curatedMetagenomicData) and [study metadata](https://waldronlab.io/curatedMetagenomicData/articles/available-studies.html) for the list of available datasets. ## Sample usage ```python ds = datasets.load_dataset("wwydmanski/metagenomic_curated", "EH1726") X = np.array([list(i.values()) for i in ds['train']['features']]) y = np.array([x['study_condition'] for x in ds['train']['metadata']]) ``` ## Finding a relevant dataset EHID The easiest way to find an interesting study is via [study metadata](https://waldronlab.io/curatedMetagenomicData/articles/available-studies.html). After that, you can find corresponding EHIDs by referring on the https://experimenthub.bioconductor.org/title/{study_name} page. Let's say that the ThomasAM_2018a study piqued your curiosity - it means that you will be able to find all relevant datasets on the [https://experimenthub.bioconductor.org/title/ThomasAM_2018a](https://experimenthub.bioconductor.org/title/ThomasAM_2018a) website.
AdapterOcean/gptindex-standardized_cluster_0	--- 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: 10931345 num_examples: 1234 download_size: 2937820 dataset_size: 10931345 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gptindex-standardized_cluster_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lipi17/building-cracks	--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface --- <div align="center"> <img width="640" alt="lipi17/building-cracks" src="https://huggingface.co/datasets/lipi17/building-cracks/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['crack'] ``` ### Number of Images ```json {'valid': 433, 'test': 211, 'train': 1490} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("lipi17/building-cracks", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg/dataset/2](https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg/dataset/2?ref=roboflow2huggingface) ### Citation ``` @misc{ crack-detection-y5kyg_dataset, title = { Crack Detection Dataset }, type = { Open Source Dataset }, author = { António Raimundo }, howpublished = { \\url{ https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg } }, url = { https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { feb }, note = { visited on 2023-10-21 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on February 10, 2023 at 3:51 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com The dataset includes 2134 images. Soil are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) No image augmentation techniques were applied.
vmathur87/llm-support	--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 717288.0 num_examples: 242 - name: test num_bytes: 59280.0 num_examples: 20 download_size: 338197 dataset_size: 776568.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Shularp/Process_tested-Shularp-Process_tested-facebook-floresarb_Arab_to_eng_Latn	--- dataset_info: features: - name: translation struct: - name: ar dtype: string - name: en dtype: string - name: id sequence: int64 splits: - name: train num_bytes: 361758 num_examples: 997 - name: test num_bytes: 379791 num_examples: 1012 download_size: 412821 dataset_size: 741549 --- # Dataset Card for "Process_tested-Shularp-Process_tested-facebook-floresarb_Arab_to_eng_Latn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
atiranela/rodrigo	--- license: openrail ---
chishiki-ai/cnn-course	--- license: cc-by-4.0 ---
Lhaippp/DMHomo	--- license: mit ---
open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG	--- pretty_name: Evaluation run of LTC-AI-Labs/L2-7b-Base-test-WVG dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LTC-AI-Labs/L2-7b-Base-test-WVG](https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG)\ \ 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_LTC-AI-Labs__L2-7b-Base-test-WVG\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T07:52:27.189086](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG/blob/main/results_2023-10-28T07-52-27.189086.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\": 0.003145973154362416,\n\ \ \"em_stderr\": 0.0005734993648436373,\n \"f1\": 0.07481229026845672,\n\ \ \"f1_stderr\": 0.0016422896702234556,\n \"acc\": 0.40267285313968093,\n\ \ \"acc_stderr\": 0.00970555723399882\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436373,\n\ \ \"f1\": 0.07481229026845672,\n \"f1_stderr\": 0.0016422896702234556\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.06974981046247157,\n \ \ \"acc_stderr\": 0.007016389571013843\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7355958958168903,\n \"acc_stderr\": 0.012394724896983799\n\ \ }\n}\n```" repo_url: https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG 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_03T17_19_09.186622 path: - '/details_harness\|arc:challenge\|25_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-10-03T17-19-09.186622.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T06_36_16.797528 path: - '/details_harness\|drop\|3_2023-10-28T06-36-16.797528.parquet' - split: 2023_10_28T07_52_27.189086 path: - '/details_harness\|drop\|3_2023-10-28T07-52-27.189086.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-28T07-52-27.189086.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T06_36_16.797528 path: - '/details_harness\|gsm8k\|5_2023-10-28T06-36-16.797528.parquet' - split: 2023_10_28T07_52_27.189086 path: - '/details_harness\|gsm8k\|5_2023-10-28T07-52-27.189086.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-28T07-52-27.189086.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hellaswag\|10_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T17-19-09.186622.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-management\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_03T17_19_09.186622 path: - '/details_harness\|truthfulqa:mc\|0_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-10-03T17-19-09.186622.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T06_36_16.797528 path: - '/details_harness\|winogrande\|5_2023-10-28T06-36-16.797528.parquet' - split: 2023_10_28T07_52_27.189086 path: - '/details_harness\|winogrande\|5_2023-10-28T07-52-27.189086.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-28T07-52-27.189086.parquet' - config_name: results data_files: - split: 2023_10_03T17_19_09.186622 path: - results_2023-10-03T17-19-09.186622.parquet - split: 2023_10_28T06_36_16.797528 path: - results_2023-10-28T06-36-16.797528.parquet - split: 2023_10_28T07_52_27.189086 path: - results_2023-10-28T07-52-27.189086.parquet - split: latest path: - results_2023-10-28T07-52-27.189086.parquet --- # Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Base-test-WVG ## Dataset Description - Homepage: - Repository: https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG - 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 [LTC-AI-Labs/L2-7b-Base-test-WVG](https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG) 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_LTC-AI-Labs__L2-7b-Base-test-WVG", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T07:52:27.189086](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG/blob/main/results_2023-10-28T07-52-27.189086.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": 0.003145973154362416, "em_stderr": 0.0005734993648436373, "f1": 0.07481229026845672, "f1_stderr": 0.0016422896702234556, "acc": 0.40267285313968093, "acc_stderr": 0.00970555723399882 }, "harness\|drop\|3": { "em": 0.003145973154362416, "em_stderr": 0.0005734993648436373, "f1": 0.07481229026845672, "f1_stderr": 0.0016422896702234556 }, "harness\|gsm8k\|5": { "acc": 0.06974981046247157, "acc_stderr": 0.007016389571013843 }, "harness\|winogrande\|5": { "acc": 0.7355958958168903, "acc_stderr": 0.012394724896983799 } } ``` ### 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]
kpriyanshu256/MultiTabQA-multitable_pretraining-valid-v2	--- dataset_info: features: - name: query dtype: string - name: table_names sequence: string - name: tables sequence: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 853236383 num_examples: 536 download_size: 207209865 dataset_size: 853236383 configs: - config_name: default data_files: - split: train path: data/train-* ---
w95/fin	--- configs: - config_name: default data_files: - split: train path: train.jsonl dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string ---
Deojoandco/capstone_fromgpt_without_gold	--- dataset_info: features: - name: dialog_id dtype: int64 - name: dialogue dtype: string - name: summary dtype: string - name: gold_tags dtype: string - name: query dtype: string - name: gpt_success dtype: bool - name: gpt_response dtype: string - name: gold_tags_tokens_count dtype: int64 - name: GPT_OUTPUT_FOUND dtype: bool - name: gpt_output_tags dtype: string - name: gpt_output_tag_tokens dtype: int64 - name: summary_gpt_token_count_match dtype: bool - name: gpt_output_token_count dtype: int64 - name: gpt_output_tag_count dtype: int64 - name: summary_gpt_tags_token_count_match dtype: bool splits: - name: test num_bytes: 57588 num_examples: 12 download_size: 30674 dataset_size: 57588 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "capstone_fromgpt_without_gold" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Abhinav-B/finetune_llama_wikisql	--- dataset_info: features: - name: formatted_text dtype: string splits: - name: train num_bytes: 1530908 num_examples: 10000 download_size: 703398 dataset_size: 1530908 configs: - config_name: default data_files: - split: train path: data/train-* ---
dexhrestha/annomi-sample	--- dataset_info: features: - name: text dtype: string - name: len dtype: int64 splits: - name: train num_bytes: 734843.2554793004 num_examples: 7461 - name: validation num_bytes: 81747.74186406907 num_examples: 830 download_size: 611225 dataset_size: 816590.9973433695 --- # Dataset Card for "annomi-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sjonas50/test	--- license: creativeml-openrail-m ---
Ironov/abstract	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1245046.0 num_examples: 409 download_size: 1254490 dataset_size: 1245046.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Rowan/hellaswag	--- language: - en paperswithcode_id: hellaswag pretty_name: HellaSwag dataset_info: features: - name: ind dtype: int32 - name: activity_label dtype: string - name: ctx_a dtype: string - name: ctx_b dtype: string - name: ctx dtype: string - name: endings sequence: string - name: source_id dtype: string - name: split dtype: string - name: split_type dtype: string - name: label dtype: string splits: - name: train num_bytes: 43232624 num_examples: 39905 - name: test num_bytes: 10791853 num_examples: 10003 - name: validation num_bytes: 11175717 num_examples: 10042 download_size: 71494896 dataset_size: 65200194 --- # Dataset Card for "hellaswag" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [https://rowanzellers.com/hellaswag/](https://rowanzellers.com/hellaswag/) - Repository: [https://github.com/rowanz/hellaswag/](https://github.com/rowanz/hellaswag/) - Paper: [HellaSwag: Can a Machine Really Finish Your Sentence?](https://arxiv.org/abs/1905.07830) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of downloaded dataset files: 71.49 MB - Size of the generated dataset: 65.32 MB - Total amount of disk used: 136.81 MB ### Dataset Summary HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - Size of downloaded dataset files: 71.49 MB - Size of the generated dataset: 65.32 MB - Total amount of disk used: 136.81 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "activity_label": "Removing ice from car", "ctx": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles. then", "ctx_a": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles.", "ctx_b": "then", "endings": "[\", the man adds wax to the windshield and cuts it.\", \", a person board a ski lift, while two men supporting the head of the per...", "ind": 4, "label": "3", "source_id": "activitynet~v_-1IBHYS3L-Y", "split": "train", "split_type": "indomain" } ``` ### Data Fields The data fields are the same among all splits. #### default - `ind`: a `int32` feature. - `activity_label`: a `string` feature. - `ctx_a`: a `string` feature. - `ctx_b`: a `string` feature. - `ctx`: a `string` feature. - `endings`: a `list` of `string` features. - `source_id`: a `string` feature. - `split`: a `string` feature. - `split_type`: a `string` feature. - `label`: a `string` feature. ### Data Splits \| name \|train\|validation\|test \| \|-------\|----:\|---------:\|----:\| \|default\|39905\| 10042\|10003\| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information MIT https://github.com/rowanz/hellaswag/blob/master/LICENSE ### Citation Information ``` @inproceedings{zellers2019hellaswag, title={HellaSwag: Can a Machine Really Finish Your Sentence?}, author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin}, booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
ag2428/reasoningDataV4	--- dataset_info: features: - name: instruction dtype: string - name: answer dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 2481669221 num_examples: 2062854 download_size: 1500063761 dataset_size: 2481669221 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "reasoningDataV4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
paoloitaliani/news_articles	--- dataset_info: - config_name: corriere_autunno features: - name: author dtype: string - name: journal dtype: string - name: body dtype: string - name: date dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 339578 num_examples: 90 download_size: 237083 dataset_size: 339578 - config_name: corriere_primavera features: - name: author dtype: string - name: journal dtype: string - name: body dtype: string - name: date dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 319422 num_examples: 105 download_size: 206264 dataset_size: 319422 - config_name: fattoq_autunno features: - name: author dtype: string - name: journal dtype: string - name: body dtype: string - name: date dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 519012 num_examples: 133 download_size: 338948 dataset_size: 519012 - config_name: fattoq_primavera features: - name: author dtype: string - name: journal dtype: string - name: body dtype: string - name: date dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 508621 num_examples: 152 download_size: 331977 dataset_size: 508621 - config_name: ukraine features: - name: date dtype: timestamp[ns] - name: body dtype: string - name: author dtype: string - name: journal dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 81923456 num_examples: 27449 download_size: 0 dataset_size: 81923456 configs: - config_name: corriere_autunno data_files: - split: train path: corriere_autunno/train-* - config_name: corriere_primavera data_files: - split: train path: corriere_primavera/train-* - config_name: fattoq_autunno data_files: - split: train path: fattoq_autunno/train-* - config_name: fattoq_primavera data_files: - split: train path: fattoq_primavera/train-* - config_name: ukraine data_files: - split: train path: ukraine/train-* --- # Dataset Card for "news_articles" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nirantk/french-books	--- dataset_info: features: - name: file_id dtype: string - name: ocr dtype: string - name: title dtype: string - name: date dtype: string - name: author dtype: string - name: page_count dtype: int64 - name: word_count dtype: int64 - name: character_count dtype: int64 - name: complete_text dtype: string splits: - name: train num_bytes: 427823810 num_examples: 1101 download_size: 246984465 dataset_size: 427823810 configs: - config_name: default data_files: - split: train path: data/train-* ---
nlpai-lab/kullm-v2	--- license: apache-2.0 task_categories: - text-generation language: - ko pretty_name: kullm size_categories: - 10K<n<100K --- # Dataset Card for "KULLM-v2" ## Dataset Summary Korean translation of GPT4ALL, Dolly, and Vicuna data. repository: [nlpai-lab/KULLM](https://github.com/nlpai-lab/KULLM) huggingface: [nlpai-lab/kullm-v2](https://huggingface.co/nlpai-lab/kullm-polyglot-12.8b-v2) #### Translate dataset Translated 'instruction', 'input', and 'output' in the dataset via the DeepL API ## Lisence Apache-2.0 ```python >>> from datasets import load_dataset >>> ds = load_dataset("nlpai-lab/kullm-v2", split="train") >>> ds DatasetDict({ train: Dataset({ features: ['id', 'instruction', 'input', 'output'], num_rows: 152630 }) }) ``` ```python >>> ds[0] {'id': 'alpaca_{idx}', 'instruction': '3원색이란 무엇인가요?', 'input': '', 'output': '세 가지 기본 색은 빨강, 파랑, 노랑입니다. 이 색은 다른 색을 혼합하여 만들 수 없고 다른 모든 색은 다양한 비율로 조합하여 만들 수 있기 때문에 원색이라고 부릅니다. 빛에 사용되는 첨가제 색상 시스템에서 원색은 빨강, 녹색, 파랑(RGB)입니다.'} ```
open-llm-leaderboard/details_UCLA-AGI__test_final	--- pretty_name: Evaluation run of UCLA-AGI/test_final dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [UCLA-AGI/test_final](https://huggingface.co/UCLA-AGI/test_final) 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_UCLA-AGI__test_final\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-13T15:52:58.260309](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__test_final/blob/main/results_2024-01-13T15-52-58.260309.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\": 0.6144035496773548,\n\ \ \"acc_stderr\": 0.032858739117399755,\n \"acc_norm\": 0.6200519616024565,\n\ \ \"acc_norm_stderr\": 0.03352475225298005,\n \"mc1\": 0.4222766217870257,\n\ \ \"mc1_stderr\": 0.017290733254248174,\n \"mc2\": 0.5789464689775264,\n\ \ \"mc2_stderr\": 0.015807009741465705\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6305460750853242,\n \"acc_stderr\": 0.014104578366491887,\n\ \ \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.01383056892797433\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.676458872734515,\n\ \ \"acc_stderr\": 0.0046687106891924,\n \"acc_norm\": 0.8584943238398726,\n\ \ \"acc_norm_stderr\": 0.0034783009945146973\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\": 0.6447368421052632,\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\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.028815615713432115,\n\ \ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.028815615713432115\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\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\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\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\": 0.6184971098265896,\n \"acc_stderr\": 0.03703851193099521,\n\ \ \"acc_norm\": 0.6184971098265896,\n \"acc_norm_stderr\": 0.03703851193099521\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.4117647058823529,\n\ \ \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n\ \ \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\":\ \ 0.5446808510638298,\n \"acc_stderr\": 0.03255525359340355,\n \"\ acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.03255525359340355\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\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.04343525428949098,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.04343525428949098\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\"\ : 0.7322580645161291,\n \"acc_stderr\": 0.02518900666021238,\n \"\ acc_norm\": 0.7322580645161291,\n \"acc_norm_stderr\": 0.02518900666021238\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\ acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338642,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338642\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709447,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709447\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647074,\n\ \ \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647074\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121622,\n\ \ \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121622\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7944954128440367,\n \"acc_stderr\": 0.01732435232501601,\n \"\ acc_norm\": 0.7944954128440367,\n \"acc_norm_stderr\": 0.01732435232501601\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252336,\n \"\ acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252336\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588663,\n \"\ acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588663\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808514,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808514\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.04058042015646034,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.04058042015646034\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8461538461538461,\n\ \ \"acc_stderr\": 0.023636873317489284,\n \"acc_norm\": 0.8461538461538461,\n\ \ \"acc_norm_stderr\": 0.023636873317489284\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.8084291187739464,\n\ \ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\ \ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6965317919075145,\n \"acc_stderr\": 0.024752411960917212,\n\ \ \"acc_norm\": 0.6965317919075145,\n \"acc_norm_stderr\": 0.024752411960917212\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3664804469273743,\n\ \ \"acc_stderr\": 0.016115235504865464,\n \"acc_norm\": 0.3664804469273743,\n\ \ \"acc_norm_stderr\": 0.016115235504865464\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6895424836601307,\n \"acc_stderr\": 0.026493033225145898,\n\ \ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.026493033225145898\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6816720257234726,\n\ \ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n\ \ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6512345679012346,\n \"acc_stderr\": 0.02651759772446501,\n\ \ \"acc_norm\": 0.6512345679012346,\n \"acc_norm_stderr\": 0.02651759772446501\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303055,\n \ \ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303055\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.44589308996088656,\n\ \ \"acc_stderr\": 0.012695244711379778,\n \"acc_norm\": 0.44589308996088656,\n\ \ \"acc_norm_stderr\": 0.012695244711379778\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.619281045751634,\n \"acc_stderr\": 0.0196438015579248,\n \ \ \"acc_norm\": 0.619281045751634,\n \"acc_norm_stderr\": 0.0196438015579248\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6653061224489796,\n \"acc_stderr\": 0.030209235226242307,\n\ \ \"acc_norm\": 0.6653061224489796,\n \"acc_norm_stderr\": 0.030209235226242307\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786845,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786845\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4222766217870257,\n\ \ \"mc1_stderr\": 0.017290733254248174,\n \"mc2\": 0.5789464689775264,\n\ \ \"mc2_stderr\": 0.015807009741465705\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7663772691397001,\n \"acc_stderr\": 0.011892194477183525\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3419257012888552,\n \ \ \"acc_stderr\": 0.0130660896251828\n }\n}\n```" repo_url: https://huggingface.co/UCLA-AGI/test_final 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_13T15_52_58.260309 path: - '/details_harness\|arc:challenge\|25_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-13T15-52-58.260309.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|gsm8k\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hellaswag\|10_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T15-52-58.260309.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-13T15-52-58.260309.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '/details_harness\|winogrande\|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-13T15-52-58.260309.parquet' - config_name: results data_files: - split: 2024_01_13T15_52_58.260309 path: - results_2024-01-13T15-52-58.260309.parquet - split: latest path: - results_2024-01-13T15-52-58.260309.parquet --- # Dataset Card for Evaluation run of UCLA-AGI/test_final <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [UCLA-AGI/test_final](https://huggingface.co/UCLA-AGI/test_final) 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_UCLA-AGI__test_final", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T15:52:58.260309](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__test_final/blob/main/results_2024-01-13T15-52-58.260309.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": 0.6144035496773548, "acc_stderr": 0.032858739117399755, "acc_norm": 0.6200519616024565, "acc_norm_stderr": 0.03352475225298005, "mc1": 0.4222766217870257, "mc1_stderr": 0.017290733254248174, "mc2": 0.5789464689775264, "mc2_stderr": 0.015807009741465705 }, "harness\|arc:challenge\|25": { "acc": 0.6305460750853242, "acc_stderr": 0.014104578366491887, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.01383056892797433 }, "harness\|hellaswag\|10": { "acc": 0.676458872734515, "acc_stderr": 0.0046687106891924, "acc_norm": 0.8584943238398726, "acc_norm_stderr": 0.0034783009945146973 }, "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": 0.6447368421052632, "acc_stderr": 0.03894734487013317, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013317 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6754716981132075, "acc_stderr": 0.028815615713432115, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.028815615713432115 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "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": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "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": 0.6184971098265896, "acc_stderr": 0.03703851193099521, "acc_norm": 0.6184971098265896, "acc_norm_stderr": 0.03703851193099521 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5446808510638298, "acc_stderr": 0.03255525359340355, "acc_norm": 0.5446808510638298, "acc_norm_stderr": 0.03255525359340355 }, "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": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.38095238095238093, "acc_stderr": 0.04343525428949098, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.04343525428949098 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7322580645161291, "acc_stderr": 0.02518900666021238, "acc_norm": 0.7322580645161291, "acc_norm_stderr": 0.02518900666021238 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7727272727272727, "acc_stderr": 0.02985751567338642, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.02985751567338642 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709447, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709447 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6102564102564103, "acc_stderr": 0.024726967886647074, "acc_norm": 0.6102564102564103, "acc_norm_stderr": 0.024726967886647074 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948485, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948485 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6554621848739496, "acc_stderr": 0.030868682604121622, "acc_norm": 0.6554621848739496, "acc_norm_stderr": 0.030868682604121622 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7944954128440367, "acc_stderr": 0.01732435232501601, "acc_norm": 0.7944954128440367, "acc_norm_stderr": 0.01732435232501601 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4305555555555556, "acc_stderr": 0.03376922151252336, "acc_norm": 0.4305555555555556, "acc_norm_stderr": 0.03376922151252336 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588663, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588663 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7679324894514767, "acc_stderr": 0.027479744550808514, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.027479744550808514 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.672645739910314, "acc_stderr": 0.031493846709941306, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.031493846709941306 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094633, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094633 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7484662576687117, "acc_stderr": 0.03408997886857529, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.03408997886857529 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.04058042015646034, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.04058042015646034 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8461538461538461, "acc_stderr": 0.023636873317489284, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.023636873317489284 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8084291187739464, "acc_stderr": 0.014072859310451949, "acc_norm": 0.8084291187739464, "acc_norm_stderr": 0.014072859310451949 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6965317919075145, "acc_stderr": 0.024752411960917212, "acc_norm": 0.6965317919075145, "acc_norm_stderr": 0.024752411960917212 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3664804469273743, "acc_stderr": 0.016115235504865464, "acc_norm": 0.3664804469273743, "acc_norm_stderr": 0.016115235504865464 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6895424836601307, "acc_stderr": 0.026493033225145898, "acc_norm": 0.6895424836601307, "acc_norm_stderr": 0.026493033225145898 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811025, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811025 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6512345679012346, "acc_stderr": 0.02651759772446501, "acc_norm": 0.6512345679012346, "acc_norm_stderr": 0.02651759772446501 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4716312056737589, "acc_stderr": 0.029779450957303055, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.029779450957303055 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.44589308996088656, "acc_stderr": 0.012695244711379778, "acc_norm": 0.44589308996088656, "acc_norm_stderr": 0.012695244711379778 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.028418208619406755, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.028418208619406755 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.619281045751634, "acc_stderr": 0.0196438015579248, "acc_norm": 0.619281045751634, "acc_norm_stderr": 0.0196438015579248 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6653061224489796, "acc_stderr": 0.030209235226242307, "acc_norm": 0.6653061224489796, "acc_norm_stderr": 0.030209235226242307 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786845, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786845 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4222766217870257, "mc1_stderr": 0.017290733254248174, "mc2": 0.5789464689775264, "mc2_stderr": 0.015807009741465705 }, "harness\|winogrande\|5": { "acc": 0.7663772691397001, "acc_stderr": 0.011892194477183525 }, "harness\|gsm8k\|5": { "acc": 0.3419257012888552, "acc_stderr": 0.0130660896251828 } } ``` ## 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]
DarthReca/quakeset	--- license: openrail task_categories: - image-classification - image-segmentation tags: - climate pretty_name: QuakeSet size_categories: - 1K<n<10K --- # Dataset Card for QuakeSet QuakeSet is a dataset to analyze different attributes of earthquakes. It contains bi-temporal time series of images and ground truth annotations for magnitudes, hypocenters, and affected areas. - PrePrint: https://arxiv.org/abs/2403.18116 ## Dataset Details ### Dataset Description The images are taken from the Sentinel-1 mission using the Interferometric Wide swath mode. The International Seismological Centre provides information about earthquakes. - License: OPENRAIL ## Dataset Structure The dataset is divided into three folds with equal distribution of magnitudes and balanced in positive and negative examples. Each sample contains: - an image - x,y coordinates - epsg of the coordinates - affected label - magnitude ## Citation BibTeX: ``` @article{quakeset, title={QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1}, author={Daniele Rege Cambrin and Paolo Garza}, journal={Proceedings of the 21th International Conference on Information Systems for Crisis Response and Management}, year={2024}, } ```
hazyresearch/fda	--- dataset_info: features: - name: doc_id dtype: string - name: file_name dtype: string - name: key dtype: string - name: value dtype: string - name: text dtype: string splits: - name: validaion num_bytes: 8498008 num_examples: 1102 download_size: 1381388 dataset_size: 8498008 configs: - config_name: default data_files: - split: validaion path: data/validaion-* ---
Fizzarolli/bluemoon_processeed	--- task_categories: - text-generation source_datasets: PJMixers/grimulkan_bluemoon_Karen_cleaned-carded --- preprocessed version of [PJMixers/grimulkan_bluemoon_Karen_cleaned-carded](https://huggingface.co/datasets/PJMixers/grimulkan_bluemoon_Karen_cleaned-carded) into a fun little prompt format for finetuning
xwjzds/pretrain_punctuation	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 2388642394 num_examples: 631331 download_size: 1485646531 dataset_size: 2388642394 --- # Dataset Card for "pretrain_punctuation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aisuko/funsd-layoutlmv3	--- license: apache-2.0 --- Original from: https://huggingface.co/datasets/aisuko/funsd-layoutlmv3 Adaptered by: Aisuko License: Apache-2.0 ```python dataset = load_dataset("aisuko/funsd-layoutlmv3") # check the dataset dataset # check the features dataset["train"].features # check the first example example=dataset["train"][0] example["image"] ```
projectbaraat/hindi-translation-data-v0.1	--- dataset_info: features: - name: src dtype: string - name: tgt dtype: string splits: - name: train num_bytes: 3880301105 num_examples: 10572313 download_size: 2054879767 dataset_size: 3880301105 configs: - config_name: default data_files: - split: train path: data/train-* ---
distilled-from-one-sec-cv12/chunk_16	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1245592852 num_examples: 242711 download_size: 1269320601 dataset_size: 1245592852 --- # Dataset Card for "chunk_16" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rb05751/reuters_articles	--- license: cc 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: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 12503434 num_examples: 15000 - name: validation num_bytes: 4272675 num_examples: 5000 - name: test num_bytes: 1709070 num_examples: 2000 download_size: 10790292 dataset_size: 18485179 ---
Twenty1/aws-lambda-developer-guide-docs	--- license: openrail ---
Sitedemerde/projet	--- license: other license_name: jesaispas license_link: LICENSE ---
Ehraim/SequentialLearnerv3	--- license: apache-2.0 ---
cheafdevo56/Influential_CitedNegs_1_Percent	--- dataset_info: features: - name: query struct: - name: abstract dtype: string - name: corpus_id dtype: int64 - name: title dtype: string - name: pos struct: - name: abstract dtype: string - name: corpus_id dtype: int64 - name: title dtype: string - name: neg struct: - name: abstract dtype: string - name: corpus_id dtype: int64 - name: score dtype: int64 - name: title dtype: string splits: - name: train num_bytes: 173163283.2 num_examples: 45000 - name: validation num_bytes: 19240364.8 num_examples: 5000 download_size: 115634444 dataset_size: 192403648.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
shibing624/huatuo_medical_qa_sharegpt	--- license: apache-2.0 --- source： - https://huggingface.co/datasets/FreedomIntelligence/HuatuoGPT-sft-data-v1 - https://huggingface.co/datasets/FreedomIntelligence/HuatuoGPT2_sft_instruct_GPT4_50K 转为sharegpt格式，jsonl文件。 data size: ``` > wc -l HuatuoGPT_sft_data_v1_sharegpt.jsonl 226042 HuatuoGPT_sft_data_v1_sharegpt.jsonl > wc -l HuatuoGPT2_sft_instruct_GPT4_sharegpt.jsonl 50000 HuatuoGPT2_sft_instruct_GPT4_sharegpt.jsonl ``` 转换代码：convert.py ```python import json # 假设您的JSONL文件名为 'input.jsonl' input_file = './HuatuoGPT2_sft_instruct_GPT4.jsonl' output_file = './HuatuoGPT2_sft_instruct_GPT4_sharegpt.jsonl' # 初始化输出文件 with open(input_file, 'r', encoding='utf-8') as infile, open(output_file, 'w', encoding='utf-8') as outfile: # 初始化输出的JSON结构 # 逐行读取JSONL文件 for id,line in enumerate(infile): output_json = {"conversations": []} # 解析JSON对象 data = json.loads(line.strip()) # if id > 10: # break # 假设每个JSON对象都有一个"data"列表，包含问题和答案 for i, item in enumerate(data['data']): if i % 2 == 0: # 假设问题在偶数位置，答案在奇数位置 output_json['conversations'].append({ "from": "human", "value": item[2:] }) else: output_json['conversations'].append({ "from": "gpt", "value": item[2:] }) # 将转换后的JSON写入文件 a = json.dumps(output_json, ensure_ascii=False) outfile.write(a + '\n') print(f"Conversion complete. Output saved to '{output_file}'.") ```
AlexanderBenady/generated_lectures	--- license: unknown ---
ArjjunS/Pantaloons_public	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4229185.0 num_examples: 75 download_size: 4206323 dataset_size: 4229185.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
liuyanchen1015/MULTI_VALUE_mnli_his_he	--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 125800 num_examples: 584 - name: dev_mismatched num_bytes: 126834 num_examples: 518 - name: test_matched num_bytes: 141846 num_examples: 661 - name: test_mismatched num_bytes: 121135 num_examples: 491 - name: train num_bytes: 5412418 num_examples: 25033 download_size: 3767461 dataset_size: 5928033 --- # Dataset Card for "MULTI_VALUE_mnli_his_he" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
up-the-brics/raw-chatgpt	--- license: apache-2.0 ---
afrikaans_ner_corpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - af license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Afrikaans Ner Corpus license_details: Creative Commons Attribution 2.5 South Africa License dataset_info: config_name: afrikaans_ner_corpus features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 4025651 num_examples: 8962 download_size: 944804 dataset_size: 4025651 configs: - config_name: afrikaans_ner_corpus data_files: - split: train path: afrikaans_ner_corpus/train-* default: true --- # Dataset Card for Afrikaans Ner Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Afrikaans Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/299) - Repository: - Paper: - Leaderboard: - Point of Contact: [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za) ### Dataset Summary The Afrikaans Ner Corpus is an Afrikaans dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Afrikaans. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [0, 0, 0, 0, 0], 'tokens': ['Vertaling', 'van', 'die', 'inligting', 'in'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity. ### Data Splits The data was not split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Afrikaans. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data is based on South African government domain and was crawled from gov.za websites. [More Information Needed] #### Who are the source language producers? The data was produced by writers of South African government websites - gov.za [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated during the NCHLT text resource development project. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa). See: [more information](http://www.nwu.ac.za/ctext) ### Licensing Information The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode) ### Citation Information ``` @inproceedings{afrikaans_ner_corpus, author = { Gerhard van Huyssteen and Martin Puttkammer and E.B. Trollip and J.C. Liversage and Roald Eiselen}, title = {NCHLT Afrikaans Named Entity Annotated Corpus}, booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.}, year = {2016}, url = {https://repo.sadilar.org/handle/20.500.12185/299}, } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
orgcatorg/megawika	--- dataset_info: - config_name: my features: - name: article_title dtype: string - name: article_text dtype: string - name: entries list: - name: id dtype: string - name: original dtype: string - name: original_sents sequence: string - name: parse_tokens sequence: sequence: string - name: passage struct: - name: en_lang_token_map struct: - name: '0' dtype: int64 - name: '1' dtype: int64 - name: '10' dtype: int64 - name: '100' dtype: int64 - name: '101' dtype: int64 - name: '102' dtype: int64 - name: '103' dtype: int64 - name: '104' dtype: int64 - name: '105' dtype: int64 - name: '106' dtype: int64 - name: '107' dtype: int64 - name: '108' dtype: int64 - name: '109' dtype: int64 - name: '11' dtype: int64 - name: '110' dtype: int64 - name: '111' dtype: int64 - name: '112' dtype: int64 - name: '113' dtype: int64 - name: '114' dtype: int64 - name: '115' dtype: int64 - name: '116' dtype: int64 - name: '117' dtype: int64 - name: '118' dtype: int64 - name: '119' dtype: int64 - name: '12' dtype: int64 - name: '120' dtype: int64 - name: '121' dtype: int64 - name: '122' dtype: int64 - name: '123' dtype: int64 - name: '124' dtype: int64 - name: '125' dtype: int64 - name: '126' dtype: int64 - name: '127' dtype: int64 - name: '128' dtype: int64 - name: '129' dtype: int64 - name: '13' dtype: int64 - name: '130' dtype: int64 - name: '131' dtype: int64 - name: '132' dtype: int64 - name: '133' dtype: int64 - name: '134' dtype: int64 - name: '135' dtype: int64 - name: '136' dtype: int64 - name: '137' dtype: int64 - name: '138' dtype: int64 - name: '139' dtype: int64 - name: '14' dtype: int64 - name: '140' dtype: int64 - name: '141' dtype: int64 - name: '142' dtype: int64 - name: '143' dtype: int64 - name: '144' dtype: int64 - name: '145' dtype: int64 - name: '146' dtype: int64 - name: '147' dtype: int64 - name: '148' dtype: int64 - name: '149' dtype: int64 - name: '15' dtype: int64 - name: '150' dtype: int64 - name: '151' dtype: int64 - name: '152' dtype: int64 - name: '153' dtype: int64 - 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name: '364' dtype: string - name: '365' dtype: string - name: '366' dtype: string - name: '367' dtype: string - name: '368' dtype: string - name: '369' dtype: string - name: '37' dtype: string - name: '370' dtype: string - name: '371' dtype: string - name: '372' dtype: string - name: '373' dtype: string - name: '374' dtype: string - name: '375' dtype: string - name: '376' dtype: string - name: '38' dtype: string - name: '39' dtype: string - name: '4' dtype: string - name: '40' dtype: string - name: '41' dtype: string - name: '42' dtype: string - name: '43' dtype: string - name: '44' dtype: string - name: '45' dtype: string - name: '46' dtype: string - name: '47' dtype: string - name: '48' dtype: string - name: '49' dtype: string - name: '5' dtype: string - name: '50' dtype: string - name: '51' dtype: string - name: '52' dtype: string - name: '53' dtype: string - name: '54' dtype: string - name: '55' dtype: string - name: '56' dtype: string - name: '57' dtype: string - name: '58' dtype: string - name: '59' dtype: string - name: '6' dtype: string - name: '60' dtype: string - name: '61' dtype: string - name: '62' dtype: string - name: '63' dtype: string - name: '64' dtype: string - name: '65' dtype: string - name: '66' dtype: string - name: '67' dtype: string - name: '68' dtype: string - name: '69' dtype: string - name: '7' dtype: string - name: '70' dtype: string - name: '71' dtype: string - name: '72' dtype: string - name: '73' dtype: string - name: '74' dtype: string - name: '75' dtype: string - name: '76' dtype: string - name: '77' dtype: string - name: '78' dtype: string - name: '79' dtype: string - name: '8' dtype: string - name: '80' dtype: string - name: '81' dtype: string - name: '82' dtype: string - name: '83' dtype: string - name: '84' dtype: string - name: '85' dtype: string - name: '86' dtype: string - name: '87' dtype: string - name: '88' dtype: string - name: '89' dtype: string - name: '9' dtype: string - name: '90' dtype: string - name: '91' dtype: string - name: '92' dtype: string - name: '93' dtype: string - name: '94' dtype: string - name: '95' dtype: string - name: '96' dtype: string - name: '97' dtype: string - name: '98' dtype: string - name: '99' dtype: string - name: parse list: - name: children list: - name: children list: - name: children sequence: 'null' - name: confidence dtype: float64 - name: label dtype: string - name: span sequence: int64 - name: confidence dtype: float64 - name: label dtype: string - name: span sequence: int64 - name: confidence dtype: float64 - name: label dtype: string - name: span sequence: int64 - name: text sequence: string - name: qa_pairs list: - name: en_answer dtype: string - name: en_answer_tokens sequence: string - name: en_match_in_passage sequence: int64 - name: en_matches_in_source sequence: sequence: int64 - name: frames list: - name: argument dtype: string - name: frame dtype: string - name: lang_answer dtype: string - name: lang_match_in_passage sequence: int64 - name: lang_matches_in_source sequence: sequence: int64 - 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name: span sequence: int64 - name: confidence dtype: float64 - name: label dtype: string - name: span sequence: int64 - name: confidence dtype: float64 - name: label dtype: string - name: span sequence: int64 - name: text sequence: string - name: qa_pairs list: - name: en_answer dtype: string - name: en_answer_tokens sequence: string - name: en_match_in_passage sequence: int64 - name: en_matches_in_source sequence: sequence: int64 - name: frames list: - name: argument dtype: string - name: frame dtype: string - name: lang_answer dtype: string - name: lang_match_in_passage sequence: int64 - name: lang_matches_in_source sequence: sequence: int64 - name: match_disambiguated_question dtype: string - name: passage sequence: string - name: passage_id dtype: string - name: question dtype: string - name: repetitious_translation dtype: bool - name: source_lang dtype: string - name: source_text dtype: string - name: source_url dtype: string - name: translation dtype: string - name: translation_probs sequence: string - name: translation_sents sequence: string splits: - name: train num_bytes: 50253843 num_examples: 842 download_size: 12279811 dataset_size: 50253843 configs: - config_name: my data_files: - split: my path: my/my-* - config_name: my_refined data_files: - split: train path: my_refined/train-* --- # Dataset Card for "megawika" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
distilled-from-one-sec-cv12/chunk_228	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1186307988 num_examples: 231159 download_size: 1208791652 dataset_size: 1186307988 --- # Dataset Card for "chunk_228" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ShenaoZ/0.001_ablation_dataset	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: is_better dtype: bool splits: - name: train_prefs_1 num_bytes: 168081294 num_examples: 20378 - name: test_prefs_1 num_bytes: 16410846 num_examples: 2000 - name: train_prefs_2 num_bytes: 174168708 num_examples: 20378 - name: test_prefs_2 num_bytes: 16912720 num_examples: 2000 download_size: 207506841 dataset_size: 375573568 configs: - config_name: default data_files: - split: train_prefs_1 path: data/train_prefs_1-* - split: test_prefs_1 path: data/test_prefs_1-* - split: train_prefs_2 path: data/train_prefs_2-* - split: test_prefs_2 path: data/test_prefs_2-* --- # Dataset Card for "0.001_ablation_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
johnsonlee/My-First-Dataset	--- license: postgresql language: - ch tags: - finance pretty_name: It's a good name. size_categories: - 1K<n<10K --- 这是我的第一个dataset
davidberenstein1957/text2text-10-predictions	--- dataset_info: features: - name: text dtype: string - name: target dtype: string splits: - name: train num_bytes: 104132.0 num_examples: 72 - name: test num_bytes: 26033.0 num_examples: 18 download_size: 86213 dataset_size: 130165.0 --- # Dataset Card for "text2text-10-predictions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
osyvokon/pavlick-formality-scores	--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-3.0 multilinguality: - monolingual pretty_name: 'Sentence-level formality annotations for news, blogs, email and QA forums. Published in "An Empirical Analysis of Formality in Online Communication" (Pavlick and Tetreault, 2016) ' size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring --- This dataset contains sentence-level formality annotations used in the 2016 TACL paper "An Empirical Analysis of Formality in Online Communication" (Pavlick and Tetreault, 2016). It includes sentences from four genres (news, blogs, email, and QA forums), all annotated by humans on Amazon Mechanical Turk. The news and blog data was collected by Shibamouli Lahiri, and we are redistributing it here for the convenience of other researchers. We collected the email and answers data ourselves, using a similar annotation setup to Shibamouli. In the original dataset, `answers` and `email` were tokenized. In this version, Oleksiy Syvokon detokenized them with `moses-detokenizer` and a bunch of additional regexps. If you use this data in your work, please cite BOTH of the below papers: ``` @article{PavlickAndTetreault-2016:TACL, author = {Ellie Pavlick and Joel Tetreault}, title = {An Empirical Analysis of Formality in Online Communication}, journal = {Transactions of the Association for Computational Linguistics}, year = {2016}, publisher = {Association for Computational Linguistics} } @article{Lahiri-2015:arXiv, title={{SQUINKY! A} Corpus of Sentence-level Formality, Informativeness, and Implicature}, author={Lahiri, Shibamouli}, journal={arXiv preprint arXiv:1506.02306}, year={2015} } ``` ## Contents The annotated data files and number of lines in each are as follows: * 4977 answers -- Annotated sentences from a random sample of posts from the Yahoo! Answers forums: https://answers.yahoo.com/ * 1821 blog -- Annotated sentences from the top 100 blogs listed on http://technorati.com/ on October 31, 2009. * 1701 email -- Annotated sentences from a random sample of emails from the Jeb Bush email archive: http://americanbridgepac.org/jeb-bushs-gubernatorial-email-archive/ * 2775 news -- Annotated sentences from the "breaking", "recent", and "local" news sections of the following 20 news sites: CNN, CBS News, ABC News, Reuters, BBC News Online, New York Times, Los Angeles Times, The Guardian (U.K.), Voice of America, Boston Globe, Chicago Tribune, San Francisco Chronicle, Times Online (U.K.), news.com.au, Xinhua, The Times of India, Seattle Post Intelligencer, Daily Mail, and Bloomberg L.P. ## Format Each record contains the following fields: 1. `avg_score`: the mean formality rating, which ranges from -3 to 3 where lower scores indicate less formal sentences 2. `sentence`
CyberHarem/altina_fireemblem	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of altina (Fire Emblem) This is the dataset of altina (Fire Emblem), containing 54 images and their tags. The core tags of this character are `long_hair, blue_eyes, purple_hair, breasts, bangs, large_breasts, very_long_hair, blue_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 \| 54 \| 83.55 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 54 \| 48.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 127 \| 90.62 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 54 \| 73.48 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 127 \| 124.57 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/altina_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 5 \| ![](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, armor, looking_at_viewer, solo, sword, dress, fingerless_gloves, holding, thighhighs, simple_background, white_background, boots, elbow_gloves \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, bell, christmas, fur_trim, reindeer_antlers, solo, black_thighhighs, deer_ears, full_body, looking_at_viewer, candy_cane, fake_animal_ears, gift_box, holding_sword, medium_breasts, simple_background, smile, white_footwear, white_gloves, elbow_gloves, low-tied_long_hair, open_mouth, parted_lips \| \| 2 \| 17 \| ![](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, cleavage, looking_at_viewer, solo, navel, smile, hat, blush, abs, collarbone, muscular_female, one-piece_swimsuit, simple_background, white_background, bracelet \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| armor \| looking_at_viewer \| solo \| sword \| dress \| fingerless_gloves \| holding \| thighhighs \| simple_background \| white_background \| boots \| elbow_gloves \| bell \| christmas \| fur_trim \| reindeer_antlers \| black_thighhighs \| deer_ears \| full_body \| candy_cane \| fake_animal_ears \| gift_box \| holding_sword \| medium_breasts \| smile \| white_footwear \| white_gloves \| low-tied_long_hair \| open_mouth \| parted_lips \| cleavage \| navel \| hat \| blush \| abs \| collarbone \| muscular_female \| one-piece_swimsuit \| bracelet \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:--------------------\|:-------\|:--------\|:--------\|:--------------------\|:----------\|:-------------\|:--------------------\|:-------------------\|:--------\|:---------------\|:-------\|:------------\|:-----------\|:-------------------\|:-------------------\|:------------\|:------------\|:-------------\|:-------------------\|:-----------\|:----------------\|:-----------------\|:--------\|:-----------------\|:---------------\|:---------------------\|:-------------\|:--------------\|:-----------\|:--------\|:------\|:--------\|:------\|:-------------\|:------------------\|:---------------------\|:-----------\| \| 0 \| 5 \| ![](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 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 2 \| 17 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
ibranze/araproje_mmlu_tr_conf_gpt2_nearestscore_true_x	--- dataset_info: features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: validation num_bytes: 137404.0 num_examples: 250 download_size: 83805 dataset_size: 137404.0 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "araproje_mmlu_tr_conf_gpt2_nearestscore_true_x" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
philschmid/hh-rrhf-dahoas-gptj-rm-25k	--- dataset_info: features: - name: prompt dtype: string - name: responses sequence: string - name: scores sequence: float64 splits: - name: train num_bytes: 21973591 num_examples: 24983 download_size: 12522534 dataset_size: 21973591 --- # Dataset Card for "hh-rrhf-dahoas-gptj-rm-25k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bigscience-data/roots_ar_wiktionary	--- language: ar license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox ---
jiwon65/aihub_general_6000_for_train	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: audio sequence: float32 splits: - name: train num_bytes: 1212419491 num_examples: 6000 download_size: 1071487189 dataset_size: 1212419491 --- # Dataset Card for "korean-general-command-voice_0-6000_samplingRate-16000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ibragim-bad/arc_challenge	--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices struct: - name: label sequence: string - name: text sequence: string - name: answerKey dtype: string splits: - name: test num_bytes: 375511 num_examples: 1172 - name: train num_bytes: 349760 num_examples: 1119 - name: validation num_bytes: 96660 num_examples: 299 download_size: 449682 dataset_size: 821931 --- # Dataset Card for "arc_challenge" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
imvladikon/hebrew_news	--- annotations_creators: - no-annotation language_creators: - other language: - he license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization --- ## 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 ``` id - article id articleBody - article main content description - short version of the article, description of the article headline - headline of the article title - title of the article ```
open-llm-leaderboard/details_maywell__PiVoT-SUS-RP	--- pretty_name: Evaluation run of maywell/PiVoT-SUS-RP dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [maywell/PiVoT-SUS-RP](https://huggingface.co/maywell/PiVoT-SUS-RP) 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_maywell__PiVoT-SUS-RP\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-15T19:33:37.820287](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__PiVoT-SUS-RP/blob/main/results_2024-01-15T19-33-37.820287.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\": 0.7586143927988348,\n\ \ \"acc_stderr\": 0.028201585690631335,\n \"acc_norm\": 0.7620604659128375,\n\ \ \"acc_norm_stderr\": 0.028744091509590272,\n \"mc1\": 0.3843329253365973,\n\ \ \"mc1_stderr\": 0.017028707301245203,\n \"mc2\": 0.5456609919227617,\n\ \ \"mc2_stderr\": 0.014778672831926782\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620192,\n\ \ \"acc_norm\": 0.6655290102389079,\n \"acc_norm_stderr\": 0.013787460322441375\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6398127862975503,\n\ \ \"acc_stderr\": 0.00479073468370459,\n \"acc_norm\": 0.8422624975104561,\n\ \ \"acc_norm_stderr\": 0.0036374977089340356\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.7111111111111111,\n\ \ \"acc_stderr\": 0.0391545063041425,\n \"acc_norm\": 0.7111111111111111,\n\ \ \"acc_norm_stderr\": 0.0391545063041425\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.9013157894736842,\n \"acc_stderr\": 0.024270227737522715,\n\ \ \"acc_norm\": 0.9013157894736842,\n \"acc_norm_stderr\": 0.024270227737522715\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8113207547169812,\n \"acc_stderr\": 0.024079995130062253,\n\ \ \"acc_norm\": 0.8113207547169812,\n \"acc_norm_stderr\": 0.024079995130062253\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9097222222222222,\n\ \ \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n\ \ \"acc_norm_stderr\": 0.023964965777906935\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\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7341040462427746,\n\ \ \"acc_stderr\": 0.03368762932259431,\n \"acc_norm\": 0.7341040462427746,\n\ \ \"acc_norm_stderr\": 0.03368762932259431\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7787234042553192,\n \"acc_stderr\": 0.027136349602424063,\n\ \ \"acc_norm\": 0.7787234042553192,\n \"acc_norm_stderr\": 0.027136349602424063\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5789473684210527,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7655172413793103,\n \"acc_stderr\": 0.035306258743465914,\n\ \ \"acc_norm\": 0.7655172413793103,\n \"acc_norm_stderr\": 0.035306258743465914\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.6931216931216931,\n \"acc_stderr\": 0.023752928712112136,\n \"\ acc_norm\": 0.6931216931216931,\n \"acc_norm_stderr\": 0.023752928712112136\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5952380952380952,\n\ \ \"acc_stderr\": 0.04390259265377563,\n \"acc_norm\": 0.5952380952380952,\n\ \ \"acc_norm_stderr\": 0.04390259265377563\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-high_school_biology\|5\"\ : {\n \"acc\": 0.8935483870967742,\n \"acc_stderr\": 0.01754510295165663,\n\ \ \"acc_norm\": 0.8935483870967742,\n \"acc_norm_stderr\": 0.01754510295165663\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.6551724137931034,\n \"acc_stderr\": 0.03344283744280458,\n \"\ acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03344283744280458\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\"\ : 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.8153846153846154,\n \"acc_stderr\": 0.019671632413100295,\n\ \ \"acc_norm\": 0.8153846153846154,\n \"acc_norm_stderr\": 0.019671632413100295\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.43333333333333335,\n \"acc_stderr\": 0.030213340289237927,\n \ \ \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.030213340289237927\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8697478991596639,\n \"acc_stderr\": 0.021863258494852118,\n\ \ \"acc_norm\": 0.8697478991596639,\n \"acc_norm_stderr\": 0.021863258494852118\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289715,\n \"\ acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289715\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769578,\n \"\ acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769578\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6527777777777778,\n \"acc_stderr\": 0.032468872436376486,\n \"\ acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.032468872436376486\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089678,\n \"\ acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089678\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \ \ \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7937219730941704,\n\ \ \"acc_stderr\": 0.02715715047956382,\n \"acc_norm\": 0.7937219730941704,\n\ \ \"acc_norm_stderr\": 0.02715715047956382\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\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622793,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622793\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.03038159675665168,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.03038159675665168\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n\ \ \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5625,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.912621359223301,\n \"acc_stderr\": 0.027960689125970654,\n\ \ \"acc_norm\": 0.912621359223301,\n \"acc_norm_stderr\": 0.027960689125970654\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9316239316239316,\n\ \ \"acc_stderr\": 0.016534627684311364,\n \"acc_norm\": 0.9316239316239316,\n\ \ \"acc_norm_stderr\": 0.016534627684311364\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263714,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263714\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9054916985951469,\n\ \ \"acc_stderr\": 0.010461015338193068,\n \"acc_norm\": 0.9054916985951469,\n\ \ \"acc_norm_stderr\": 0.010461015338193068\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8121387283236994,\n \"acc_stderr\": 0.021029269752423228,\n\ \ \"acc_norm\": 0.8121387283236994,\n \"acc_norm_stderr\": 0.021029269752423228\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.6212290502793296,\n\ \ \"acc_stderr\": 0.016223533510365123,\n \"acc_norm\": 0.6212290502793296,\n\ \ \"acc_norm_stderr\": 0.016223533510365123\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8562091503267973,\n \"acc_stderr\": 0.020091188936043693,\n\ \ \"acc_norm\": 0.8562091503267973,\n \"acc_norm_stderr\": 0.020091188936043693\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8231511254019293,\n\ \ \"acc_stderr\": 0.0216700588855108,\n \"acc_norm\": 0.8231511254019293,\n\ \ \"acc_norm_stderr\": 0.0216700588855108\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.018689725721062072,\n\ \ \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.018689725721062072\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6347517730496454,\n \"acc_stderr\": 0.028723863853281267,\n \ \ \"acc_norm\": 0.6347517730496454,\n \"acc_norm_stderr\": 0.028723863853281267\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.6075619295958279,\n\ \ \"acc_stderr\": 0.012471243669229096,\n \"acc_norm\": 0.6075619295958279,\n\ \ \"acc_norm_stderr\": 0.012471243669229096\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.02315746830855936,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02315746830855936\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8088235294117647,\n \"acc_stderr\": 0.015908290136278036,\n \ \ \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.015908290136278036\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8326530612244898,\n \"acc_stderr\": 0.02389714476891452,\n\ \ \"acc_norm\": 0.8326530612244898,\n \"acc_norm_stderr\": 0.02389714476891452\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824664,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824664\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5783132530120482,\n\ \ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\ \ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.9005847953216374,\n \"acc_stderr\": 0.022949025579355027,\n\ \ \"acc_norm\": 0.9005847953216374,\n \"acc_norm_stderr\": 0.022949025579355027\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3843329253365973,\n\ \ \"mc1_stderr\": 0.017028707301245203,\n \"mc2\": 0.5456609919227617,\n\ \ \"mc2_stderr\": 0.014778672831926782\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.010470796496781105\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7050796057619408,\n \ \ \"acc_stderr\": 0.012560698010954762\n }\n}\n```" repo_url: https://huggingface.co/maywell/PiVoT-SUS-RP 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_15T19_33_37.820287 path: - '/details_harness\|arc:challenge\|25_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-15T19-33-37.820287.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|gsm8k\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hellaswag\|10_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-15T19-33-37.820287.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-15T19-33-37.820287.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '/details_harness\|winogrande\|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-15T19-33-37.820287.parquet' - config_name: results data_files: - split: 2024_01_15T19_33_37.820287 path: - results_2024-01-15T19-33-37.820287.parquet - split: latest path: - results_2024-01-15T19-33-37.820287.parquet --- # Dataset Card for Evaluation run of maywell/PiVoT-SUS-RP <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [maywell/PiVoT-SUS-RP](https://huggingface.co/maywell/PiVoT-SUS-RP) 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_maywell__PiVoT-SUS-RP", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-15T19:33:37.820287](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__PiVoT-SUS-RP/blob/main/results_2024-01-15T19-33-37.820287.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": 0.7586143927988348, "acc_stderr": 0.028201585690631335, "acc_norm": 0.7620604659128375, "acc_norm_stderr": 0.028744091509590272, "mc1": 0.3843329253365973, "mc1_stderr": 0.017028707301245203, "mc2": 0.5456609919227617, "mc2_stderr": 0.014778672831926782 }, "harness\|arc:challenge\|25": { "acc": 0.643344709897611, "acc_stderr": 0.013998056902620192, "acc_norm": 0.6655290102389079, "acc_norm_stderr": 0.013787460322441375 }, "harness\|hellaswag\|10": { "acc": 0.6398127862975503, "acc_stderr": 0.00479073468370459, "acc_norm": 0.8422624975104561, "acc_norm_stderr": 0.0036374977089340356 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7111111111111111, "acc_stderr": 0.0391545063041425, "acc_norm": 0.7111111111111111, "acc_norm_stderr": 0.0391545063041425 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.9013157894736842, "acc_stderr": 0.024270227737522715, "acc_norm": 0.9013157894736842, "acc_norm_stderr": 0.024270227737522715 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8113207547169812, "acc_stderr": 0.024079995130062253, "acc_norm": 0.8113207547169812, "acc_norm_stderr": 0.024079995130062253 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "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": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.45, "acc_stderr": 0.04999999999999999, "acc_norm": 0.45, "acc_norm_stderr": 0.04999999999999999 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.03368762932259431, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.03368762932259431 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.5588235294117647, "acc_stderr": 0.049406356306056595, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7787234042553192, "acc_stderr": 0.027136349602424063, "acc_norm": 0.7787234042553192, "acc_norm_stderr": 0.027136349602424063 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5789473684210527, "acc_stderr": 0.046446020912223177, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.046446020912223177 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7655172413793103, "acc_stderr": 0.035306258743465914, "acc_norm": 0.7655172413793103, "acc_norm_stderr": 0.035306258743465914 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6931216931216931, "acc_stderr": 0.023752928712112136, "acc_norm": 0.6931216931216931, "acc_norm_stderr": 0.023752928712112136 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5952380952380952, "acc_stderr": 0.04390259265377563, "acc_norm": 0.5952380952380952, "acc_norm_stderr": 0.04390259265377563 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8935483870967742, "acc_stderr": 0.01754510295165663, "acc_norm": 0.8935483870967742, "acc_norm_stderr": 0.01754510295165663 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03344283744280458, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03344283744280458 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527033, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527033 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8153846153846154, "acc_stderr": 0.019671632413100295, "acc_norm": 0.8153846153846154, "acc_norm_stderr": 0.019671632413100295 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.43333333333333335, "acc_stderr": 0.030213340289237927, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.030213340289237927 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8697478991596639, "acc_stderr": 0.021863258494852118, "acc_norm": 0.8697478991596639, "acc_norm_stderr": 0.021863258494852118 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.48344370860927155, "acc_stderr": 0.040802441856289715, "acc_norm": 0.48344370860927155, "acc_norm_stderr": 0.040802441856289715 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9211009174311927, "acc_stderr": 0.011558198113769578, "acc_norm": 0.9211009174311927, "acc_norm_stderr": 0.011558198113769578 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6527777777777778, "acc_stderr": 0.032468872436376486, "acc_norm": 0.6527777777777778, "acc_norm_stderr": 0.032468872436376486 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9264705882352942, "acc_stderr": 0.018318855850089678, "acc_norm": 0.9264705882352942, "acc_norm_stderr": 0.018318855850089678 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9113924050632911, "acc_stderr": 0.018498315206865384, "acc_norm": 0.9113924050632911, "acc_norm_stderr": 0.018498315206865384 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7937219730941704, "acc_stderr": 0.02715715047956382, "acc_norm": 0.7937219730941704, "acc_norm_stderr": 0.02715715047956382 }, "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": 0.8842975206611571, "acc_stderr": 0.029199802455622793, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622793 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.03038159675665168, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.03038159675665168 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5625, "acc_stderr": 0.04708567521880525, "acc_norm": 0.5625, "acc_norm_stderr": 0.04708567521880525 }, "harness\|hendrycksTest-management\|5": { "acc": 0.912621359223301, "acc_stderr": 0.027960689125970654, "acc_norm": 0.912621359223301, "acc_norm_stderr": 0.027960689125970654 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9316239316239316, "acc_stderr": 0.016534627684311364, "acc_norm": 0.9316239316239316, "acc_norm_stderr": 0.016534627684311364 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.85, "acc_stderr": 0.035887028128263714, "acc_norm": 0.85, "acc_norm_stderr": 0.035887028128263714 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9054916985951469, "acc_stderr": 0.010461015338193068, "acc_norm": 0.9054916985951469, "acc_norm_stderr": 0.010461015338193068 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8121387283236994, "acc_stderr": 0.021029269752423228, "acc_norm": 0.8121387283236994, "acc_norm_stderr": 0.021029269752423228 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.6212290502793296, "acc_stderr": 0.016223533510365123, "acc_norm": 0.6212290502793296, "acc_norm_stderr": 0.016223533510365123 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8562091503267973, "acc_stderr": 0.020091188936043693, "acc_norm": 0.8562091503267973, "acc_norm_stderr": 0.020091188936043693 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8231511254019293, "acc_stderr": 0.0216700588855108, "acc_norm": 0.8231511254019293, "acc_norm_stderr": 0.0216700588855108 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8703703703703703, "acc_stderr": 0.018689725721062072, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.018689725721062072 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6347517730496454, "acc_stderr": 0.028723863853281267, "acc_norm": 0.6347517730496454, "acc_norm_stderr": 0.028723863853281267 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.6075619295958279, "acc_stderr": 0.012471243669229096, "acc_norm": 0.6075619295958279, "acc_norm_stderr": 0.012471243669229096 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8235294117647058, "acc_stderr": 0.02315746830855936, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.02315746830855936 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8088235294117647, "acc_stderr": 0.015908290136278036, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.015908290136278036 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8326530612244898, "acc_stderr": 0.02389714476891452, "acc_norm": 0.8326530612244898, "acc_norm_stderr": 0.02389714476891452 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824664, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824664 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5783132530120482, "acc_stderr": 0.038444531817709175, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.038444531817709175 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.9005847953216374, "acc_stderr": 0.022949025579355027, "acc_norm": 0.9005847953216374, "acc_norm_stderr": 0.022949025579355027 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3843329253365973, "mc1_stderr": 0.017028707301245203, "mc2": 0.5456609919227617, "mc2_stderr": 0.014778672831926782 }, "harness\|winogrande\|5": { "acc": 0.8334648776637726, "acc_stderr": 0.010470796496781105 }, "harness\|gsm8k\|5": { "acc": 0.7050796057619408, "acc_stderr": 0.012560698010954762 } } ``` ## 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 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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. 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mteb/twentynewsgroups-clustering	--- language: - en ---
X-LANCE/WebSRC_v1.0	--- license: cc-by-4.0 --- # WebSRC v1.0 WebSRC v1.0 is a dataset for reading comprehension on structural web pages. The task is to answer questions about web pages, which requires a system to have a comprehensive understanding of the spatial structure and logical structure. WebSRC consists of 6.4K web pages and 400K question-answer pairs about web pages. For each web page, we manually chose one segment from it and saved the corresponding HTML code, screenshot, and metadata like positions and sizes. Questions in WebSRC were created for each segment. Answers are either text spans from web pages or yes/no. Taking the HTML code, screenshot, metadata as well as question as input, a model is to predict the answer from the web page. Our dataset is the first one that provides HTML documents as well as images, and is larger in the number of domains and queries. For more details, please refer to our paper [WebSRC: A Dataset for Web-Based Structural Reading Comprehension](https://arxiv.org/abs/2101.09465). The Leaderboard of WebSRC v1.0 can be found [here](https://x-lance.github.io/WebSRC/). ## Data Format Description The dataset for each website will be stored in `dataset.csv` in the directory `{domain-name}/{website-number}`. The corresponding raw data (including HTML files, screenshots, bounding box coordinates, and page names and urls) is stored in the `processed_data` folder in the same directory. In `dataset.csv`, each row corresponds to one question-answer data point except the header. The meanings of each column are as follows: * `question`: a string, the question of this question-answer data point. * `id`: a unique id for this question-answer data point. Each `id` has a length 14, the first two characters are the domain indicator, the following two number is the website name. The corresponding page id can be extracted by `id[2:9]`, for example, id "sp160000100001" means this line is created from the sport domain, website 16, and the corresponding page is `1600001.html`. * `element_id`: an integer, the tag id (corresponding to the tag's `tid` attribute in the HTML files) of the deepest tag in the DOM tree which contain all the answer. For yes/no question, there is no tag associated with the answer, so the `element_id` is -1. * `answer_start`: an integer, the char offset of the answer from the start of the content of the tag specified by `element_id`. Note that before counting this number, we first eliminate all the inner tags in the specified tag and replace all the consecutive whitespaces with one space. For yes/no questions, `answer_start` is 1 for answer "yes" and 0 for answer "no". * `answer`: a string, the answer of this question-answer data point. ## Data Statistics We roughly divided the questions in WebSRC v1.0 into three categories: KV, Compare, and Table. The detailed definitions can be found in our [paper](https://arxiv.org/abs/2101.09465). The numbers of websites, webpages, and QAs corresponding to the three categories are as follows: Type \| # Websites \| # Webpages \| # QAs ---- \| ---------- \| ---------- \| ----- KV \| 34 \| 3,207 \| 168,606 Comparison \| 15 \| 1,339 \| 68,578 Table \| 21 \| 1,901 \| 163,314 The statistics of the dataset splits are as follows: Split \| # Websites \| # Webpages \| # QAs ----- \| ---------- \| ---------- \| ----- Train \| 50 \| 4,549 \| 307,315 Dev \| 10 \| 913 \| 52,826 Test \| 10 \| 985 \| 40,357 ## Obtain Test Result For test set evaluation, please send your prediction files to zhao_mengxin@sjtu.edu.cn and chenlusz@sjtu.edu.cn with title "WebSRC Test: \<your model name\>+\<your institution\>". For evaluation, the prediction files should contain two files: ```jsonc // prediction.json // A json format file, keys are ids and values are the predicted answers (string). { "sp160000100001": "predicted answer", "sp160000100002": "...", //... } // tag_prediction.json // A json format file, keys are ids and values are the predicted tag tid (int) { "sp160000100001": -1, "sp160000100002": -1, //... } ``` We encourage to submit results from at least three runs with different random seeds to reduce the uncertainty of the experiments. Please place prediction files for each run in different directories and submit a zipped file. The average test result will be sent by email. ## Reference If you use any source codes or datasets included in this repository in your work, please cite the corresponding papers. The bibtex are listed below: ```text @inproceedings{chen-etal-2021-websrc, title = "{W}eb{SRC}: A Dataset for Web-Based Structural Reading Comprehension", author = "Chen, Xingyu and Zhao, Zihan and Chen, Lu and Ji, JiaBao and Zhang, Danyang and Luo, Ao and Xiong, Yuxuan and Yu, Kai", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.343", pages = "4173--4185", abstract = "Web search is an essential way for humans to obtain information, but it{'}s still a great challenge for machines to understand the contents of web pages. In this paper, we introduce the task of web-based structural reading comprehension. Given a web page and a question about it, the task is to find an answer from the web page. This task requires a system not only to understand the semantics of texts but also the structure of the web page. Moreover, we proposed WebSRC, a novel Web-based Structural Reading Comprehension dataset. WebSRC consists of 400K question-answer pairs, which are collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata. Each question in WebSRC requires a certain structural understanding of a web page to answer, and the answer is either a text span on the web page or yes/no. We evaluate various strong baselines on our dataset to show the difficulty of our task. We also investigate the usefulness of structural information and visual features. Our dataset and baselines have been publicly available.", } ```
open-llm-leaderboard/details_EleutherAI__gpt-neo-125m	--- pretty_name: Evaluation run of EleutherAI/gpt-neo-125m dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) 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_EleutherAI__gpt-neo-125m\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-18T09:42:25.890470](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neo-125m/blob/main/results_2023-10-18T09-42-25.890470.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\": 0.0016778523489932886,\n\ \ \"em_stderr\": 0.0004191330178826801,\n \"f1\": 0.03690436241610747,\n\ \ \"f1_stderr\": 0.0011592977848577672,\n \"acc\": 0.2603955425321017,\n\ \ \"acc_stderr\": 0.007779096578699754\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.0004191330178826801,\n\ \ \"f1\": 0.03690436241610747,\n \"f1_stderr\": 0.0011592977848577672\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.003032600454890068,\n \ \ \"acc_stderr\": 0.0015145735612245494\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5177584846093133,\n \"acc_stderr\": 0.014043619596174959\n\ \ }\n}\n```" repo_url: https://huggingface.co/EleutherAI/gpt-neo-125m leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:58:00.274896.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_18T09_42_25.890470 path: - '/details_harness\|drop\|3_2023-10-18T09-42-25.890470.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-18T09-42-25.890470.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_18T09_42_25.890470 path: - '/details_harness\|gsm8k\|5_2023-10-18T09-42-25.890470.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-18T09-42-25.890470.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hellaswag\|10_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:58:00.274896.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_58_00.274896 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:58:00.274896.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_18T09_42_25.890470 path: - '/details_harness\|winogrande\|5_2023-10-18T09-42-25.890470.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-18T09-42-25.890470.parquet' - config_name: results data_files: - split: 2023_07_19T13_58_00.274896 path: - results_2023-07-19T13:58:00.274896.parquet - split: 2023_10_18T09_42_25.890470 path: - results_2023-10-18T09-42-25.890470.parquet - split: latest path: - results_2023-10-18T09-42-25.890470.parquet --- # Dataset Card for Evaluation run of EleutherAI/gpt-neo-125m ## Dataset Description - Homepage: - Repository: https://huggingface.co/EleutherAI/gpt-neo-125m - 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 [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) 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_EleutherAI__gpt-neo-125m", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-18T09:42:25.890470](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neo-125m/blob/main/results_2023-10-18T09-42-25.890470.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": 0.0016778523489932886, "em_stderr": 0.0004191330178826801, "f1": 0.03690436241610747, "f1_stderr": 0.0011592977848577672, "acc": 0.2603955425321017, "acc_stderr": 0.007779096578699754 }, "harness\|drop\|3": { "em": 0.0016778523489932886, "em_stderr": 0.0004191330178826801, "f1": 0.03690436241610747, "f1_stderr": 0.0011592977848577672 }, "harness\|gsm8k\|5": { "acc": 0.003032600454890068, "acc_stderr": 0.0015145735612245494 }, "harness\|winogrande\|5": { "acc": 0.5177584846093133, "acc_stderr": 0.014043619596174959 } } ``` ### 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]
adamo1139/toxic-dpo-natural-v3	--- license: other license_name: other license_link: LICENSE ---
jason-lee08/TinyStories_Mars	--- dataset_info: features: - name: prompts dtype: string - name: stories dtype: string splits: - name: train num_bytes: 5569066 num_examples: 3364 download_size: 2292233 dataset_size: 5569066 --- # Dataset Card for "TinyStories_Mars" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TinyPixel/air	--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string - name: category dtype: string - name: question_id dtype: float64 splits: - name: train num_bytes: 43852716 num_examples: 27729 download_size: 23510335 dataset_size: 43852716 --- # Dataset Card for "air" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
d0rj/OpenOrca-ru	--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 11568757682 num_examples: 4233923 download_size: 5699482220 dataset_size: 11568757682 size_categories: - 1M<n<10M language_creators: - translated language: - ru multilinguality: - monolingual pretty_name: Dolphin (ru) source_datasets: - Open-Orca/OpenOrca license: mit tags: - ChatGPT - instruct - instruct-tune task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation paperswithcode_id: orca-progressive-learning-from-complex --- # OpenOrca-ru ## Dataset Description - Paper: https://arxiv.org/abs/2306.02707 This is translated version of [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) into Russian.
distilled-one-sec-cv12-each-chunk-uniq/chunk_142	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1078700212.0 num_examples: 210191 download_size: 1104583624 dataset_size: 1078700212.0 --- # Dataset Card for "chunk_142" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
totally-not-an-llm/EverythingLM-data-V3	--- license: mit --- # EverythingLM V3 Dataset EverythingLM V3 is a diverse instruct dataset consisting of roughly 1.1k of sysprompt-user-assistant triads. These were generated using principles from both evol-instruct and Orca. The dataset encompasses a wide array of topics and interactions. ### Diferences from V2 * Used march gpt-4 instead of latest * Dynamically adjusted temperature based on the task * Much more diverse (8 new categories) * Flesch hints * 10% more data * Better filtering * Overall refined dataset generation pipeline ### Category distribution ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6464317c38083255f676ff38/J2TRUtw1p8M2NLouxwpyh.png) \*These values represent the data as generated, but slight filtering has been applied, so values might be a bit different.
hml707/test	--- license: apache-2.0 ---
kristmh/high_vs_randommin_100_issues_per_repo	--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validate path: data/validate-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text_clean dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 13172272 num_examples: 11681 - name: train num_bytes: 103353722 num_examples: 93441 - name: validate num_bytes: 13025230 num_examples: 11680 download_size: 61083853 dataset_size: 129551224 --- # Dataset Card for "high_vs_randommin_100_issues_per_repo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
japanese-asr/whisper_transcriptions.reazonspeech.all_60	--- dataset_info: config_name: all features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 29261046421.0 num_examples: 256966 download_size: 29028844193 dataset_size: 29261046421.0 configs: - config_name: all data_files: - split: train path: all/train-* ---
greathero/evenmorex13-newothersmallerthreeclass-newercontrailsvalidationdataset	--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 15125878.8 num_examples: 1800 download_size: 3155745 dataset_size: 15125878.8 configs: - config_name: default data_files: - split: train path: data/train-* ---
joshuasundance/mtg-coloridentity-multilabel-classification	--- dataset_info: features: - name: text dtype: string - name: labels sequence: int64 splits: - name: train num_bytes: 5011317.077050539 num_examples: 22208 - name: test num_bytes: 1253054.9229494615 num_examples: 5553 download_size: 2405205 dataset_size: 6264372 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: mit task_categories: - text-classification task_ids: - multi-label-classification language: - en tags: - mtg - multilabel - magic pretty_name: Magic the Gathering Color Identity Multilabel Classification size_categories: - 10K<n<100K --- This dataset was made specifically for multilabel classification using the following process: 1. Downloading https://mtgjson.com/api/v5/AtomicCards.json.bz2 on January 10, 2024 2. Encoding color identity of each card into the `labels` feature ```python colors = ['B', 'G', 'R', 'U', 'W'] b = [1, 0, 0, 0, 0] bw = [1, 0, 0, 0, 1] gru = [0, 1, 1, 1, 0] # and so on ``` 3. Concatenating card name and card text into the `text` feature 4. `split = ds['train'].train_test_split(test_size=0.2)` 5. `split.push_to_hub("mtg-coloridentity-multilabel-classification")`
TinyPixel/fish-1	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 4680929994 num_examples: 2840090 download_size: 2704444515 dataset_size: 4680929994 --- # Dataset Card for "fish-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jhaberbe/lipid-droplets-v2	--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 12799326.0 num_examples: 10 download_size: 12808424 dataset_size: 12799326.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
MicPie/unpredictable_cluster24	--- annotations_creators: - no-annotation language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual pretty_name: UnpredicTable-cluster24 size_categories: - 100K<n<1M source_datasets: [] task_categories: - multiple-choice - question-answering - zero-shot-classification - text2text-generation - table-question-answering - text-generation - text-classification - tabular-classification task_ids: - multiple-choice-qa - extractive-qa - open-domain-qa - closed-domain-qa - closed-book-qa - open-book-qa - language-modeling - multi-class-classification - natural-language-inference - topic-classification - multi-label-classification - tabular-multi-class-classification - tabular-multi-label-classification --- # Dataset Card for "UnpredicTable-cluster24" - Dataset of Few-shot Tasks from Tables ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://ethanperez.net/unpredictable - Repository: https://github.com/JunShern/few-shot-adaptation - Paper: Few-shot Adaptation Works with UnpredicTable Data - Point of Contact: junshern@nyu.edu, perez@nyu.edu ### Dataset Summary The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. There are several dataset versions available: * [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites. * [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites. * [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset. * UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings): * [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low) * [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium) * [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) * UnpredicTable data subsets based on the website of origin: * [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com) * [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net) * [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com) * [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com) * [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com) * [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com) * [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org) * [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org) * [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com) * [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com) * [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com) * [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com) * [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com) * [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com) * [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com) * [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com) * [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com) * [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org) * [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org) * [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org) * UnpredicTable data subsets based on clustering (for the clustering details please see our publication): * [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00) * [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01) * [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02) * [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03) * [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04) * [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05) * [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06) * [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07) * [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08) * [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09) * [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10) * [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11) * [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12) * [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13) * [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14) * [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15) * [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16) * [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17) * [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18) * [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19) * [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20) * [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21) * [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22) * [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23) * [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24) * [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25) * [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26) * [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27) * [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28) * [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29) * [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise) ### Supported Tasks and Leaderboards Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc. The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset. ### Languages English ## Dataset Structure ### Data Instances Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from. There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'. ### Data Fields 'task': task identifier 'input': column elements of a specific row in the table. 'options': for multiple choice classification, it provides the options to choose from. 'output': target column element of the same row as input. 'pageTitle': the title of the page containing the table. 'outputColName': output column name 'url': url to the website containing the table 'wdcFile': WDC Web Table Corpus file ### Data Splits The UnpredicTable datasets do not come with additional data splits. ## Dataset Creation ### Curation Rationale Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning. ### Source Data #### Initial Data Collection and Normalization We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline. #### Who are the source language producers? The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/). ### Annotations #### Annotation process Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low), [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication. #### Who are the annotators? Annotations were carried out by a lab assistant. ### Personal and Sensitive Information The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations. ### Discussion of Biases Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset. ### Other Known Limitations No additional known limitations. ## Additional Information ### Dataset Curators Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez ### Licensing Information Apache 2.0 ### Citation Information ``` @misc{chan2022few, author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan}, title = {Few-shot Adaptation Works with UnpredicTable Data}, publisher={arXiv}, year = {2022}, url = {https://arxiv.org/abs/2208.01009} } ```
danielpark/mquad-v1	--- license: apache-2.0 task_categories: - question-answering - text-generation language: - en - ko tags: - biology pretty_name: Medical domain QA dataset for training a medical chatbot. --- # MQuAD The Medical Question and Answering dataset(MQuAD) has been refined, including the following datasets. You can download it through the Hugging Face dataset. Use the DATASETS method as follows. ## Quick Guide ```python from datasets import load_dataset dataset = load_dataset("danielpark/MQuAD-v1") ``` Medical Q/A datasets gathered from the following websites. - eHealth Forum - iCliniq - Question Doctors - WebMD Data was gathered at the 5th of May 2017. The MQuAD provides embedded question and answer arrays in string format, so it is recommended to convert the string-formatted arrays into float format as follows. This measure has been applied to save resources and time used for embedding. ```python from datasets import load_dataset from utilfunction import col_convert import pandas as pd qa = load_dataset("danielpark/MQuAD-v1", "csv") df_qa = pd.DataFrame(qa['train']) df_qa = col_convert(df_qa, ['Q_FFNN_embeds', 'A_FFNN_embeds']) ```
cahya/instructions-test	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 16048 num_examples: 22 download_size: 15127 dataset_size: 16048 --- # Dataset Card for "instructions-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thefcraft/civitai-stable-diffusion-337k	--- annotations_creators: - no-annotation language_creators: - thefcraft language: - en pretty_name: civitai-stable-diffusion-337k size_categories: - 1M<n<10M source_datasets: - civitai --- ### How to Use ``` from datasets import load_dataset dataset = load_dataset("thefcraft/civitai-stable-diffusion-337k") print(dataset['train'][0]) ``` ### download images download zip files from images dir https://huggingface.co/datasets/thefcraft/civitai-stable-diffusion-337k/tree/main/images it contains some images with id ``` from zipfile import ZipFile with ZipFile("filename.zip", 'r') as zObject: zObject.extractall() ``` ### Dataset Summary GitHub URL:- https://github.com/thefcraft/civitai-stable-diffusion-337k dataset:- civitai-stable-diffusion-337k this dataset contains 337k civitai images url with prompts etc. i use civitai api to get all prompts. project:- https://github.com/thefcraft/nsfw-prompt-detection-sd I train a model on this dataset DATA STRUCTURE for othertype/civitai.json:- ```{ 'items':[ {'id': 100657, 'url': 'https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2338276a-87f7-4a1e-f92a-776a18ee4200/width=768/2338276a-87f7-4a1e-f92a-776a18ee4200.jpeg', 'hash': 'U5Exz_00.8D$t89Z%M0100~VD*RktQxaIU~p', 'width': 768, 'height': 1368, 'nsfw': True, 'createdAt': '2023-02-14T10:05:11.498Z', 'postId': 60841, 'stats': {'cryCount': 0, 'laughCount': 0, 'likeCount': 26, 'dislikeCount': 0, 'heartCount': 50, 'commentCount': 4}, 'meta': {'ENSD': '31337', 'Size': '512x912', 'seed': 3994946333, 'Model': 'AbyssOrangeMix2_sfw', 'steps': 20, 'prompt': '<lora:hiqcg_body-epoch-000004:0.5>, <lora:hiqcg_face-epoch-000004:0.4>, hiqcgbody, hiqcgface, 1girl, full body, standing, \ndetailed skin texture, detailed cloth texture, beautiful detailed face,\nmasterpiece, best quality, ultra detailed, 8k, intricate details,', 'sampler': 'DPM++ 2M Karras', 'cfgScale': 7, 'Clip skip': '2', 'resources': [{'hash': '038ba203d8', 'name': 'AbyssOrangeMix2_sfw', 'type': 'model'}], 'Model hash': '038ba203d8', 'Hires upscale': '1.5', 'Hires upscaler': 'Latent', 'negativePrompt': 'EasyNegative, extra fingers,fewer fingers, multiple girls, multiple views,', 'Denoising strength': '0.6'}, 'username': 'NeoClassicalRibbon'}, {..}, ..], 'metadata':{'totalItems': 327145} } ```
anderloh/MotorizedTransportSplit	--- dataset_info: - config_name: 2Class features: - name: audio dtype: audio - name: label dtype: class_label: names: '0': Helicopter '1': Racecar splits: - name: train num_bytes: 620401860.871 num_examples: 2769 - name: test num_bytes: 351989787.33 num_examples: 1571 download_size: 972417488 dataset_size: 972391648.201 - config_name: 5Class features: - name: audio dtype: audio - name: label dtype: class_label: names: '0': Helicopter '1': Jet '2': Racecar '3': Trains '4': Truck splits: - name: train num_bytes: 1860497842.936 num_examples: 8304 - name: test num_bytes: 761781853.0 num_examples: 3400 download_size: 2621469388 dataset_size: 2622279695.936 configs: - config_name: 2Class data_files: - split: train path: 2Class/train-* - split: test path: 2Class/test-* - config_name: 5Class data_files: - split: train path: 5Class/train-* - split: test path: 5Class/test-* --- # Dataset Card for "MotorizedTransportSplit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/yu_mei_ren_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yu_mei_ren/虞美人/虞美人 (Fate/Grand Order) This is the dataset of yu_mei_ren/虞美人/虞美人 (Fate/Grand Order), containing 500 images and their tags. The core tags of this character are `long_hair, brown_hair, breasts, very_long_hair, earrings, medium_breasts, red_eyes, ear_piercing, multiple_earrings, glasses, brown_eyes, braid, 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 \| 500 \| 825.54 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yu_mei_ren_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 702.32 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yu_mei_ren_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1256 \| 1.31 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/yu_mei_ren_fgo/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/yu_mei_ren_fgo', 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 \| 5 \| ![](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, black_dress, black_jacket, center_opening, choker, cleavage, collarbone, fur-trimmed_jacket, jewelry, open_jacket, piercing, revealing_clothes, solo, strapless_dress, looking_at_viewer, ribbon-trimmed_dress, simple_background, upper_body, bare_shoulders, hair_between_eyes, long_sleeves, open_mouth, white_background, closed_mouth, navel, sidelocks \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_dress, center_opening, choker, cleavage, collarbone, jewelry, long_sleeves, looking_at_viewer, navel, revealing_clothes, solo, strapless_dress, black_jacket, fur-trimmed_jacket, open_jacket, piercing, bare_shoulders, closed_mouth, ribbon-trimmed_dress, smile, hair_between_eyes, red_background, simple_background \| \| 2 \| 14 \| ![](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, bare_shoulders, black_dress, center_opening, choker, revealing_clothes, ribbon-trimmed_dress, solo, strapless_dress, black_gloves, collarbone, elbow_gloves, jewelry, cleavage, looking_at_viewer, navel, piercing, blush, closed_mouth, simple_background, white_background \| \| 3 \| 10 \| ![](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_dress, black_gloves, choker, elbow_gloves, holding_sword, jewelry, looking_at_viewer, piercing, revealing_clothes, solo, strapless_dress, bare_shoulders, center_opening, collarbone, closed_mouth, dual_wielding, navel, simple_background, cleavage, ribbon-trimmed_dress, white_background \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, black_dress, jewelry, long_sleeves, piercing, simple_background, solo, white_background, black_gloves, choker, cleavage, collarbone, fur-trimmed_jacket, looking_at_viewer, blush, closed_mouth, black_jacket, center_opening, fur_collar, head_rest, upper_body \| \| 5 \| 6 \| ![](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, black-framed_eyewear, black_dress, closed_mouth, elbow_gloves, jewelry, piercing, single_braid, solo, black_gloves, looking_at_viewer, choker \| \| 6 \| 9 \| ![](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, black-framed_eyewear, black_dress, blue_dress, ribbon-trimmed_dress, black_gloves, blush, elbow_gloves, ribbed_dress, single_braid, solo, arm_strap, center_opening, open_mouth, braided_ponytail, jewelry, layered_dress, looking_at_viewer, choker, piercing, strapless_dress, pinstripe_pattern \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, bare_shoulders, detached_collar, fake_animal_ears, jewelry, playboy_bunny, rabbit_ears, single_braid, solo, black-framed_eyewear, black_leotard, blush, bowtie, braided_ponytail, cleavage, highleg_leotard, looking_at_viewer, strapless_leotard, open_mouth, simple_background, thighs, white_background, wrist_cuffs, black_footwear, collarbone, covered_navel, fishnet_pantyhose, red_bow \| \| 8 \| 9 \| ![](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, bare_shoulders, cleavage, looking_at_viewer, navel, collarbone, jewelry, o-ring, solo, choker, single_braid, sling_bikini_top, blush, ocean, outdoors, smile, blue_sky, closed_mouth, day, piercing, beach, cloud, hair_scrunchie, hairclip, sitting, thighs, water \| \| 9 \| 23 \| ![](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) \| jewelry, 1girl, bare_shoulders, china_dress, braided_ponytail, looking_at_viewer, blue_dress, thighs, blush, brown_pantyhose, single_braid, solo, smile, looking_back, pelvic_curtain, tassel, panties, red_trim, black-framed_eyewear, sideboob \| \| 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, anus, ass, blush, from_behind, jewelry, looking_at_viewer, looking_back, pussy, solo, thighs, arm_strap, braided_ponytail, piercing, single_braid, sweat, barefoot, completely_nude, feet, lying, mosaic_censoring, pillow \| \| 11 \| 9 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1boy, 1girl, blush, hetero, navel, nipples, collarbone, open_mouth, penis, sweat, choker, jewelry, mosaic_censoring, sex, spread_legs, thighs, vaginal, nude, solo_focus, arm_strap, black_gloves, cowgirl_position, cum_in_pussy, elbow_gloves, girl_on_top, on_back \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_dress \| black_jacket \| center_opening \| choker \| cleavage \| collarbone \| fur-trimmed_jacket \| jewelry \| open_jacket \| piercing \| revealing_clothes \| solo \| strapless_dress \| looking_at_viewer \| ribbon-trimmed_dress \| simple_background \| upper_body \| bare_shoulders \| hair_between_eyes \| long_sleeves \| open_mouth \| white_background \| closed_mouth \| navel \| sidelocks \| smile \| red_background \| black_gloves \| elbow_gloves \| blush \| holding_sword \| dual_wielding \| fur_collar \| head_rest \| black-framed_eyewear \| single_braid \| blue_dress \| ribbed_dress \| arm_strap \| braided_ponytail \| layered_dress \| pinstripe_pattern \| detached_collar \| fake_animal_ears \| playboy_bunny \| rabbit_ears \| black_leotard \| bowtie \| highleg_leotard \| strapless_leotard \| thighs \| wrist_cuffs \| black_footwear \| covered_navel \| fishnet_pantyhose \| red_bow \| o-ring \| sling_bikini_top \| ocean \| outdoors \| blue_sky \| day \| beach \| cloud \| hair_scrunchie \| hairclip \| sitting \| water \| china_dress \| brown_pantyhose \| looking_back \| pelvic_curtain \| tassel \| panties \| red_trim \| sideboob \| anus \| ass \| from_behind \| pussy \| sweat \| barefoot \| completely_nude \| feet \| lying \| mosaic_censoring \| pillow \| 1boy \| hetero \| nipples \| penis \| sex \| spread_legs \| vaginal \| nude \| solo_focus \| cowgirl_position \| cum_in_pussy \| girl_on_top \| on_back \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:--------------\|:---------------\|:-----------------\|:---------\|:-----------\|:-------------\|:---------------------\|:----------\|:--------------\|:-----------\|:--------------------\|:-------\|:------------------\|:--------------------\|:-----------------------\|:--------------------\|:-------------\|:-----------------\|:--------------------\|:---------------\|:-------------\|:-------------------\|:---------------\|:--------\|:------------\|:--------\|:-----------------\|:---------------\|:---------------\|:--------\|:----------------\|:----------------\|:-------------\|:------------\|:-----------------------\|:---------------\|:-------------\|:---------------\|:------------\|:-------------------\|:----------------\|:--------------------\|:------------------\|:-------------------\|:----------------\|:--------------\|:----------------\|:---------\|:------------------\|:--------------------\|:---------\|:--------------\|:-----------------\|:----------------\|:--------------------\|:----------\|:---------\|:-------------------\|:--------\|:-----------\|:-----------\|:------\|:--------\|:--------\|:-----------------\|:-----------\|:----------\|:--------\|:--------------\|:------------------\|:---------------\|:-----------------\|:---------\|:----------\|:-----------\|:-----------\|:-------\|:------\|:--------------\|:--------\|:--------\|:-----------\|:------------------\|:-------\|:--------\|:-------------------\|:---------\|:-------\|:---------\|:----------\|:--------\|:------\|:--------------\|:----------\|:-------\|:-------------\|:-------------------\|:---------------\|:--------------\|:----------\| \| 0 \| 5 \| ![](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 \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 14 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| X \| X \| X \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| \| \| X \| X \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 10 \| ![](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 \| X \| \| X \| \| \| \| X \| X \| X \| \| \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| \| X \| \| X \| X \| \| \| X \| \| X \| X \| \| \| \| \| X \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 6 \| ![](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 \| 9 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| \| X \| X \| \| \| \| X \| \| X \| \| X \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| \| \| X \| X \| \| X \| \| \| \| X \| \| X \| \| X \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 9 \| ![](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 \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 23 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| 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 \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 9 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| X \| \| \| \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
mahdibaghbanzadeh/GUE_tf_4	--- dataset_info: features: - name: sequence dtype: string - name: labels dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 2147000 num_examples: 19000 - name: val num_bytes: 113000 num_examples: 1000 - name: test num_bytes: 113000 num_examples: 1000 download_size: 1070830 dataset_size: 2373000 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880	--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices num_bytes: 692068 num_examples: 1880 - name: fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices num_bytes: 1329559 num_examples: 1880 - name: fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 1330297 num_examples: 1880 download_size: 997973 dataset_size: 3351924 --- # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Vitorbr2009/voz-indio-treinada	--- license: openrail ---
cmeraki/hindi_eval_general_mcq	--- language: - hi license: cc size_categories: - 1K<n<10K task_categories: - question-answering dataset_info: features: - name: QUESTION dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: TARGET dtype: string - name: SUBJECT dtype: string - name: GRADE dtype: string - name: TOPIC dtype: string - name: SPLIT dtype: string splits: - name: train num_bytes: 881272.5942028986 num_examples: 1653 download_size: 380835 dataset_size: 881272.5942028986 configs: - config_name: default data_files: - split: train path: data/train-* tags: - maths - physics ---
ShoukanLabs/OpenNiji-380001_415000	--- dataset_info: features: - name: image dtype: image - name: url dtype: string - name: prompt dtype: string - name: style dtype: string splits: - name: train num_bytes: 56237791750.702 num_examples: 34999 download_size: 0 dataset_size: 56237791750.702 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "OpenNiji-380001_415000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alpayariyak/something	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 62321431 num_examples: 56037 download_size: 30816818 dataset_size: 62321431 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "something" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yuvalkirstain/PickaPic-ft-pairwise	--- dataset_info: features: - name: good_image dtype: image - name: bad_image dtype: image - name: text dtype: string - name: good_url dtype: string - name: bad_url dtype: string splits: - name: train num_bytes: 8329737112.221633 num_examples: 10313 - name: test num_bytes: 347307956.7783673 num_examples: 430 download_size: 8675583639 dataset_size: 8677045069.0 --- # Dataset Card for "PickaPic-ft-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yardeny/processed_bert_context_len_128	--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 12001893540.0 num_examples: 15387043 download_size: 4048532411 dataset_size: 12001893540.0 --- # Dataset Card for "processed_bert_context_len_128" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
manot/pothole-segmentation	--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface --- <div align="center"> <img width="640" alt="manot/pothole-segmentation" src="https://huggingface.co/datasets/manot/pothole-segmentation/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['potholes', 'object', 'pothole', 'potholes'] ``` ### Number of Images ```json {'valid': 157, 'test': 80, 'train': 582} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("manot/pothole-segmentation", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d/dataset/3](https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d/dataset/3?ref=roboflow2huggingface) ### Citation ``` @misc{ road-damage-xvt2d_dataset, title = { road damage Dataset }, type = { Open Source Dataset }, author = { abdulmohsen fahad }, howpublished = { \\url{ https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d } }, url = { https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { jun }, note = { visited on 2023-06-13 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on June 13, 2023 at 8:47 AM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com The dataset includes 819 images. Potholes are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.
psroy/mini-platypus-two	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245925 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA

--- pretty_name: Evaluation run of adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA)\ \ 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_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T16:09:42.767487](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA/blob/main/results_2024-01-27T16-09-42.767487.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\": 0.7413605787370283,\n\ \ \"acc_stderr\": 0.02895069135836259,\n \"acc_norm\": 0.7476488274301629,\n\ \ \"acc_norm_stderr\": 0.029481162291123596,\n \"mc1\": 0.40758873929008566,\n\ \ \"mc1_stderr\": 0.017201949234553107,\n \"mc2\": 0.5708422092704679,\n\ \ \"mc2_stderr\": 0.015184723749426742\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759091,\n\ \ \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.01384746051889298\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6420035849432384,\n\ \ \"acc_stderr\": 0.0047843129724954,\n \"acc_norm\": 0.8388767177853017,\n\ \ \"acc_norm_stderr\": 0.003668932629672556\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.868421052631579,\n \"acc_stderr\": 0.027508689533549915,\n\ \ \"acc_norm\": 0.868421052631579,\n \"acc_norm_stderr\": 0.027508689533549915\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7962264150943397,\n \"acc_stderr\": 0.024790784501775402,\n\ \ \"acc_norm\": 0.7962264150943397,\n \"acc_norm_stderr\": 0.024790784501775402\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.02628055093284806,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.02628055093284806\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.7167630057803468,\n\ \ \"acc_stderr\": 0.034355680560478746,\n \"acc_norm\": 0.7167630057803468,\n\ \ \"acc_norm_stderr\": 0.034355680560478746\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367406,\n\ \ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367406\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7531914893617021,\n \"acc_stderr\": 0.02818544130123409,\n\ \ \"acc_norm\": 0.7531914893617021,\n \"acc_norm_stderr\": 0.02818544130123409\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7310344827586207,\n \"acc_stderr\": 0.036951833116502325,\n\ \ \"acc_norm\": 0.7310344827586207,\n \"acc_norm_stderr\": 0.036951833116502325\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.6666666666666666,\n \"acc_stderr\": 0.0242785680243077,\n \"acc_norm\"\ : 0.6666666666666666,\n \"acc_norm_stderr\": 0.0242785680243077\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5238095238095238,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.5238095238095238,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8870967741935484,\n\ \ \"acc_stderr\": 0.01800360332586361,\n \"acc_norm\": 0.8870967741935484,\n\ \ \"acc_norm_stderr\": 0.01800360332586361\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6502463054187192,\n \"acc_stderr\": 0.03355400904969566,\n\ \ \"acc_norm\": 0.6502463054187192,\n \"acc_norm_stderr\": 0.03355400904969566\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\"\ : 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527029,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527029\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8051282051282052,\n \"acc_stderr\": 0.020083167595181393,\n\ \ \"acc_norm\": 0.8051282051282052,\n \"acc_norm_stderr\": 0.020083167595181393\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3888888888888889,\n \"acc_stderr\": 0.029723278961476668,\n \ \ \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.029723278961476668\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8403361344537815,\n \"acc_stderr\": 0.023793353997528802,\n\ \ \"acc_norm\": 0.8403361344537815,\n \"acc_norm_stderr\": 0.023793353997528802\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5033112582781457,\n \"acc_stderr\": 0.04082393379449654,\n \"\ acc_norm\": 0.5033112582781457,\n \"acc_norm_stderr\": 0.04082393379449654\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9119266055045872,\n \"acc_stderr\": 0.01215074371948165,\n \"\ acc_norm\": 0.9119266055045872,\n \"acc_norm_stderr\": 0.01215074371948165\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6527777777777778,\n \"acc_stderr\": 0.032468872436376486,\n \"\ acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.032468872436376486\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8970588235294118,\n \"acc_stderr\": 0.02132833757080437,\n \"\ acc_norm\": 0.8970588235294118,\n \"acc_norm_stderr\": 0.02132833757080437\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065498,\n \ \ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065498\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7713004484304933,\n\ \ \"acc_stderr\": 0.028188240046929203,\n \"acc_norm\": 0.7713004484304933,\n\ \ \"acc_norm_stderr\": 0.028188240046929203\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515375,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515375\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622804,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622804\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8796296296296297,\n\ \ \"acc_stderr\": 0.03145703854306251,\n \"acc_norm\": 0.8796296296296297,\n\ \ \"acc_norm_stderr\": 0.03145703854306251\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8588957055214724,\n \"acc_stderr\": 0.027351605518389752,\n\ \ \"acc_norm\": 0.8588957055214724,\n \"acc_norm_stderr\": 0.027351605518389752\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5446428571428571,\n\ \ \"acc_stderr\": 0.04726835553719098,\n \"acc_norm\": 0.5446428571428571,\n\ \ \"acc_norm_stderr\": 0.04726835553719098\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.0339329572976101,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.0339329572976101\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.01553751426325388,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.01553751426325388\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8876117496807152,\n\ \ \"acc_stderr\": 0.011294541351216554,\n \"acc_norm\": 0.8876117496807152,\n\ \ \"acc_norm_stderr\": 0.011294541351216554\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8208092485549133,\n \"acc_stderr\": 0.020647590029679332,\n\ \ \"acc_norm\": 0.8208092485549133,\n \"acc_norm_stderr\": 0.020647590029679332\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7150837988826816,\n\ \ \"acc_stderr\": 0.015096222302469802,\n \"acc_norm\": 0.7150837988826816,\n\ \ \"acc_norm_stderr\": 0.015096222302469802\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8464052287581699,\n \"acc_stderr\": 0.02064559791041877,\n\ \ \"acc_norm\": 0.8464052287581699,\n \"acc_norm_stderr\": 0.02064559791041877\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\": 0.8148148148148148,\n \"acc_stderr\": 0.021613809395224812,\n\ \ \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.021613809395224812\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6170212765957447,\n \"acc_stderr\": 0.028999080904806185,\n \ \ \"acc_norm\": 0.6170212765957447,\n \"acc_norm_stderr\": 0.028999080904806185\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5827900912646675,\n\ \ \"acc_stderr\": 0.012593959992906427,\n \"acc_norm\": 0.5827900912646675,\n\ \ \"acc_norm_stderr\": 0.012593959992906427\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.023709788253811766,\n \ \ \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.023709788253811766\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8088235294117647,\n \"acc_stderr\": 0.015908290136278043,\n \ \ \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.015908290136278043\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\": 0.8285714285714286,\n \"acc_stderr\": 0.02412746346265016,\n\ \ \"acc_norm\": 0.8285714285714286,\n \"acc_norm_stderr\": 0.02412746346265016\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.9104477611940298,\n\ \ \"acc_stderr\": 0.02019067053502792,\n \"acc_norm\": 0.9104477611940298,\n\ \ \"acc_norm_stderr\": 0.02019067053502792\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.0261682213446623,\n\ \ \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.0261682213446623\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40758873929008566,\n\ \ \"mc1_stderr\": 0.017201949234553107,\n \"mc2\": 0.5708422092704679,\n\ \ \"mc2_stderr\": 0.015184723749426742\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.011508957690722764\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5549658832448825,\n \ \ \"acc_stderr\": 0.0136890115674142\n }\n}\n```" repo_url: https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA 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_27T16_09_42.767487 path: - '**/details_harness|arc:challenge|25_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-27T16-09-42.767487.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|gsm8k|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hellaswag|10_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T16-09-42.767487.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T16-09-42.767487.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T16-09-42.767487.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T16_09_42.767487 path: - '**/details_harness|winogrande|5_2024-01-27T16-09-42.767487.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-27T16-09-42.767487.parquet' - config_name: results data_files: - split: 2024_01_27T16_09_42.767487 path: - results_2024-01-27T16-09-42.767487.parquet - split: latest path: - results_2024-01-27T16-09-42.767487.parquet --- # Dataset Card for Evaluation run of adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA  Dataset automatically created during the evaluation run of model [adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-RAW-2301-LoRA) 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_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T16:09:42.767487](https://huggingface.co/datasets/open-llm-leaderboard/details_adamo1139__Yi-34B-200K-AEZAKMI-RAW-2301-LoRA/blob/main/results_2024-01-27T16-09-42.767487.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": 0.7413605787370283, "acc_stderr": 0.02895069135836259, "acc_norm": 0.7476488274301629, "acc_norm_stderr": 0.029481162291123596, "mc1": 0.40758873929008566, "mc1_stderr": 0.017201949234553107, "mc2": 0.5708422092704679, "mc2_stderr": 0.015184723749426742 }, "harness|arc:challenge|25": { "acc": 0.6262798634812287, "acc_stderr": 0.014137708601759091, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.01384746051889298 }, "harness|hellaswag|10": { "acc": 0.6420035849432384, "acc_stderr": 0.0047843129724954, "acc_norm": 0.8388767177853017, "acc_norm_stderr": 0.003668932629672556 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7037037037037037, "acc_stderr": 0.03944624162501116, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.03944624162501116 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.868421052631579, "acc_stderr": 0.027508689533549915, "acc_norm": 0.868421052631579, "acc_norm_stderr": 0.027508689533549915 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7962264150943397, "acc_stderr": 0.024790784501775402, "acc_norm": 0.7962264150943397, "acc_norm_stderr": 0.024790784501775402 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8888888888888888, "acc_stderr": 0.02628055093284806, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.02628055093284806 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7167630057803468, "acc_stderr": 0.034355680560478746, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.034355680560478746 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5196078431372549, "acc_stderr": 0.04971358884367406, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.04971358884367406 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7531914893617021, "acc_stderr": 0.02818544130123409, "acc_norm": 0.7531914893617021, "acc_norm_stderr": 0.02818544130123409 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7310344827586207, "acc_stderr": 0.036951833116502325, "acc_norm": 0.7310344827586207, "acc_norm_stderr": 0.036951833116502325 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.0242785680243077, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.0242785680243077 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5238095238095238, "acc_stderr": 0.04467062628403273, "acc_norm": 0.5238095238095238, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8870967741935484, "acc_stderr": 0.01800360332586361, "acc_norm": 0.8870967741935484, "acc_norm_stderr": 0.01800360332586361 }, 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"harness|hendrycksTest-prehistory|5": { "acc": 0.8148148148148148, "acc_stderr": 0.021613809395224812, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.021613809395224812 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6170212765957447, "acc_stderr": 0.028999080904806185, "acc_norm": 0.6170212765957447, "acc_norm_stderr": 0.028999080904806185 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5827900912646675, "acc_stderr": 0.012593959992906427, "acc_norm": 0.5827900912646675, "acc_norm_stderr": 0.012593959992906427 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8125, "acc_stderr": 0.023709788253811766, "acc_norm": 0.8125, "acc_norm_stderr": 0.023709788253811766 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8088235294117647, "acc_stderr": 0.015908290136278043, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.015908290136278043 }, "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": 0.8285714285714286, "acc_stderr": 0.02412746346265016, "acc_norm": 0.8285714285714286, "acc_norm_stderr": 0.02412746346265016 }, "harness|hendrycksTest-sociology|5": { "acc": 0.9104477611940298, "acc_stderr": 0.02019067053502792, "acc_norm": 0.9104477611940298, "acc_norm_stderr": 0.02019067053502792 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8654970760233918, "acc_stderr": 0.0261682213446623, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.0261682213446623 }, "harness|truthfulqa:mc|0": { "mc1": 0.40758873929008566, "mc1_stderr": 0.017201949234553107, "mc2": 0.5708422092704679, "mc2_stderr": 0.015184723749426742 }, "harness|winogrande|5": { "acc": 0.7868981846882399, "acc_stderr": 0.011508957690722764 }, "harness|gsm8k|5": { "acc": 0.5549658832448825, "acc_stderr": 0.0136890115674142 } } ``` ## 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]

bubl-ai/story_with_synthetic_test_set

--- license: mit ---

freshpearYoon/val_free_6

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 8745352416 num_examples: 9105 download_size: 1361253032 dataset_size: 8745352416 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/k3_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of k3/K3/K3 (Girls' Frontline) This is the dataset of k3/K3/K3 (Girls' Frontline), containing 24 images and their tags. The core tags of this character are `breasts, hairband, large_breasts, grey_hair, long_hair, grey_eyes, headband, bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 24 | 24.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 24 | 14.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 53 | 29.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 24 | 21.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 53 | 41.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/k3_girlsfrontline/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/k3_girlsfrontline', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, open_mouth, cleavage, collarbone, blush, looking_at_viewer, navel, sports_bra, simple_background, smile, jacket, sweat, pants, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | open_mouth | cleavage | collarbone | blush | looking_at_viewer | navel | sports_bra | simple_background | smile | jacket | sweat | pants | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------|:-----------|:-------------|:--------|:--------------------|:--------|:-------------|:--------------------|:--------|:---------|:--------|:--------|:-------------------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

result-kand2-sdxl-wuerst-karlo/86947388

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 168 num_examples: 10 download_size: 1325 dataset_size: 168 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "86947388" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

huggingface/autotrain-data-4v9x-3cwh-jsa8

Invalid username or password.

autoevaluate/autoeval-staging-eval-project-Blaise-g__SumPubmed-3c512f6e-12265640

--- type: predictions tags: - autotrain - evaluation datasets: - Blaise-g/SumPubmed eval_info: task: summarization model: Blaise-g/led_large_baseline_pubmed metrics: ['bertscore'] dataset_name: Blaise-g/SumPubmed dataset_config: Blaise-g--SumPubmed dataset_split: test col_mapping: text: text target: abstract --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: Blaise-g/led_large_baseline_pubmed * Dataset: Blaise-g/SumPubmed * Config: Blaise-g--SumPubmed * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Blaise-g](https://huggingface.co/Blaise-g) for evaluating this model.

temiy/cat-simple

--- license: apache-2.0 ---

yuejunasia/haaudio

--- license: other --- 这是一个ha测试数据集!

bigbio/psytar

--- language: - en bigbio_language: - English license: cc-by-4.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_4p0 pretty_name: PsyTAR homepage: https://www.askapatient.com/research/pharmacovigilance/corpus-ades-psychiatric-medications.asp bigbio_pubmed: False bigbio_public: False bigbio_tasks: - NAMED_ENTITY_RECOGNITION - TEXT_CLASSIFICATION --- # Dataset Card for PsyTAR ## Dataset Description - **Homepage:** https://www.askapatient.com/research/pharmacovigilance/corpus-ades-psychiatric-medications.asp - **Pubmed:** False - **Public:** False - **Tasks:** NER,TXTCLASS The "Psychiatric Treatment Adverse Reactions" (PsyTAR) dataset contains 891 drugs reviews posted by patients on "askapatient.com", about the effectiveness and adverse drug events associated with Zoloft, Lexapro, Cymbalta, and Effexor XR. This dataset can be used for (multi-label) sentence classification of Adverse Drug Reaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug Indications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF) and Others, as well as for recognition of 5 different types of named entity (in the categories ADRs, WDs, SSIs and DIs) ## Citation Information ``` @article{Zolnoori2019, author = {Maryam Zolnoori and Kin Wah Fung and Timothy B. Patrick and Paul Fontelo and Hadi Kharrazi and Anthony Faiola and Yi Shuan Shirley Wu and Christina E. Eldredge and Jake Luo and Mike Conway and Jiaxi Zhu and Soo Kyung Park and Kelly Xu and Hamideh Moayyed and Somaieh Goudarzvand}, title = {A systematic approach for developing a corpus of patient reported adverse drug events: A case study for {SSRI} and {SNRI} medications}, journal = {Journal of Biomedical Informatics}, volume = {90}, year = {2019}, url = {https://doi.org/10.1016/j.jbi.2018.12.005}, doi = {10.1016/j.jbi.2018.12.005}, } ```

DeepFoldProtein/Foldseek_over70_proteome_UniDoc_test

--- dataset_info: features: - name: uniprotAccession dtype: string - name: domain sequence: sequence: int64 - name: ndom dtype: int64 - name: taxId dtype: string - name: uniprotSequence dtype: string splits: - name: train num_bytes: 4002 num_examples: 12 download_size: 7272 dataset_size: 4002 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvarochelo/dataset_nautical

--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 908645884.0 num_examples: 239 download_size: 875628182 dataset_size: 908645884.0 --- # Dataset Card for "dataset_nautical" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tyzhu/find_first_sent_train_50_eval_10_recite

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 170837 num_examples: 110 - name: validation num_bytes: 15661 num_examples: 10 download_size: 0 dataset_size: 186498 --- # Dataset Card for "find_first_sent_train_50_eval_10_recite" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nlp-brin-id/pos_pairs

--- license: apache-2.0 ---

wwydmanski/metagenomic_curated

--- license: artistic-2.0 --- # Metagenomic curated data This is a python repack of the curated data from the [Metagenomic Data Repository](https://waldronlab.io/curatedMetagenomicData/). Please refer to the [study list](https://experimenthub.bioconductor.org/package/curatedMetagenomicData) and [study metadata](https://waldronlab.io/curatedMetagenomicData/articles/available-studies.html) for the list of available datasets. ## Sample usage ```python ds = datasets.load_dataset("wwydmanski/metagenomic_curated", "EH1726") X = np.array([list(i.values()) for i in ds['train']['features']]) y = np.array([x['study_condition'] for x in ds['train']['metadata']]) ``` ## Finding a relevant dataset EHID The easiest way to find an interesting study is via [study metadata](https://waldronlab.io/curatedMetagenomicData/articles/available-studies.html). After that, you can find corresponding EHIDs by referring on the https://experimenthub.bioconductor.org/title/{study_name} page. Let's say that the ThomasAM_2018a study piqued your curiosity - it means that you will be able to find all relevant datasets on the [https://experimenthub.bioconductor.org/title/ThomasAM_2018a](https://experimenthub.bioconductor.org/title/ThomasAM_2018a) website.

AdapterOcean/gptindex-standardized_cluster_0

--- 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: 10931345 num_examples: 1234 download_size: 2937820 dataset_size: 10931345 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gptindex-standardized_cluster_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lipi17/building-cracks

--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface --- <div align="center"> <img width="640" alt="lipi17/building-cracks" src="https://huggingface.co/datasets/lipi17/building-cracks/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['crack'] ``` ### Number of Images ```json {'valid': 433, 'test': 211, 'train': 1490} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("lipi17/building-cracks", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg/dataset/2](https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg/dataset/2?ref=roboflow2huggingface) ### Citation ``` @misc{ crack-detection-y5kyg_dataset, title = { Crack Detection Dataset }, type = { Open Source Dataset }, author = { António Raimundo }, howpublished = { \\url{ https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg } }, url = { https://universe.roboflow.com/antonio-raimundo/crack-detection-y5kyg }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { feb }, note = { visited on 2023-10-21 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on February 10, 2023 at 3:51 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com The dataset includes 2134 images. Soil are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) No image augmentation techniques were applied.

vmathur87/llm-support

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 717288.0 num_examples: 242 - name: test num_bytes: 59280.0 num_examples: 20 download_size: 338197 dataset_size: 776568.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Shularp/Process_tested-Shularp-Process_tested-facebook-floresarb_Arab_to_eng_Latn

--- dataset_info: features: - name: translation struct: - name: ar dtype: string - name: en dtype: string - name: id sequence: int64 splits: - name: train num_bytes: 361758 num_examples: 997 - name: test num_bytes: 379791 num_examples: 1012 download_size: 412821 dataset_size: 741549 --- # Dataset Card for "Process_tested-Shularp-Process_tested-facebook-floresarb_Arab_to_eng_Latn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

atiranela/rodrigo

--- license: openrail ---

chishiki-ai/cnn-course

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

Lhaippp/DMHomo

--- license: mit ---

open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG

--- pretty_name: Evaluation run of LTC-AI-Labs/L2-7b-Base-test-WVG dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LTC-AI-Labs/L2-7b-Base-test-WVG](https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG)\ \ 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_LTC-AI-Labs__L2-7b-Base-test-WVG\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T07:52:27.189086](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG/blob/main/results_2023-10-28T07-52-27.189086.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\": 0.003145973154362416,\n\ \ \"em_stderr\": 0.0005734993648436373,\n \"f1\": 0.07481229026845672,\n\ \ \"f1_stderr\": 0.0016422896702234556,\n \"acc\": 0.40267285313968093,\n\ \ \"acc_stderr\": 0.00970555723399882\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436373,\n\ \ \"f1\": 0.07481229026845672,\n \"f1_stderr\": 0.0016422896702234556\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06974981046247157,\n \ \ \"acc_stderr\": 0.007016389571013843\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7355958958168903,\n \"acc_stderr\": 0.012394724896983799\n\ \ }\n}\n```" repo_url: https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG 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_03T17_19_09.186622 path: - '**/details_harness|arc:challenge|25_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-03T17-19-09.186622.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T06_36_16.797528 path: - '**/details_harness|drop|3_2023-10-28T06-36-16.797528.parquet' - split: 2023_10_28T07_52_27.189086 path: - '**/details_harness|drop|3_2023-10-28T07-52-27.189086.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T07-52-27.189086.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T06_36_16.797528 path: - '**/details_harness|gsm8k|5_2023-10-28T06-36-16.797528.parquet' - split: 2023_10_28T07_52_27.189086 path: - '**/details_harness|gsm8k|5_2023-10-28T07-52-27.189086.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T07-52-27.189086.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hellaswag|10_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-19-09.186622.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-management|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T17-19-09.186622.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_03T17_19_09.186622 path: - '**/details_harness|truthfulqa:mc|0_2023-10-03T17-19-09.186622.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-03T17-19-09.186622.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T06_36_16.797528 path: - '**/details_harness|winogrande|5_2023-10-28T06-36-16.797528.parquet' - split: 2023_10_28T07_52_27.189086 path: - '**/details_harness|winogrande|5_2023-10-28T07-52-27.189086.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T07-52-27.189086.parquet' - config_name: results data_files: - split: 2023_10_03T17_19_09.186622 path: - results_2023-10-03T17-19-09.186622.parquet - split: 2023_10_28T06_36_16.797528 path: - results_2023-10-28T06-36-16.797528.parquet - split: 2023_10_28T07_52_27.189086 path: - results_2023-10-28T07-52-27.189086.parquet - split: latest path: - results_2023-10-28T07-52-27.189086.parquet --- # Dataset Card for Evaluation run of LTC-AI-Labs/L2-7b-Base-test-WVG ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG - **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 [LTC-AI-Labs/L2-7b-Base-test-WVG](https://huggingface.co/LTC-AI-Labs/L2-7b-Base-test-WVG) 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_LTC-AI-Labs__L2-7b-Base-test-WVG", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T07:52:27.189086](https://huggingface.co/datasets/open-llm-leaderboard/details_LTC-AI-Labs__L2-7b-Base-test-WVG/blob/main/results_2023-10-28T07-52-27.189086.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": 0.003145973154362416, "em_stderr": 0.0005734993648436373, "f1": 0.07481229026845672, "f1_stderr": 0.0016422896702234556, "acc": 0.40267285313968093, "acc_stderr": 0.00970555723399882 }, "harness|drop|3": { "em": 0.003145973154362416, "em_stderr": 0.0005734993648436373, "f1": 0.07481229026845672, "f1_stderr": 0.0016422896702234556 }, "harness|gsm8k|5": { "acc": 0.06974981046247157, "acc_stderr": 0.007016389571013843 }, "harness|winogrande|5": { "acc": 0.7355958958168903, "acc_stderr": 0.012394724896983799 } } ``` ### 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]

kpriyanshu256/MultiTabQA-multitable_pretraining-valid-v2

--- dataset_info: features: - name: query dtype: string - name: table_names sequence: string - name: tables sequence: string - name: answer dtype: string - name: source dtype: string - name: target dtype: string - name: source_latex dtype: string - name: target_latex dtype: string - name: source_html dtype: string - name: target_html dtype: string - name: source_markdown dtype: string - name: target_markdown dtype: string splits: - name: train num_bytes: 853236383 num_examples: 536 download_size: 207209865 dataset_size: 853236383 configs: - config_name: default data_files: - split: train path: data/train-* ---

w95/fin

--- configs: - config_name: default data_files: - split: train path: train.jsonl dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string ---

Deojoandco/capstone_fromgpt_without_gold

--- dataset_info: features: - name: dialog_id dtype: int64 - name: dialogue dtype: string - name: summary dtype: string - name: gold_tags dtype: string - name: query dtype: string - name: gpt_success dtype: bool - name: gpt_response dtype: string - name: gold_tags_tokens_count dtype: int64 - name: GPT_OUTPUT_FOUND dtype: bool - name: gpt_output_tags dtype: string - name: gpt_output_tag_tokens dtype: int64 - name: summary_gpt_token_count_match dtype: bool - name: gpt_output_token_count dtype: int64 - name: gpt_output_tag_count dtype: int64 - name: summary_gpt_tags_token_count_match dtype: bool splits: - name: test num_bytes: 57588 num_examples: 12 download_size: 30674 dataset_size: 57588 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "capstone_fromgpt_without_gold" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Abhinav-B/finetune_llama_wikisql

--- dataset_info: features: - name: formatted_text dtype: string splits: - name: train num_bytes: 1530908 num_examples: 10000 download_size: 703398 dataset_size: 1530908 configs: - config_name: default data_files: - split: train path: data/train-* ---

dexhrestha/annomi-sample

--- dataset_info: features: - name: text dtype: string - name: len dtype: int64 splits: - name: train num_bytes: 734843.2554793004 num_examples: 7461 - name: validation num_bytes: 81747.74186406907 num_examples: 830 download_size: 611225 dataset_size: 816590.9973433695 --- # Dataset Card for "annomi-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sjonas50/test

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

Ironov/abstract

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1245046.0 num_examples: 409 download_size: 1254490 dataset_size: 1245046.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Rowan/hellaswag

--- language: - en paperswithcode_id: hellaswag pretty_name: HellaSwag dataset_info: features: - name: ind dtype: int32 - name: activity_label dtype: string - name: ctx_a dtype: string - name: ctx_b dtype: string - name: ctx dtype: string - name: endings sequence: string - name: source_id dtype: string - name: split dtype: string - name: split_type dtype: string - name: label dtype: string splits: - name: train num_bytes: 43232624 num_examples: 39905 - name: test num_bytes: 10791853 num_examples: 10003 - name: validation num_bytes: 11175717 num_examples: 10042 download_size: 71494896 dataset_size: 65200194 --- # Dataset Card for "hellaswag" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://rowanzellers.com/hellaswag/](https://rowanzellers.com/hellaswag/) - **Repository:** [https://github.com/rowanz/hellaswag/](https://github.com/rowanz/hellaswag/) - **Paper:** [HellaSwag: Can a Machine Really Finish Your Sentence?](https://arxiv.org/abs/1905.07830) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 71.49 MB - **Size of the generated dataset:** 65.32 MB - **Total amount of disk used:** 136.81 MB ### Dataset Summary HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### default - **Size of downloaded dataset files:** 71.49 MB - **Size of the generated dataset:** 65.32 MB - **Total amount of disk used:** 136.81 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "activity_label": "Removing ice from car", "ctx": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles. then", "ctx_a": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles.", "ctx_b": "then", "endings": "[\", the man adds wax to the windshield and cuts it.\", \", a person board a ski lift, while two men supporting the head of the per...", "ind": 4, "label": "3", "source_id": "activitynet~v_-1IBHYS3L-Y", "split": "train", "split_type": "indomain" } ``` ### Data Fields The data fields are the same among all splits. #### default - `ind`: a `int32` feature. - `activity_label`: a `string` feature. - `ctx_a`: a `string` feature. - `ctx_b`: a `string` feature. - `ctx`: a `string` feature. - `endings`: a `list` of `string` features. - `source_id`: a `string` feature. - `split`: a `string` feature. - `split_type`: a `string` feature. - `label`: a `string` feature. ### Data Splits | name |train|validation|test | |-------|----:|---------:|----:| |default|39905| 10042|10003| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information MIT https://github.com/rowanz/hellaswag/blob/master/LICENSE ### Citation Information ``` @inproceedings{zellers2019hellaswag, title={HellaSwag: Can a Machine Really Finish Your Sentence?}, author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin}, booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year={2019} } ``` ### Contributions Thanks to [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

ag2428/reasoningDataV4

--- dataset_info: features: - name: instruction dtype: string - name: answer dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 2481669221 num_examples: 2062854 download_size: 1500063761 dataset_size: 2481669221 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "reasoningDataV4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

paoloitaliani/news_articles

--- dataset_info: - config_name: corriere_autunno features: - name: author dtype: string - name: journal dtype: string - name: body dtype: string - name: date dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 339578 num_examples: 90 download_size: 237083 dataset_size: 339578 - config_name: corriere_primavera features: - name: author dtype: string - name: journal dtype: string - name: body dtype: string - name: date dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 319422 num_examples: 105 download_size: 206264 dataset_size: 319422 - config_name: fattoq_autunno features: - name: author dtype: string - name: journal dtype: string - name: body dtype: string - name: date dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 519012 num_examples: 133 download_size: 338948 dataset_size: 519012 - config_name: fattoq_primavera features: - name: author dtype: string - name: journal dtype: string - name: body dtype: string - name: date dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 508621 num_examples: 152 download_size: 331977 dataset_size: 508621 - config_name: ukraine features: - name: date dtype: timestamp[ns] - name: body dtype: string - name: author dtype: string - name: journal dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 81923456 num_examples: 27449 download_size: 0 dataset_size: 81923456 configs: - config_name: corriere_autunno data_files: - split: train path: corriere_autunno/train-* - config_name: corriere_primavera data_files: - split: train path: corriere_primavera/train-* - config_name: fattoq_autunno data_files: - split: train path: fattoq_autunno/train-* - config_name: fattoq_primavera data_files: - split: train path: fattoq_primavera/train-* - config_name: ukraine data_files: - split: train path: ukraine/train-* --- # Dataset Card for "news_articles" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nirantk/french-books

--- dataset_info: features: - name: file_id dtype: string - name: ocr dtype: string - name: title dtype: string - name: date dtype: string - name: author dtype: string - name: page_count dtype: int64 - name: word_count dtype: int64 - name: character_count dtype: int64 - name: complete_text dtype: string splits: - name: train num_bytes: 427823810 num_examples: 1101 download_size: 246984465 dataset_size: 427823810 configs: - config_name: default data_files: - split: train path: data/train-* ---

nlpai-lab/kullm-v2

--- license: apache-2.0 task_categories: - text-generation language: - ko pretty_name: kullm size_categories: - 10K<n<100K --- # Dataset Card for "KULLM-v2" ## Dataset Summary Korean translation of GPT4ALL, Dolly, and Vicuna data. repository: [nlpai-lab/KULLM](https://github.com/nlpai-lab/KULLM) huggingface: [nlpai-lab/kullm-v2](https://huggingface.co/nlpai-lab/kullm-polyglot-12.8b-v2) #### Translate dataset Translated 'instruction', 'input', and 'output' in the dataset via the DeepL API ## Lisence Apache-2.0 ```python >>> from datasets import load_dataset >>> ds = load_dataset("nlpai-lab/kullm-v2", split="train") >>> ds DatasetDict({ train: Dataset({ features: ['id', 'instruction', 'input', 'output'], num_rows: 152630 }) }) ``` ```python >>> ds[0] {'id': 'alpaca_{idx}', 'instruction': '3원색이란 무엇인가요?', 'input': '', 'output': '세 가지 기본 색은 빨강, 파랑, 노랑입니다. 이 색은 다른 색을 혼합하여 만들 수 없고 다른 모든 색은 다양한 비율로 조합하여 만들 수 있기 때문에 원색이라고 부릅니다. 빛에 사용되는 첨가제 색상 시스템에서 원색은 빨강, 녹색, 파랑(RGB)입니다.'} ```

open-llm-leaderboard/details_UCLA-AGI__test_final

--- pretty_name: Evaluation run of UCLA-AGI/test_final dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [UCLA-AGI/test_final](https://huggingface.co/UCLA-AGI/test_final) 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_UCLA-AGI__test_final\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-13T15:52:58.260309](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__test_final/blob/main/results_2024-01-13T15-52-58.260309.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\": 0.6144035496773548,\n\ \ \"acc_stderr\": 0.032858739117399755,\n \"acc_norm\": 0.6200519616024565,\n\ \ \"acc_norm_stderr\": 0.03352475225298005,\n \"mc1\": 0.4222766217870257,\n\ \ \"mc1_stderr\": 0.017290733254248174,\n \"mc2\": 0.5789464689775264,\n\ \ \"mc2_stderr\": 0.015807009741465705\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6305460750853242,\n \"acc_stderr\": 0.014104578366491887,\n\ \ \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.01383056892797433\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.676458872734515,\n\ \ \"acc_stderr\": 0.0046687106891924,\n \"acc_norm\": 0.8584943238398726,\n\ \ \"acc_norm_stderr\": 0.0034783009945146973\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\": 0.6447368421052632,\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\": 0.57,\n\ \ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.028815615713432115,\n\ \ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.028815615713432115\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\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\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\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\": 0.6184971098265896,\n \"acc_stderr\": 0.03703851193099521,\n\ \ \"acc_norm\": 0.6184971098265896,\n \"acc_norm_stderr\": 0.03703851193099521\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n\ \ \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n\ \ \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\ \ 0.5446808510638298,\n \"acc_stderr\": 0.03255525359340355,\n \"\ acc_norm\": 0.5446808510638298,\n \"acc_norm_stderr\": 0.03255525359340355\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\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\ acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.04343525428949098,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.04343525428949098\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\ : 0.7322580645161291,\n \"acc_stderr\": 0.02518900666021238,\n \"\ acc_norm\": 0.7322580645161291,\n \"acc_norm_stderr\": 0.02518900666021238\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\ acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338642,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338642\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709447,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709447\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6102564102564103,\n \"acc_stderr\": 0.024726967886647074,\n\ \ \"acc_norm\": 0.6102564102564103,\n \"acc_norm_stderr\": 0.024726967886647074\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121622,\n\ \ \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121622\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7944954128440367,\n \"acc_stderr\": 0.01732435232501601,\n \"\ acc_norm\": 0.7944954128440367,\n \"acc_norm_stderr\": 0.01732435232501601\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252336,\n \"\ acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252336\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588663,\n \"\ acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588663\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808514,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808514\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.031493846709941306,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.031493846709941306\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n\ \ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.04058042015646034,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.04058042015646034\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8461538461538461,\n\ \ \"acc_stderr\": 0.023636873317489284,\n \"acc_norm\": 0.8461538461538461,\n\ \ \"acc_norm_stderr\": 0.023636873317489284\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n\ \ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\ \ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6965317919075145,\n \"acc_stderr\": 0.024752411960917212,\n\ \ \"acc_norm\": 0.6965317919075145,\n \"acc_norm_stderr\": 0.024752411960917212\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3664804469273743,\n\ \ \"acc_stderr\": 0.016115235504865464,\n \"acc_norm\": 0.3664804469273743,\n\ \ \"acc_norm_stderr\": 0.016115235504865464\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6895424836601307,\n \"acc_stderr\": 0.026493033225145898,\n\ \ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.026493033225145898\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n\ \ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n\ \ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6512345679012346,\n \"acc_stderr\": 0.02651759772446501,\n\ \ \"acc_norm\": 0.6512345679012346,\n \"acc_norm_stderr\": 0.02651759772446501\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303055,\n \ \ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303055\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44589308996088656,\n\ \ \"acc_stderr\": 0.012695244711379778,\n \"acc_norm\": 0.44589308996088656,\n\ \ \"acc_norm_stderr\": 0.012695244711379778\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.619281045751634,\n \"acc_stderr\": 0.0196438015579248,\n \ \ \"acc_norm\": 0.619281045751634,\n \"acc_norm_stderr\": 0.0196438015579248\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6653061224489796,\n \"acc_stderr\": 0.030209235226242307,\n\ \ \"acc_norm\": 0.6653061224489796,\n \"acc_norm_stderr\": 0.030209235226242307\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786845,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786845\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4222766217870257,\n\ \ \"mc1_stderr\": 0.017290733254248174,\n \"mc2\": 0.5789464689775264,\n\ \ \"mc2_stderr\": 0.015807009741465705\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7663772691397001,\n \"acc_stderr\": 0.011892194477183525\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3419257012888552,\n \ \ \"acc_stderr\": 0.0130660896251828\n }\n}\n```" repo_url: https://huggingface.co/UCLA-AGI/test_final 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_13T15_52_58.260309 path: - '**/details_harness|arc:challenge|25_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-13T15-52-58.260309.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|gsm8k|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hellaswag|10_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-52-58.260309.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T15-52-58.260309.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T15-52-58.260309.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_13T15_52_58.260309 path: - '**/details_harness|winogrande|5_2024-01-13T15-52-58.260309.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-13T15-52-58.260309.parquet' - config_name: results data_files: - split: 2024_01_13T15_52_58.260309 path: - results_2024-01-13T15-52-58.260309.parquet - split: latest path: - results_2024-01-13T15-52-58.260309.parquet --- # Dataset Card for Evaluation run of UCLA-AGI/test_final  Dataset automatically created during the evaluation run of model [UCLA-AGI/test_final](https://huggingface.co/UCLA-AGI/test_final) 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_UCLA-AGI__test_final", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T15:52:58.260309](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__test_final/blob/main/results_2024-01-13T15-52-58.260309.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": 0.6144035496773548, "acc_stderr": 0.032858739117399755, "acc_norm": 0.6200519616024565, "acc_norm_stderr": 0.03352475225298005, "mc1": 0.4222766217870257, "mc1_stderr": 0.017290733254248174, "mc2": 0.5789464689775264, "mc2_stderr": 0.015807009741465705 }, "harness|arc:challenge|25": { "acc": 0.6305460750853242, "acc_stderr": 0.014104578366491887, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.01383056892797433 }, "harness|hellaswag|10": { "acc": 0.676458872734515, "acc_stderr": 0.0046687106891924, "acc_norm": 0.8584943238398726, "acc_norm_stderr": 0.0034783009945146973 }, "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": 0.6447368421052632, "acc_stderr": 0.03894734487013317, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013317 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6754716981132075, "acc_stderr": 0.028815615713432115, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.028815615713432115 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "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": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "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": 0.6184971098265896, "acc_stderr": 0.03703851193099521, "acc_norm": 0.6184971098265896, "acc_norm_stderr": 0.03703851193099521 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5446808510638298, "acc_stderr": 0.03255525359340355, "acc_norm": 0.5446808510638298, "acc_norm_stderr": 0.03255525359340355 }, "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": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.40476190476190477, "acc_stderr": 0.025279850397404904, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.025279850397404904 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.38095238095238093, "acc_stderr": 0.04343525428949098, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.04343525428949098 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7322580645161291, "acc_stderr": 0.02518900666021238, "acc_norm": 0.7322580645161291, "acc_norm_stderr": 0.02518900666021238 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.02985751567338642, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.02985751567338642 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709447, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709447 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6102564102564103, "acc_stderr": 0.024726967886647074, "acc_norm": 0.6102564102564103, "acc_norm_stderr": 0.024726967886647074 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948485, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948485 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6554621848739496, "acc_stderr": 0.030868682604121622, "acc_norm": 0.6554621848739496, "acc_norm_stderr": 0.030868682604121622 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7944954128440367, "acc_stderr": 0.01732435232501601, "acc_norm": 0.7944954128440367, "acc_norm_stderr": 0.01732435232501601 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4305555555555556, "acc_stderr": 0.03376922151252336, "acc_norm": 0.4305555555555556, "acc_norm_stderr": 0.03376922151252336 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7941176470588235, "acc_stderr": 0.028379449451588663, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.028379449451588663 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7679324894514767, "acc_stderr": 0.027479744550808514, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.027479744550808514 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.031493846709941306, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.031493846709941306 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094633, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094633 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7484662576687117, "acc_stderr": 0.03408997886857529, "acc_norm": 0.7484662576687117, "acc_norm_stderr": 0.03408997886857529 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.04726835553719099, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.04726835553719099 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.04058042015646034, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.04058042015646034 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8461538461538461, "acc_stderr": 0.023636873317489284, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.023636873317489284 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8084291187739464, "acc_stderr": 0.014072859310451949, "acc_norm": 0.8084291187739464, "acc_norm_stderr": 0.014072859310451949 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6965317919075145, "acc_stderr": 0.024752411960917212, "acc_norm": 0.6965317919075145, "acc_norm_stderr": 0.024752411960917212 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3664804469273743, "acc_stderr": 0.016115235504865464, "acc_norm": 0.3664804469273743, "acc_norm_stderr": 0.016115235504865464 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6895424836601307, "acc_stderr": 0.026493033225145898, "acc_norm": 0.6895424836601307, "acc_norm_stderr": 0.026493033225145898 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6816720257234726, "acc_stderr": 0.026457225067811025, "acc_norm": 0.6816720257234726, "acc_norm_stderr": 0.026457225067811025 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6512345679012346, "acc_stderr": 0.02651759772446501, "acc_norm": 0.6512345679012346, "acc_norm_stderr": 0.02651759772446501 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4716312056737589, "acc_stderr": 0.029779450957303055, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.029779450957303055 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44589308996088656, "acc_stderr": 0.012695244711379778, "acc_norm": 0.44589308996088656, "acc_norm_stderr": 0.012695244711379778 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6764705882352942, "acc_stderr": 0.028418208619406755, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.028418208619406755 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.619281045751634, "acc_stderr": 0.0196438015579248, "acc_norm": 0.619281045751634, "acc_norm_stderr": 0.0196438015579248 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6653061224489796, "acc_stderr": 0.030209235226242307, "acc_norm": 0.6653061224489796, "acc_norm_stderr": 0.030209235226242307 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786845, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786845 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774708, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774708 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness|truthfulqa:mc|0": { "mc1": 0.4222766217870257, "mc1_stderr": 0.017290733254248174, "mc2": 0.5789464689775264, "mc2_stderr": 0.015807009741465705 }, "harness|winogrande|5": { "acc": 0.7663772691397001, "acc_stderr": 0.011892194477183525 }, "harness|gsm8k|5": { "acc": 0.3419257012888552, "acc_stderr": 0.0130660896251828 } } ``` ## 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]

DarthReca/quakeset

--- license: openrail task_categories: - image-classification - image-segmentation tags: - climate pretty_name: QuakeSet size_categories: - 1K<n<10K --- # Dataset Card for QuakeSet QuakeSet is a dataset to analyze different attributes of earthquakes. It contains bi-temporal time series of images and ground truth annotations for magnitudes, hypocenters, and affected areas. - **PrePrint:** https://arxiv.org/abs/2403.18116 ## Dataset Details ### Dataset Description The images are taken from the Sentinel-1 mission using the Interferometric Wide swath mode. The International Seismological Centre provides information about earthquakes. - **License:** OPENRAIL ## Dataset Structure The dataset is divided into three folds with equal distribution of magnitudes and balanced in positive and negative examples. Each sample contains: - an image - x,y coordinates - epsg of the coordinates - affected label - magnitude ## Citation **BibTeX:** ``` @article{quakeset, title={QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1}, author={Daniele Rege Cambrin and Paolo Garza}, journal={Proceedings of the 21th International Conference on Information Systems for Crisis Response and Management}, year={2024}, } ```

hazyresearch/fda

--- dataset_info: features: - name: doc_id dtype: string - name: file_name dtype: string - name: key dtype: string - name: value dtype: string - name: text dtype: string splits: - name: validaion num_bytes: 8498008 num_examples: 1102 download_size: 1381388 dataset_size: 8498008 configs: - config_name: default data_files: - split: validaion path: data/validaion-* ---

Fizzarolli/bluemoon_processeed

--- task_categories: - text-generation source_datasets: PJMixers/grimulkan_bluemoon_Karen_cleaned-carded --- preprocessed version of [PJMixers/grimulkan_bluemoon_Karen_cleaned-carded](https://huggingface.co/datasets/PJMixers/grimulkan_bluemoon_Karen_cleaned-carded) into a fun little prompt format for finetuning

xwjzds/pretrain_punctuation

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 2388642394 num_examples: 631331 download_size: 1485646531 dataset_size: 2388642394 --- # Dataset Card for "pretrain_punctuation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

aisuko/funsd-layoutlmv3

--- license: apache-2.0 --- Original from: https://huggingface.co/datasets/aisuko/funsd-layoutlmv3 Adaptered by: Aisuko License: Apache-2.0 ```python dataset = load_dataset("aisuko/funsd-layoutlmv3") # check the dataset dataset # check the features dataset["train"].features # check the first example example=dataset["train"][0] example["image"] ```

projectbaraat/hindi-translation-data-v0.1

--- dataset_info: features: - name: src dtype: string - name: tgt dtype: string splits: - name: train num_bytes: 3880301105 num_examples: 10572313 download_size: 2054879767 dataset_size: 3880301105 configs: - config_name: default data_files: - split: train path: data/train-* ---

distilled-from-one-sec-cv12/chunk_16

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1245592852 num_examples: 242711 download_size: 1269320601 dataset_size: 1245592852 --- # Dataset Card for "chunk_16" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rb05751/reuters_articles

--- license: cc 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: title dtype: string - name: body dtype: string splits: - name: train num_bytes: 12503434 num_examples: 15000 - name: validation num_bytes: 4272675 num_examples: 5000 - name: test num_bytes: 1709070 num_examples: 2000 download_size: 10790292 dataset_size: 18485179 ---

Twenty1/aws-lambda-developer-guide-docs

--- license: openrail ---

Sitedemerde/projet

--- license: other license_name: jesaispas license_link: LICENSE ---

Ehraim/SequentialLearnerv3

--- license: apache-2.0 ---

cheafdevo56/Influential_CitedNegs_1_Percent

--- dataset_info: features: - name: query struct: - name: abstract dtype: string - name: corpus_id dtype: int64 - name: title dtype: string - name: pos struct: - name: abstract dtype: string - name: corpus_id dtype: int64 - name: title dtype: string - name: neg struct: - name: abstract dtype: string - name: corpus_id dtype: int64 - name: score dtype: int64 - name: title dtype: string splits: - name: train num_bytes: 173163283.2 num_examples: 45000 - name: validation num_bytes: 19240364.8 num_examples: 5000 download_size: 115634444 dataset_size: 192403648.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

shibing624/huatuo_medical_qa_sharegpt

--- license: apache-2.0 --- source： - https://huggingface.co/datasets/FreedomIntelligence/HuatuoGPT-sft-data-v1 - https://huggingface.co/datasets/FreedomIntelligence/HuatuoGPT2_sft_instruct_GPT4_50K 转为sharegpt格式，jsonl文件。 data size: ``` > wc -l HuatuoGPT_sft_data_v1_sharegpt.jsonl 226042 HuatuoGPT_sft_data_v1_sharegpt.jsonl > wc -l HuatuoGPT2_sft_instruct_GPT4_sharegpt.jsonl 50000 HuatuoGPT2_sft_instruct_GPT4_sharegpt.jsonl ``` 转换代码：convert.py ```python import json # 假设您的JSONL文件名为 'input.jsonl' input_file = './HuatuoGPT2_sft_instruct_GPT4.jsonl' output_file = './HuatuoGPT2_sft_instruct_GPT4_sharegpt.jsonl' # 初始化输出文件 with open(input_file, 'r', encoding='utf-8') as infile, open(output_file, 'w', encoding='utf-8') as outfile: # 初始化输出的JSON结构 # 逐行读取JSONL文件 for id,line in enumerate(infile): output_json = {"conversations": []} # 解析JSON对象 data = json.loads(line.strip()) # if id > 10: # break # 假设每个JSON对象都有一个"data"列表，包含问题和答案 for i, item in enumerate(data['data']): if i % 2 == 0: # 假设问题在偶数位置，答案在奇数位置 output_json['conversations'].append({ "from": "human", "value": item[2:] }) else: output_json['conversations'].append({ "from": "gpt", "value": item[2:] }) # 将转换后的JSON写入文件 a = json.dumps(output_json, ensure_ascii=False) outfile.write(a + '\n') print(f"Conversion complete. Output saved to '{output_file}'.") ```

AlexanderBenady/generated_lectures

--- license: unknown ---

ArjjunS/Pantaloons_public

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4229185.0 num_examples: 75 download_size: 4206323 dataset_size: 4229185.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

liuyanchen1015/MULTI_VALUE_mnli_his_he

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 125800 num_examples: 584 - name: dev_mismatched num_bytes: 126834 num_examples: 518 - name: test_matched num_bytes: 141846 num_examples: 661 - name: test_mismatched num_bytes: 121135 num_examples: 491 - name: train num_bytes: 5412418 num_examples: 25033 download_size: 3767461 dataset_size: 5928033 --- # Dataset Card for "MULTI_VALUE_mnli_his_he" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

up-the-brics/raw-chatgpt

--- license: apache-2.0 ---

afrikaans_ner_corpus

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - af license: - other multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Afrikaans Ner Corpus license_details: Creative Commons Attribution 2.5 South Africa License dataset_info: config_name: afrikaans_ner_corpus features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 4025651 num_examples: 8962 download_size: 944804 dataset_size: 4025651 configs: - config_name: afrikaans_ner_corpus data_files: - split: train path: afrikaans_ner_corpus/train-* default: true --- # Dataset Card for Afrikaans Ner Corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Afrikaans Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/299) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za) ### Dataset Summary The Afrikaans Ner Corpus is an Afrikaans dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Afrikaans. ## Dataset Structure ### Data Instances A data point consists of sentences seperated by empty line and tab-seperated tokens and tags. {'id': '0', 'ner_tags': [0, 0, 0, 0, 0], 'tokens': ['Vertaling', 'van', 'die', 'inligting', 'in'] } ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC", ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity. ### Data Splits The data was not split. ## Dataset Creation ### Curation Rationale The data was created to help introduce resources to new language - Afrikaans. [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The data is based on South African government domain and was crawled from gov.za websites. [More Information Needed] #### Who are the source language producers? The data was produced by writers of South African government websites - gov.za [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The data was annotated during the NCHLT text resource development project. [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa). See: [more information](http://www.nwu.ac.za/ctext) ### Licensing Information The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode) ### Citation Information ``` @inproceedings{afrikaans_ner_corpus, author = { Gerhard van Huyssteen and Martin Puttkammer and E.B. Trollip and J.C. Liversage and Roald Eiselen}, title = {NCHLT Afrikaans Named Entity Annotated Corpus}, booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.}, year = {2016}, url = {https://repo.sadilar.org/handle/20.500.12185/299}, } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.

orgcatorg/megawika

--- dataset_info: - config_name: my features: - name: article_title dtype: string - name: article_text dtype: string - name: entries list: - name: id dtype: string - name: original dtype: string - name: original_sents sequence: string - name: parse_tokens sequence: sequence: string - name: passage struct: - name: en_lang_token_map struct: - name: '0' dtype: int64 - name: '1' dtype: int64 - name: '10' dtype: int64 - name: '100' dtype: int64 - name: '101' dtype: int64 - name: '102' dtype: int64 - name: '103' dtype: int64 - name: '104' dtype: int64 - name: '105' dtype: int64 - name: '106' dtype: int64 - name: '107' dtype: int64 - name: '108' dtype: int64 - name: '109' dtype: int64 - name: '11' dtype: int64 - name: '110' dtype: int64 - name: '111' dtype: int64 - name: '112' dtype: int64 - name: '113' dtype: int64 - name: '114' dtype: int64 - name: '115' dtype: int64 - name: '116' dtype: int64 - name: '117' dtype: int64 - name: '118' dtype: int64 - name: '119' dtype: int64 - name: '12' dtype: int64 - name: '120' dtype: int64 - name: '121' dtype: int64 - name: '122' dtype: int64 - name: '123' dtype: int64 - name: '124' dtype: int64 - name: '125' dtype: int64 - name: '126' dtype: int64 - name: '127' dtype: int64 - name: '128' dtype: int64 - name: '129' dtype: int64 - name: '13' dtype: int64 - name: '130' dtype: int64 - name: '131' dtype: int64 - name: '132' dtype: int64 - name: '133' dtype: int64 - name: '134' dtype: int64 - name: '135' dtype: int64 - name: '136' dtype: int64 - name: '137' dtype: int64 - name: '138' dtype: int64 - name: '139' dtype: int64 - name: '14' dtype: int64 - name: '140' dtype: int64 - name: '141' dtype: int64 - name: '142' dtype: int64 - name: '143' dtype: int64 - name: '144' dtype: int64 - name: '145' dtype: int64 - name: '146' dtype: int64 - name: '147' dtype: int64 - name: '148' dtype: int64 - name: '149' dtype: int64 - name: '15' dtype: int64 - name: '150' dtype: int64 - name: '151' dtype: int64 - name: '152' dtype: int64 - name: '153' dtype: int64 - name: '154' dtype: int64 - 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name: '71' dtype: string - name: '72' dtype: string - name: '73' dtype: string - name: '74' dtype: string - name: '75' dtype: string - name: '76' dtype: string - name: '77' dtype: string - name: '78' dtype: string - name: '79' dtype: string - name: '8' dtype: string - name: '80' dtype: string - name: '81' dtype: string - name: '82' dtype: string - name: '83' dtype: string - name: '84' dtype: string - name: '85' dtype: string - name: '86' dtype: string - name: '87' dtype: string - name: '88' dtype: string - name: '89' dtype: string - name: '9' dtype: string - name: '90' dtype: string - name: '91' dtype: string - name: '92' dtype: string - name: '93' dtype: string - name: '94' dtype: string - name: '95' dtype: string - name: '96' dtype: string - name: '97' dtype: string - name: '98' dtype: string - name: '99' dtype: string - name: parse list: - name: children list: - name: children list: - name: children sequence: 'null' - name: confidence dtype: float64 - name: label dtype: string - name: span sequence: int64 - name: confidence dtype: float64 - name: label dtype: string - name: span sequence: int64 - name: confidence dtype: float64 - name: label dtype: string - name: span sequence: int64 - name: text sequence: string - name: qa_pairs list: - name: en_answer dtype: string - name: en_answer_tokens sequence: string - name: en_match_in_passage sequence: int64 - name: en_matches_in_source sequence: sequence: int64 - name: frames list: - name: argument dtype: string - name: frame dtype: string - name: lang_answer dtype: string - name: lang_match_in_passage sequence: int64 - name: lang_matches_in_source sequence: sequence: int64 - name: match_disambiguated_question dtype: string - name: passage sequence: string - name: passage_id dtype: string - name: question dtype: string - name: repetitious_translation dtype: bool - name: source_lang dtype: string - name: source_text dtype: string - name: source_url dtype: string - name: translation dtype: string - name: translation_probs sequence: string - name: translation_sents sequence: string splits: - name: train num_bytes: 50253843 num_examples: 842 download_size: 12279811 dataset_size: 50253843 configs: - config_name: my data_files: - split: my path: my/my-* - config_name: my_refined data_files: - split: train path: my_refined/train-* --- # Dataset Card for "megawika" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

distilled-from-one-sec-cv12/chunk_228

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1186307988 num_examples: 231159 download_size: 1208791652 dataset_size: 1186307988 --- # Dataset Card for "chunk_228" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ShenaoZ/0.001_ablation_dataset

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: is_better dtype: bool splits: - name: train_prefs_1 num_bytes: 168081294 num_examples: 20378 - name: test_prefs_1 num_bytes: 16410846 num_examples: 2000 - name: train_prefs_2 num_bytes: 174168708 num_examples: 20378 - name: test_prefs_2 num_bytes: 16912720 num_examples: 2000 download_size: 207506841 dataset_size: 375573568 configs: - config_name: default data_files: - split: train_prefs_1 path: data/train_prefs_1-* - split: test_prefs_1 path: data/test_prefs_1-* - split: train_prefs_2 path: data/train_prefs_2-* - split: test_prefs_2 path: data/test_prefs_2-* --- # Dataset Card for "0.001_ablation_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

johnsonlee/My-First-Dataset

--- license: postgresql language: - ch tags: - finance pretty_name: It's a good name. size_categories: - 1K<n<10K --- 这是我的第一个dataset

davidberenstein1957/text2text-10-predictions

--- dataset_info: features: - name: text dtype: string - name: target dtype: string splits: - name: train num_bytes: 104132.0 num_examples: 72 - name: test num_bytes: 26033.0 num_examples: 18 download_size: 86213 dataset_size: 130165.0 --- # Dataset Card for "text2text-10-predictions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

osyvokon/pavlick-formality-scores

--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-3.0 multilinguality: - monolingual pretty_name: 'Sentence-level formality annotations for news, blogs, email and QA forums. Published in "An Empirical Analysis of Formality in Online Communication" (Pavlick and Tetreault, 2016) ' size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring --- This dataset contains sentence-level formality annotations used in the 2016 TACL paper "An Empirical Analysis of Formality in Online Communication" (Pavlick and Tetreault, 2016). It includes sentences from four genres (news, blogs, email, and QA forums), all annotated by humans on Amazon Mechanical Turk. The news and blog data was collected by Shibamouli Lahiri, and we are redistributing it here for the convenience of other researchers. We collected the email and answers data ourselves, using a similar annotation setup to Shibamouli. In the original dataset, `answers` and `email` were tokenized. In this version, Oleksiy Syvokon detokenized them with `moses-detokenizer` and a bunch of additional regexps. If you use this data in your work, please cite BOTH of the below papers: ``` @article{PavlickAndTetreault-2016:TACL, author = {Ellie Pavlick and Joel Tetreault}, title = {An Empirical Analysis of Formality in Online Communication}, journal = {Transactions of the Association for Computational Linguistics}, year = {2016}, publisher = {Association for Computational Linguistics} } @article{Lahiri-2015:arXiv, title={{SQUINKY! A} Corpus of Sentence-level Formality, Informativeness, and Implicature}, author={Lahiri, Shibamouli}, journal={arXiv preprint arXiv:1506.02306}, year={2015} } ``` ## Contents The annotated data files and number of lines in each are as follows: * 4977 answers -- Annotated sentences from a random sample of posts from the Yahoo! Answers forums: https://answers.yahoo.com/ * 1821 blog -- Annotated sentences from the top 100 blogs listed on http://technorati.com/ on October 31, 2009. * 1701 email -- Annotated sentences from a random sample of emails from the Jeb Bush email archive: http://americanbridgepac.org/jeb-bushs-gubernatorial-email-archive/ * 2775 news -- Annotated sentences from the "breaking", "recent", and "local" news sections of the following 20 news sites: CNN, CBS News, ABC News, Reuters, BBC News Online, New York Times, Los Angeles Times, The Guardian (U.K.), Voice of America, Boston Globe, Chicago Tribune, San Francisco Chronicle, Times Online (U.K.), news.com.au, Xinhua, The Times of India, Seattle Post Intelligencer, Daily Mail, and Bloomberg L.P. ## Format Each record contains the following fields: 1. `avg_score`: the mean formality rating, which ranges from -3 to 3 where lower scores indicate less formal sentences 2. `sentence`

CyberHarem/altina_fireemblem

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of altina (Fire Emblem) This is the dataset of altina (Fire Emblem), containing 54 images and their tags. The core tags of this character are `long_hair, blue_eyes, purple_hair, breasts, bangs, large_breasts, very_long_hair, blue_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 | 54 | 83.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 54 | 48.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 127 | 90.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 54 | 73.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 127 | 124.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/altina_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/altina_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](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, armor, looking_at_viewer, solo, sword, dress, fingerless_gloves, holding, thighhighs, simple_background, white_background, boots, elbow_gloves | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bell, christmas, fur_trim, reindeer_antlers, solo, black_thighhighs, deer_ears, full_body, looking_at_viewer, candy_cane, fake_animal_ears, gift_box, holding_sword, medium_breasts, simple_background, smile, white_footwear, white_gloves, elbow_gloves, low-tied_long_hair, open_mouth, parted_lips | | 2 | 17 | ![](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, cleavage, looking_at_viewer, solo, navel, smile, hat, blush, abs, collarbone, muscular_female, one-piece_swimsuit, simple_background, white_background, bracelet | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | armor | looking_at_viewer | solo | sword | dress | fingerless_gloves | holding | thighhighs | simple_background | white_background | boots | elbow_gloves | bell | christmas | fur_trim | reindeer_antlers | black_thighhighs | deer_ears | full_body | candy_cane | fake_animal_ears | gift_box | holding_sword | medium_breasts | smile | white_footwear | white_gloves | low-tied_long_hair | open_mouth | parted_lips | cleavage | navel | hat | blush | abs | collarbone | muscular_female | one-piece_swimsuit | bracelet | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:--------|:--------|:--------------------|:----------|:-------------|:--------------------|:-------------------|:--------|:---------------|:-------|:------------|:-----------|:-------------------|:-------------------|:------------|:------------|:-------------|:-------------------|:-----------|:----------------|:-----------------|:--------|:-----------------|:---------------|:---------------------|:-------------|:--------------|:-----------|:--------|:------|:--------|:------|:-------------|:------------------|:---------------------|:-----------| | 0 | 5 | ![](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 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 2 | 17 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | | | | | | X | X | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X |

ibranze/araproje_mmlu_tr_conf_gpt2_nearestscore_true_x

--- dataset_info: features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: validation num_bytes: 137404.0 num_examples: 250 download_size: 83805 dataset_size: 137404.0 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "araproje_mmlu_tr_conf_gpt2_nearestscore_true_x" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

philschmid/hh-rrhf-dahoas-gptj-rm-25k

--- dataset_info: features: - name: prompt dtype: string - name: responses sequence: string - name: scores sequence: float64 splits: - name: train num_bytes: 21973591 num_examples: 24983 download_size: 12522534 dataset_size: 21973591 --- # Dataset Card for "hh-rrhf-dahoas-gptj-rm-25k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bigscience-data/roots_ar_wiktionary

--- language: ar license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox ---

jiwon65/aihub_general_6000_for_train

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: audio sequence: float32 splits: - name: train num_bytes: 1212419491 num_examples: 6000 download_size: 1071487189 dataset_size: 1212419491 --- # Dataset Card for "korean-general-command-voice_0-6000_samplingRate-16000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ibragim-bad/arc_challenge

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices struct: - name: label sequence: string - name: text sequence: string - name: answerKey dtype: string splits: - name: test num_bytes: 375511 num_examples: 1172 - name: train num_bytes: 349760 num_examples: 1119 - name: validation num_bytes: 96660 num_examples: 299 download_size: 449682 dataset_size: 821931 --- # Dataset Card for "arc_challenge" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

imvladikon/hebrew_news

--- annotations_creators: - no-annotation language_creators: - other language: - he license: - other multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - summarization task_ids: - news-articles-summarization --- ## 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 ``` id - article id articleBody - article main content description - short version of the article, description of the article headline - headline of the article title - title of the article ```

open-llm-leaderboard/details_maywell__PiVoT-SUS-RP

--- pretty_name: Evaluation run of maywell/PiVoT-SUS-RP dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [maywell/PiVoT-SUS-RP](https://huggingface.co/maywell/PiVoT-SUS-RP) 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_maywell__PiVoT-SUS-RP\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-15T19:33:37.820287](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__PiVoT-SUS-RP/blob/main/results_2024-01-15T19-33-37.820287.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\": 0.7586143927988348,\n\ \ \"acc_stderr\": 0.028201585690631335,\n \"acc_norm\": 0.7620604659128375,\n\ \ \"acc_norm_stderr\": 0.028744091509590272,\n \"mc1\": 0.3843329253365973,\n\ \ \"mc1_stderr\": 0.017028707301245203,\n \"mc2\": 0.5456609919227617,\n\ \ \"mc2_stderr\": 0.014778672831926782\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.643344709897611,\n \"acc_stderr\": 0.013998056902620192,\n\ \ \"acc_norm\": 0.6655290102389079,\n \"acc_norm_stderr\": 0.013787460322441375\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6398127862975503,\n\ \ \"acc_stderr\": 0.00479073468370459,\n \"acc_norm\": 0.8422624975104561,\n\ \ \"acc_norm_stderr\": 0.0036374977089340356\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7111111111111111,\n\ \ \"acc_stderr\": 0.0391545063041425,\n \"acc_norm\": 0.7111111111111111,\n\ \ \"acc_norm_stderr\": 0.0391545063041425\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.9013157894736842,\n \"acc_stderr\": 0.024270227737522715,\n\ \ \"acc_norm\": 0.9013157894736842,\n \"acc_norm_stderr\": 0.024270227737522715\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8113207547169812,\n \"acc_stderr\": 0.024079995130062253,\n\ \ \"acc_norm\": 0.8113207547169812,\n \"acc_norm_stderr\": 0.024079995130062253\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9097222222222222,\n\ \ \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n\ \ \"acc_norm_stderr\": 0.023964965777906935\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\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7341040462427746,\n\ \ \"acc_stderr\": 0.03368762932259431,\n \"acc_norm\": 0.7341040462427746,\n\ \ \"acc_norm_stderr\": 0.03368762932259431\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7787234042553192,\n \"acc_stderr\": 0.027136349602424063,\n\ \ \"acc_norm\": 0.7787234042553192,\n \"acc_norm_stderr\": 0.027136349602424063\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5789473684210527,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7655172413793103,\n \"acc_stderr\": 0.035306258743465914,\n\ \ \"acc_norm\": 0.7655172413793103,\n \"acc_norm_stderr\": 0.035306258743465914\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.6931216931216931,\n \"acc_stderr\": 0.023752928712112136,\n \"\ acc_norm\": 0.6931216931216931,\n \"acc_norm_stderr\": 0.023752928712112136\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5952380952380952,\n\ \ \"acc_stderr\": 0.04390259265377563,\n \"acc_norm\": 0.5952380952380952,\n\ \ \"acc_norm_stderr\": 0.04390259265377563\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_biology|5\"\ : {\n \"acc\": 0.8935483870967742,\n \"acc_stderr\": 0.01754510295165663,\n\ \ \"acc_norm\": 0.8935483870967742,\n \"acc_norm_stderr\": 0.01754510295165663\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.6551724137931034,\n \"acc_stderr\": 0.03344283744280458,\n \"\ acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03344283744280458\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\"\ : 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8153846153846154,\n \"acc_stderr\": 0.019671632413100295,\n\ \ \"acc_norm\": 0.8153846153846154,\n \"acc_norm_stderr\": 0.019671632413100295\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.43333333333333335,\n \"acc_stderr\": 0.030213340289237927,\n \ \ \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.030213340289237927\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8697478991596639,\n \"acc_stderr\": 0.021863258494852118,\n\ \ \"acc_norm\": 0.8697478991596639,\n \"acc_norm_stderr\": 0.021863258494852118\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289715,\n \"\ acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289715\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9211009174311927,\n \"acc_stderr\": 0.011558198113769578,\n \"\ acc_norm\": 0.9211009174311927,\n \"acc_norm_stderr\": 0.011558198113769578\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6527777777777778,\n \"acc_stderr\": 0.032468872436376486,\n \"\ acc_norm\": 0.6527777777777778,\n \"acc_norm_stderr\": 0.032468872436376486\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089678,\n \"\ acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089678\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \ \ \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7937219730941704,\n\ \ \"acc_stderr\": 0.02715715047956382,\n \"acc_norm\": 0.7937219730941704,\n\ \ \"acc_norm_stderr\": 0.02715715047956382\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\":\ \ 0.8842975206611571,\n \"acc_stderr\": 0.029199802455622793,\n \"\ acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.029199802455622793\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.03038159675665168,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.03038159675665168\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n\ \ \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5625,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.912621359223301,\n \"acc_stderr\": 0.027960689125970654,\n\ \ \"acc_norm\": 0.912621359223301,\n \"acc_norm_stderr\": 0.027960689125970654\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n\ \ \"acc_stderr\": 0.016534627684311364,\n \"acc_norm\": 0.9316239316239316,\n\ \ \"acc_norm_stderr\": 0.016534627684311364\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.035887028128263714,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.035887028128263714\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9054916985951469,\n\ \ \"acc_stderr\": 0.010461015338193068,\n \"acc_norm\": 0.9054916985951469,\n\ \ \"acc_norm_stderr\": 0.010461015338193068\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8121387283236994,\n \"acc_stderr\": 0.021029269752423228,\n\ \ \"acc_norm\": 0.8121387283236994,\n \"acc_norm_stderr\": 0.021029269752423228\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6212290502793296,\n\ \ \"acc_stderr\": 0.016223533510365123,\n \"acc_norm\": 0.6212290502793296,\n\ \ \"acc_norm_stderr\": 0.016223533510365123\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8562091503267973,\n \"acc_stderr\": 0.020091188936043693,\n\ \ \"acc_norm\": 0.8562091503267973,\n \"acc_norm_stderr\": 0.020091188936043693\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8231511254019293,\n\ \ \"acc_stderr\": 0.0216700588855108,\n \"acc_norm\": 0.8231511254019293,\n\ \ \"acc_norm_stderr\": 0.0216700588855108\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.018689725721062072,\n\ \ \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.018689725721062072\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6347517730496454,\n \"acc_stderr\": 0.028723863853281267,\n \ \ \"acc_norm\": 0.6347517730496454,\n \"acc_norm_stderr\": 0.028723863853281267\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6075619295958279,\n\ \ \"acc_stderr\": 0.012471243669229096,\n \"acc_norm\": 0.6075619295958279,\n\ \ \"acc_norm_stderr\": 0.012471243669229096\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.02315746830855936,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02315746830855936\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8088235294117647,\n \"acc_stderr\": 0.015908290136278036,\n \ \ \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.015908290136278036\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8326530612244898,\n \"acc_stderr\": 0.02389714476891452,\n\ \ \"acc_norm\": 0.8326530612244898,\n \"acc_norm_stderr\": 0.02389714476891452\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\ \ \"acc_stderr\": 0.022076326101824664,\n \"acc_norm\": 0.8905472636815921,\n\ \ \"acc_norm_stderr\": 0.022076326101824664\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n\ \ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\ \ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.9005847953216374,\n \"acc_stderr\": 0.022949025579355027,\n\ \ \"acc_norm\": 0.9005847953216374,\n \"acc_norm_stderr\": 0.022949025579355027\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3843329253365973,\n\ \ \"mc1_stderr\": 0.017028707301245203,\n \"mc2\": 0.5456609919227617,\n\ \ \"mc2_stderr\": 0.014778672831926782\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.010470796496781105\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7050796057619408,\n \ \ \"acc_stderr\": 0.012560698010954762\n }\n}\n```" repo_url: https://huggingface.co/maywell/PiVoT-SUS-RP 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_15T19_33_37.820287 path: - '**/details_harness|arc:challenge|25_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-15T19-33-37.820287.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|gsm8k|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hellaswag|10_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-15T19-33-37.820287.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-management|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T19-33-37.820287.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|truthfulqa:mc|0_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-15T19-33-37.820287.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_15T19_33_37.820287 path: - '**/details_harness|winogrande|5_2024-01-15T19-33-37.820287.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-15T19-33-37.820287.parquet' - config_name: results data_files: - split: 2024_01_15T19_33_37.820287 path: - results_2024-01-15T19-33-37.820287.parquet - split: latest path: - results_2024-01-15T19-33-37.820287.parquet --- # Dataset Card for Evaluation run of maywell/PiVoT-SUS-RP  Dataset automatically created during the evaluation run of model [maywell/PiVoT-SUS-RP](https://huggingface.co/maywell/PiVoT-SUS-RP) 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_maywell__PiVoT-SUS-RP", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-15T19:33:37.820287](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__PiVoT-SUS-RP/blob/main/results_2024-01-15T19-33-37.820287.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": 0.7586143927988348, "acc_stderr": 0.028201585690631335, "acc_norm": 0.7620604659128375, "acc_norm_stderr": 0.028744091509590272, "mc1": 0.3843329253365973, "mc1_stderr": 0.017028707301245203, "mc2": 0.5456609919227617, "mc2_stderr": 0.014778672831926782 }, "harness|arc:challenge|25": { "acc": 0.643344709897611, "acc_stderr": 0.013998056902620192, "acc_norm": 0.6655290102389079, "acc_norm_stderr": 0.013787460322441375 }, "harness|hellaswag|10": { "acc": 0.6398127862975503, "acc_stderr": 0.00479073468370459, "acc_norm": 0.8422624975104561, "acc_norm_stderr": 0.0036374977089340356 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7111111111111111, "acc_stderr": 0.0391545063041425, "acc_norm": 0.7111111111111111, "acc_norm_stderr": 0.0391545063041425 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.9013157894736842, "acc_stderr": 0.024270227737522715, "acc_norm": 0.9013157894736842, "acc_norm_stderr": 0.024270227737522715 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8113207547169812, "acc_stderr": 0.024079995130062253, "acc_norm": 0.8113207547169812, "acc_norm_stderr": 0.024079995130062253 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "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": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.45, "acc_stderr": 0.04999999999999999, "acc_norm": 0.45, "acc_norm_stderr": 0.04999999999999999 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7341040462427746, "acc_stderr": 0.03368762932259431, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.03368762932259431 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5588235294117647, "acc_stderr": 0.049406356306056595, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7787234042553192, "acc_stderr": 0.027136349602424063, "acc_norm": 0.7787234042553192, "acc_norm_stderr": 0.027136349602424063 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5789473684210527, "acc_stderr": 0.046446020912223177, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7655172413793103, "acc_stderr": 0.035306258743465914, "acc_norm": 0.7655172413793103, "acc_norm_stderr": 0.035306258743465914 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6931216931216931, "acc_stderr": 0.023752928712112136, "acc_norm": 0.6931216931216931, "acc_norm_stderr": 0.023752928712112136 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5952380952380952, "acc_stderr": 0.04390259265377563, "acc_norm": 0.5952380952380952, "acc_norm_stderr": 0.04390259265377563 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8935483870967742, "acc_stderr": 0.01754510295165663, "acc_norm": 0.8935483870967742, "acc_norm_stderr": 0.01754510295165663 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03344283744280458, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03344283744280458 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706463, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706463 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527033, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527033 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8153846153846154, "acc_stderr": 0.019671632413100295, "acc_norm": 0.8153846153846154, "acc_norm_stderr": 0.019671632413100295 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.43333333333333335, "acc_stderr": 0.030213340289237927, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.030213340289237927 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8697478991596639, "acc_stderr": 0.021863258494852118, "acc_norm": 0.8697478991596639, "acc_norm_stderr": 0.021863258494852118 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.48344370860927155, "acc_stderr": 0.040802441856289715, "acc_norm": 0.48344370860927155, "acc_norm_stderr": 0.040802441856289715 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9211009174311927, "acc_stderr": 0.011558198113769578, "acc_norm": 0.9211009174311927, "acc_norm_stderr": 0.011558198113769578 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6527777777777778, "acc_stderr": 0.032468872436376486, "acc_norm": 0.6527777777777778, "acc_norm_stderr": 0.032468872436376486 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9264705882352942, "acc_stderr": 0.018318855850089678, "acc_norm": 0.9264705882352942, "acc_norm_stderr": 0.018318855850089678 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9113924050632911, "acc_stderr": 0.018498315206865384, "acc_norm": 0.9113924050632911, "acc_norm_stderr": 0.018498315206865384 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7937219730941704, "acc_stderr": 0.02715715047956382, "acc_norm": 0.7937219730941704, "acc_norm_stderr": 0.02715715047956382 }, "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": 0.8842975206611571, "acc_stderr": 0.029199802455622793, "acc_norm": 0.8842975206611571, "acc_norm_stderr": 0.029199802455622793 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8888888888888888, "acc_stderr": 0.03038159675665168, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.03038159675665168 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8773006134969326, "acc_stderr": 0.025777328426978927, "acc_norm": 0.8773006134969326, "acc_norm_stderr": 0.025777328426978927 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5625, "acc_stderr": 0.04708567521880525, "acc_norm": 0.5625, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.912621359223301, "acc_stderr": 0.027960689125970654, "acc_norm": 0.912621359223301, "acc_norm_stderr": 0.027960689125970654 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9316239316239316, "acc_stderr": 0.016534627684311364, "acc_norm": 0.9316239316239316, "acc_norm_stderr": 0.016534627684311364 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.85, "acc_stderr": 0.035887028128263714, "acc_norm": 0.85, "acc_norm_stderr": 0.035887028128263714 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9054916985951469, "acc_stderr": 0.010461015338193068, "acc_norm": 0.9054916985951469, "acc_norm_stderr": 0.010461015338193068 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8121387283236994, "acc_stderr": 0.021029269752423228, "acc_norm": 0.8121387283236994, "acc_norm_stderr": 0.021029269752423228 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.6212290502793296, "acc_stderr": 0.016223533510365123, "acc_norm": 0.6212290502793296, "acc_norm_stderr": 0.016223533510365123 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8562091503267973, "acc_stderr": 0.020091188936043693, "acc_norm": 0.8562091503267973, "acc_norm_stderr": 0.020091188936043693 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8231511254019293, "acc_stderr": 0.0216700588855108, "acc_norm": 0.8231511254019293, "acc_norm_stderr": 0.0216700588855108 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8703703703703703, "acc_stderr": 0.018689725721062072, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.018689725721062072 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6347517730496454, "acc_stderr": 0.028723863853281267, "acc_norm": 0.6347517730496454, "acc_norm_stderr": 0.028723863853281267 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6075619295958279, "acc_stderr": 0.012471243669229096, "acc_norm": 0.6075619295958279, "acc_norm_stderr": 0.012471243669229096 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8235294117647058, "acc_stderr": 0.02315746830855936, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.02315746830855936 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8088235294117647, "acc_stderr": 0.015908290136278036, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.015908290136278036 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8326530612244898, "acc_stderr": 0.02389714476891452, "acc_norm": 0.8326530612244898, "acc_norm_stderr": 0.02389714476891452 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824664, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824664 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.5783132530120482, "acc_stderr": 0.038444531817709175, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.038444531817709175 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.9005847953216374, "acc_stderr": 0.022949025579355027, "acc_norm": 0.9005847953216374, "acc_norm_stderr": 0.022949025579355027 }, "harness|truthfulqa:mc|0": { "mc1": 0.3843329253365973, "mc1_stderr": 0.017028707301245203, "mc2": 0.5456609919227617, "mc2_stderr": 0.014778672831926782 }, "harness|winogrande|5": { "acc": 0.8334648776637726, "acc_stderr": 0.010470796496781105 }, "harness|gsm8k|5": { "acc": 0.7050796057619408, "acc_stderr": 0.012560698010954762 } } ``` ## 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]

mteb/twentynewsgroups-clustering

--- language: - en ---

X-LANCE/WebSRC_v1.0

--- license: cc-by-4.0 --- # WebSRC v1.0 WebSRC v1.0 is a dataset for reading comprehension on structural web pages. The task is to answer questions about web pages, which requires a system to have a comprehensive understanding of the spatial structure and logical structure. WebSRC consists of 6.4K web pages and 400K question-answer pairs about web pages. For each web page, we manually chose one segment from it and saved the corresponding HTML code, screenshot, and metadata like positions and sizes. Questions in WebSRC were created for each segment. Answers are either text spans from web pages or yes/no. Taking the HTML code, screenshot, metadata as well as question as input, a model is to predict the answer from the web page. Our dataset is the first one that provides HTML documents as well as images, and is larger in the number of domains and queries. For more details, please refer to our paper [WebSRC: A Dataset for Web-Based Structural Reading Comprehension](https://arxiv.org/abs/2101.09465). The Leaderboard of WebSRC v1.0 can be found [here](https://x-lance.github.io/WebSRC/). ## Data Format Description The dataset for each website will be stored in `dataset.csv` in the directory `{domain-name}/{website-number}`. The corresponding raw data (including HTML files, screenshots, bounding box coordinates, and page names and urls) is stored in the `processed_data` folder in the same directory. In `dataset.csv`, each row corresponds to one question-answer data point except the header. The meanings of each column are as follows: * `question`: a string, the question of this question-answer data point. * `id`: a unique id for this question-answer data point. Each `id` has a length 14, the first two characters are the domain indicator, the following two number is the website name. The corresponding page id can be extracted by `id[2:9]`, for example, id "sp160000100001" means this line is created from the *sport* domain, website *16*, and the corresponding page is `1600001.html`. * `element_id`: an integer, the tag id (corresponding to the tag's `tid` attribute in the HTML files) of the deepest tag in the DOM tree which contain all the answer. For yes/no question, there is no tag associated with the answer, so the `element_id` is -1. * `answer_start`: an integer, the char offset of the answer from the start of the content of the tag specified by `element_id`. Note that before counting this number, we first eliminate all the inner tags in the specified tag and replace all the consecutive whitespaces with one space. For yes/no questions, `answer_start` is 1 for answer "yes" and 0 for answer "no". * `answer`: a string, the answer of this question-answer data point. ## Data Statistics We roughly divided the questions in WebSRC v1.0 into three categories: KV, Compare, and Table. The detailed definitions can be found in our [paper](https://arxiv.org/abs/2101.09465). The numbers of websites, webpages, and QAs corresponding to the three categories are as follows: Type | # Websites | # Webpages | # QAs ---- | ---------- | ---------- | ----- KV | 34 | 3,207 | 168,606 Comparison | 15 | 1,339 | 68,578 Table | 21 | 1,901 | 163,314 The statistics of the dataset splits are as follows: Split | # Websites | # Webpages | # QAs ----- | ---------- | ---------- | ----- Train | 50 | 4,549 | 307,315 Dev | 10 | 913 | 52,826 Test | 10 | 985 | 40,357 ## Obtain Test Result For test set evaluation, please send your prediction files to zhao_mengxin@sjtu.edu.cn and chenlusz@sjtu.edu.cn with title "WebSRC Test: \<your model name\>+\<your institution\>". For evaluation, the prediction files should contain two files: ```jsonc // prediction.json // A json format file, keys are ids and values are the predicted answers (string). { "sp160000100001": "predicted answer", "sp160000100002": "...", //... } // tag_prediction.json // A json format file, keys are ids and values are the predicted tag tid (int) { "sp160000100001": -1, "sp160000100002": -1, //... } ``` We encourage to submit results from **at least three runs with different random seeds** to reduce the uncertainty of the experiments. Please place prediction files for each run in different directories and submit a zipped file. The average test result will be sent by email. ## Reference If you use any source codes or datasets included in this repository in your work, please cite the corresponding papers. The bibtex are listed below: ```text @inproceedings{chen-etal-2021-websrc, title = "{W}eb{SRC}: A Dataset for Web-Based Structural Reading Comprehension", author = "Chen, Xingyu and Zhao, Zihan and Chen, Lu and Ji, JiaBao and Zhang, Danyang and Luo, Ao and Xiong, Yuxuan and Yu, Kai", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.343", pages = "4173--4185", abstract = "Web search is an essential way for humans to obtain information, but it{'}s still a great challenge for machines to understand the contents of web pages. In this paper, we introduce the task of web-based structural reading comprehension. Given a web page and a question about it, the task is to find an answer from the web page. This task requires a system not only to understand the semantics of texts but also the structure of the web page. Moreover, we proposed WebSRC, a novel Web-based Structural Reading Comprehension dataset. WebSRC consists of 400K question-answer pairs, which are collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata. Each question in WebSRC requires a certain structural understanding of a web page to answer, and the answer is either a text span on the web page or yes/no. We evaluate various strong baselines on our dataset to show the difficulty of our task. We also investigate the usefulness of structural information and visual features. Our dataset and baselines have been publicly available.", } ```

open-llm-leaderboard/details_EleutherAI__gpt-neo-125m

--- pretty_name: Evaluation run of EleutherAI/gpt-neo-125m dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) 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_EleutherAI__gpt-neo-125m\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-18T09:42:25.890470](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neo-125m/blob/main/results_2023-10-18T09-42-25.890470.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\": 0.0016778523489932886,\n\ \ \"em_stderr\": 0.0004191330178826801,\n \"f1\": 0.03690436241610747,\n\ \ \"f1_stderr\": 0.0011592977848577672,\n \"acc\": 0.2603955425321017,\n\ \ \"acc_stderr\": 0.007779096578699754\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.0004191330178826801,\n\ \ \"f1\": 0.03690436241610747,\n \"f1_stderr\": 0.0011592977848577672\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \ \ \"acc_stderr\": 0.0015145735612245494\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5177584846093133,\n \"acc_stderr\": 0.014043619596174959\n\ \ }\n}\n```" repo_url: https://huggingface.co/EleutherAI/gpt-neo-125m leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|arc:challenge|25_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T13:58:00.274896.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_18T09_42_25.890470 path: - '**/details_harness|drop|3_2023-10-18T09-42-25.890470.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-18T09-42-25.890470.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_18T09_42_25.890470 path: - '**/details_harness|gsm8k|5_2023-10-18T09-42-25.890470.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-18T09-42-25.890470.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hellaswag|10_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:58:00.274896.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:58:00.274896.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_58_00.274896 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T13:58:00.274896.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T13:58:00.274896.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_18T09_42_25.890470 path: - '**/details_harness|winogrande|5_2023-10-18T09-42-25.890470.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-18T09-42-25.890470.parquet' - config_name: results data_files: - split: 2023_07_19T13_58_00.274896 path: - results_2023-07-19T13:58:00.274896.parquet - split: 2023_10_18T09_42_25.890470 path: - results_2023-10-18T09-42-25.890470.parquet - split: latest path: - results_2023-10-18T09-42-25.890470.parquet --- # Dataset Card for Evaluation run of EleutherAI/gpt-neo-125m ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/EleutherAI/gpt-neo-125m - **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 [EleutherAI/gpt-neo-125m](https://huggingface.co/EleutherAI/gpt-neo-125m) 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_EleutherAI__gpt-neo-125m", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-18T09:42:25.890470](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neo-125m/blob/main/results_2023-10-18T09-42-25.890470.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": 0.0016778523489932886, "em_stderr": 0.0004191330178826801, "f1": 0.03690436241610747, "f1_stderr": 0.0011592977848577672, "acc": 0.2603955425321017, "acc_stderr": 0.007779096578699754 }, "harness|drop|3": { "em": 0.0016778523489932886, "em_stderr": 0.0004191330178826801, "f1": 0.03690436241610747, "f1_stderr": 0.0011592977848577672 }, "harness|gsm8k|5": { "acc": 0.003032600454890068, "acc_stderr": 0.0015145735612245494 }, "harness|winogrande|5": { "acc": 0.5177584846093133, "acc_stderr": 0.014043619596174959 } } ``` ### 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]

adamo1139/toxic-dpo-natural-v3

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

jason-lee08/TinyStories_Mars

--- dataset_info: features: - name: prompts dtype: string - name: stories dtype: string splits: - name: train num_bytes: 5569066 num_examples: 3364 download_size: 2292233 dataset_size: 5569066 --- # Dataset Card for "TinyStories_Mars" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TinyPixel/air

--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string - name: category dtype: string - name: question_id dtype: float64 splits: - name: train num_bytes: 43852716 num_examples: 27729 download_size: 23510335 dataset_size: 43852716 --- # Dataset Card for "air" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

d0rj/OpenOrca-ru

--- dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 11568757682 num_examples: 4233923 download_size: 5699482220 dataset_size: 11568757682 size_categories: - 1M<n<10M language_creators: - translated language: - ru multilinguality: - monolingual pretty_name: Dolphin (ru) source_datasets: - Open-Orca/OpenOrca license: mit tags: - ChatGPT - instruct - instruct-tune task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation paperswithcode_id: orca-progressive-learning-from-complex --- # OpenOrca-ru ## Dataset Description - **Paper:** https://arxiv.org/abs/2306.02707 This is translated version of [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) into Russian.

distilled-one-sec-cv12-each-chunk-uniq/chunk_142

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1078700212.0 num_examples: 210191 download_size: 1104583624 dataset_size: 1078700212.0 --- # Dataset Card for "chunk_142" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

totally-not-an-llm/EverythingLM-data-V3

--- license: mit --- # EverythingLM V3 Dataset **EverythingLM V3** is a diverse instruct dataset consisting of roughly 1.1k of sysprompt-user-assistant triads. These were generated using principles from both evol-instruct and Orca. The dataset encompasses a wide array of topics and interactions. ### Diferences from V2 * Used march gpt-4 instead of latest * Dynamically adjusted temperature based on the task * Much more diverse (8 new categories) * Flesch hints * 10% more data * Better filtering * Overall refined dataset generation pipeline ### Category distribution ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6464317c38083255f676ff38/J2TRUtw1p8M2NLouxwpyh.png) \*These values represent the data as generated, but slight filtering has been applied, so values might be a bit different.

hml707/test

--- license: apache-2.0 ---

kristmh/high_vs_randommin_100_issues_per_repo

--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validate path: data/validate-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text_clean dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 13172272 num_examples: 11681 - name: train num_bytes: 103353722 num_examples: 93441 - name: validate num_bytes: 13025230 num_examples: 11680 download_size: 61083853 dataset_size: 129551224 --- # Dataset Card for "high_vs_randommin_100_issues_per_repo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

japanese-asr/whisper_transcriptions.reazonspeech.all_60

--- dataset_info: config_name: all features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 29261046421.0 num_examples: 256966 download_size: 29028844193 dataset_size: 29261046421.0 configs: - config_name: all data_files: - split: train path: all/train-* ---

greathero/evenmorex13-newothersmallerthreeclass-newercontrailsvalidationdataset

--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 15125878.8 num_examples: 1800 download_size: 3155745 dataset_size: 15125878.8 configs: - config_name: default data_files: - split: train path: data/train-* ---

joshuasundance/mtg-coloridentity-multilabel-classification

--- dataset_info: features: - name: text dtype: string - name: labels sequence: int64 splits: - name: train num_bytes: 5011317.077050539 num_examples: 22208 - name: test num_bytes: 1253054.9229494615 num_examples: 5553 download_size: 2405205 dataset_size: 6264372 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: mit task_categories: - text-classification task_ids: - multi-label-classification language: - en tags: - mtg - multilabel - magic pretty_name: Magic the Gathering Color Identity Multilabel Classification size_categories: - 10K<n<100K --- This dataset was made specifically for multilabel classification using the following process: 1. Downloading https://mtgjson.com/api/v5/AtomicCards.json.bz2 on January 10, 2024 2. Encoding color identity of each card into the `labels` feature ```python colors = ['B', 'G', 'R', 'U', 'W'] b = [1, 0, 0, 0, 0] bw = [1, 0, 0, 0, 1] gru = [0, 1, 1, 1, 0] # and so on ``` 3. Concatenating card name and card text into the `text` feature 4. `split = ds['train'].train_test_split(test_size=0.2)` 5. `split.push_to_hub("mtg-coloridentity-multilabel-classification")`

TinyPixel/fish-1

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 4680929994 num_examples: 2840090 download_size: 2704444515 dataset_size: 4680929994 --- # Dataset Card for "fish-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jhaberbe/lipid-droplets-v2

--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 12799326.0 num_examples: 10 download_size: 12808424 dataset_size: 12799326.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

MicPie/unpredictable_cluster24

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - apache-2.0 multilinguality: - monolingual pretty_name: UnpredicTable-cluster24 size_categories: - 100K<n<1M source_datasets: [] task_categories: - multiple-choice - question-answering - zero-shot-classification - text2text-generation - table-question-answering - text-generation - text-classification - tabular-classification task_ids: - multiple-choice-qa - extractive-qa - open-domain-qa - closed-domain-qa - closed-book-qa - open-book-qa - language-modeling - multi-class-classification - natural-language-inference - topic-classification - multi-label-classification - tabular-multi-class-classification - tabular-multi-label-classification --- # Dataset Card for "UnpredicTable-cluster24" - Dataset of Few-shot Tasks from Tables ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://ethanperez.net/unpredictable - **Repository:** https://github.com/JunShern/few-shot-adaptation - **Paper:** Few-shot Adaptation Works with UnpredicTable Data - **Point of Contact:** junshern@nyu.edu, perez@nyu.edu ### Dataset Summary The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. There are several dataset versions available: * [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites. * [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites. * [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset. * UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings): * [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low) * [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium) * [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) * UnpredicTable data subsets based on the website of origin: * [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com) * [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net) * [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com) * [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com) * [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com) * [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com) * [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org) * [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org) * [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com) * [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com) * [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com) * [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com) * [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com) * [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com) * [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com) * [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com) * [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com) * [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org) * [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org) * [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org) * UnpredicTable data subsets based on clustering (for the clustering details please see our publication): * [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00) * [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01) * [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02) * [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03) * [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04) * [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05) * [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06) * [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07) * [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08) * [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09) * [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10) * [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11) * [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12) * [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13) * [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14) * [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15) * [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16) * [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17) * [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18) * [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19) * [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20) * [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21) * [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22) * [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23) * [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24) * [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25) * [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26) * [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27) * [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28) * [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29) * [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise) ### Supported Tasks and Leaderboards Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc. The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset. ### Languages English ## Dataset Structure ### Data Instances Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from. There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'. ### Data Fields 'task': task identifier 'input': column elements of a specific row in the table. 'options': for multiple choice classification, it provides the options to choose from. 'output': target column element of the same row as input. 'pageTitle': the title of the page containing the table. 'outputColName': output column name 'url': url to the website containing the table 'wdcFile': WDC Web Table Corpus file ### Data Splits The UnpredicTable datasets do not come with additional data splits. ## Dataset Creation ### Curation Rationale Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning. ### Source Data #### Initial Data Collection and Normalization We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline. #### Who are the source language producers? The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/). ### Annotations #### Annotation process Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low), [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication. #### Who are the annotators? Annotations were carried out by a lab assistant. ### Personal and Sensitive Information The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset. ## Considerations for Using the Data ### Social Impact of Dataset This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations. ### Discussion of Biases Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset. ### Other Known Limitations No additional known limitations. ## Additional Information ### Dataset Curators Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez ### Licensing Information Apache 2.0 ### Citation Information ``` @misc{chan2022few, author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan}, title = {Few-shot Adaptation Works with UnpredicTable Data}, publisher={arXiv}, year = {2022}, url = {https://arxiv.org/abs/2208.01009} } ```

danielpark/mquad-v1

--- license: apache-2.0 task_categories: - question-answering - text-generation language: - en - ko tags: - biology pretty_name: Medical domain QA dataset for training a medical chatbot. --- # MQuAD The Medical Question and Answering dataset(MQuAD) has been refined, including the following datasets. You can download it through the Hugging Face dataset. Use the DATASETS method as follows. ## Quick Guide ```python from datasets import load_dataset dataset = load_dataset("danielpark/MQuAD-v1") ``` Medical Q/A datasets gathered from the following websites. - eHealth Forum - iCliniq - Question Doctors - WebMD Data was gathered at the 5th of May 2017. The MQuAD provides embedded question and answer arrays in string format, so it is recommended to convert the string-formatted arrays into float format as follows. This measure has been applied to save resources and time used for embedding. ```python from datasets import load_dataset from utilfunction import col_convert import pandas as pd qa = load_dataset("danielpark/MQuAD-v1", "csv") df_qa = pd.DataFrame(qa['train']) df_qa = col_convert(df_qa, ['Q_FFNN_embeds', 'A_FFNN_embeds']) ```

cahya/instructions-test

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 16048 num_examples: 22 download_size: 15127 dataset_size: 16048 --- # Dataset Card for "instructions-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

thefcraft/civitai-stable-diffusion-337k

--- annotations_creators: - no-annotation language_creators: - thefcraft language: - en pretty_name: civitai-stable-diffusion-337k size_categories: - 1M<n<10M source_datasets: - civitai --- ### How to Use ``` from datasets import load_dataset dataset = load_dataset("thefcraft/civitai-stable-diffusion-337k") print(dataset['train'][0]) ``` ### download images download zip files from images dir https://huggingface.co/datasets/thefcraft/civitai-stable-diffusion-337k/tree/main/images it contains some images with id ``` from zipfile import ZipFile with ZipFile("filename.zip", 'r') as zObject: zObject.extractall() ``` ### Dataset Summary GitHub URL:- https://github.com/thefcraft/civitai-stable-diffusion-337k dataset:- civitai-stable-diffusion-337k this dataset contains 337k civitai images url with prompts etc. i use civitai api to get all prompts. project:- https://github.com/thefcraft/nsfw-prompt-detection-sd I train a model on this dataset DATA STRUCTURE for othertype/civitai.json:- ```{ 'items':[ {'id': 100657, 'url': 'https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2338276a-87f7-4a1e-f92a-776a18ee4200/width=768/2338276a-87f7-4a1e-f92a-776a18ee4200.jpeg', 'hash': 'U5Exz_00.8D$t89Z%M0100~VD*RktQxaIU~p', 'width': 768, 'height': 1368, 'nsfw': True, 'createdAt': '2023-02-14T10:05:11.498Z', 'postId': 60841, 'stats': {'cryCount': 0, 'laughCount': 0, 'likeCount': 26, 'dislikeCount': 0, 'heartCount': 50, 'commentCount': 4}, 'meta': {'ENSD': '31337', 'Size': '512x912', 'seed': 3994946333, 'Model': 'AbyssOrangeMix2_sfw', 'steps': 20, 'prompt': '<lora:hiqcg_body-epoch-000004:0.5>, <lora:hiqcg_face-epoch-000004:0.4>, hiqcgbody, hiqcgface, 1girl, full body, standing, \ndetailed skin texture, detailed cloth texture, beautiful detailed face,\nmasterpiece, best quality, ultra detailed, 8k, intricate details,', 'sampler': 'DPM++ 2M Karras', 'cfgScale': 7, 'Clip skip': '2', 'resources': [{'hash': '038ba203d8', 'name': 'AbyssOrangeMix2_sfw', 'type': 'model'}], 'Model hash': '038ba203d8', 'Hires upscale': '1.5', 'Hires upscaler': 'Latent', 'negativePrompt': 'EasyNegative, extra fingers,fewer fingers, multiple girls, multiple views,', 'Denoising strength': '0.6'}, 'username': 'NeoClassicalRibbon'}, {..}, ..], 'metadata':{'totalItems': 327145} } ```

anderloh/MotorizedTransportSplit

--- dataset_info: - config_name: 2Class features: - name: audio dtype: audio - name: label dtype: class_label: names: '0': Helicopter '1': Racecar splits: - name: train num_bytes: 620401860.871 num_examples: 2769 - name: test num_bytes: 351989787.33 num_examples: 1571 download_size: 972417488 dataset_size: 972391648.201 - config_name: 5Class features: - name: audio dtype: audio - name: label dtype: class_label: names: '0': Helicopter '1': Jet '2': Racecar '3': Trains '4': Truck splits: - name: train num_bytes: 1860497842.936 num_examples: 8304 - name: test num_bytes: 761781853.0 num_examples: 3400 download_size: 2621469388 dataset_size: 2622279695.936 configs: - config_name: 2Class data_files: - split: train path: 2Class/train-* - split: test path: 2Class/test-* - config_name: 5Class data_files: - split: train path: 5Class/train-* - split: test path: 5Class/test-* --- # Dataset Card for "MotorizedTransportSplit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/yu_mei_ren_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yu_mei_ren/虞美人/虞美人 (Fate/Grand Order) This is the dataset of yu_mei_ren/虞美人/虞美人 (Fate/Grand Order), containing 500 images and their tags. The core tags of this character are `long_hair, brown_hair, breasts, very_long_hair, earrings, medium_breasts, red_eyes, ear_piercing, multiple_earrings, glasses, brown_eyes, braid, 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 | 500 | 825.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yu_mei_ren_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 702.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yu_mei_ren_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1256 | 1.31 GiB | [Download](https://huggingface.co/datasets/CyberHarem/yu_mei_ren_fgo/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/yu_mei_ren_fgo', 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 | 5 | ![](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, black_dress, black_jacket, center_opening, choker, cleavage, collarbone, fur-trimmed_jacket, jewelry, open_jacket, piercing, revealing_clothes, solo, strapless_dress, looking_at_viewer, ribbon-trimmed_dress, simple_background, upper_body, bare_shoulders, hair_between_eyes, long_sleeves, open_mouth, white_background, closed_mouth, navel, sidelocks | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_dress, center_opening, choker, cleavage, collarbone, jewelry, long_sleeves, looking_at_viewer, navel, revealing_clothes, solo, strapless_dress, black_jacket, fur-trimmed_jacket, open_jacket, piercing, bare_shoulders, closed_mouth, ribbon-trimmed_dress, smile, hair_between_eyes, red_background, simple_background | | 2 | 14 | ![](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, bare_shoulders, black_dress, center_opening, choker, revealing_clothes, ribbon-trimmed_dress, solo, strapless_dress, black_gloves, collarbone, elbow_gloves, jewelry, cleavage, looking_at_viewer, navel, piercing, blush, closed_mouth, simple_background, white_background | | 3 | 10 | ![](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_dress, black_gloves, choker, elbow_gloves, holding_sword, jewelry, looking_at_viewer, piercing, revealing_clothes, solo, strapless_dress, bare_shoulders, center_opening, collarbone, closed_mouth, dual_wielding, navel, simple_background, cleavage, ribbon-trimmed_dress, white_background | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, black_dress, jewelry, long_sleeves, piercing, simple_background, solo, white_background, black_gloves, choker, cleavage, collarbone, fur-trimmed_jacket, looking_at_viewer, blush, closed_mouth, black_jacket, center_opening, fur_collar, head_rest, upper_body | | 5 | 6 | ![](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, black-framed_eyewear, black_dress, closed_mouth, elbow_gloves, jewelry, piercing, single_braid, solo, black_gloves, looking_at_viewer, choker | | 6 | 9 | ![](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, black-framed_eyewear, black_dress, blue_dress, ribbon-trimmed_dress, black_gloves, blush, elbow_gloves, ribbed_dress, single_braid, solo, arm_strap, center_opening, open_mouth, braided_ponytail, jewelry, layered_dress, looking_at_viewer, choker, piercing, strapless_dress, pinstripe_pattern | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, bare_shoulders, detached_collar, fake_animal_ears, jewelry, playboy_bunny, rabbit_ears, single_braid, solo, black-framed_eyewear, black_leotard, blush, bowtie, braided_ponytail, cleavage, highleg_leotard, looking_at_viewer, strapless_leotard, open_mouth, simple_background, thighs, white_background, wrist_cuffs, black_footwear, collarbone, covered_navel, fishnet_pantyhose, red_bow | | 8 | 9 | ![](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, bare_shoulders, cleavage, looking_at_viewer, navel, collarbone, jewelry, o-ring, solo, choker, single_braid, sling_bikini_top, blush, ocean, outdoors, smile, blue_sky, closed_mouth, day, piercing, beach, cloud, hair_scrunchie, hairclip, sitting, thighs, water | | 9 | 23 | ![](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) | jewelry, 1girl, bare_shoulders, china_dress, braided_ponytail, looking_at_viewer, blue_dress, thighs, blush, brown_pantyhose, single_braid, solo, smile, looking_back, pelvic_curtain, tassel, panties, red_trim, black-framed_eyewear, sideboob | | 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, anus, ass, blush, from_behind, jewelry, looking_at_viewer, looking_back, pussy, solo, thighs, arm_strap, braided_ponytail, piercing, single_braid, sweat, barefoot, completely_nude, feet, lying, mosaic_censoring, pillow | | 11 | 9 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1boy, 1girl, blush, hetero, navel, nipples, collarbone, open_mouth, penis, sweat, choker, jewelry, mosaic_censoring, sex, spread_legs, thighs, vaginal, nude, solo_focus, arm_strap, black_gloves, cowgirl_position, cum_in_pussy, elbow_gloves, girl_on_top, on_back | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_dress | black_jacket | center_opening | choker | cleavage | collarbone | fur-trimmed_jacket | jewelry | open_jacket | piercing | revealing_clothes | solo | strapless_dress | looking_at_viewer | ribbon-trimmed_dress | simple_background | upper_body | bare_shoulders | hair_between_eyes | long_sleeves | open_mouth | white_background | closed_mouth | navel | sidelocks | smile | red_background | black_gloves | elbow_gloves | blush | holding_sword | dual_wielding | fur_collar | head_rest | black-framed_eyewear | single_braid | blue_dress | ribbed_dress | arm_strap | braided_ponytail | layered_dress | pinstripe_pattern | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | black_leotard | bowtie | highleg_leotard | strapless_leotard | thighs | wrist_cuffs | black_footwear | covered_navel | fishnet_pantyhose | red_bow | o-ring | sling_bikini_top | ocean | outdoors | blue_sky | day | beach | cloud | hair_scrunchie | hairclip | sitting | water | china_dress | brown_pantyhose | looking_back | pelvic_curtain | tassel | panties | red_trim | sideboob | anus | ass | from_behind | pussy | sweat | barefoot | completely_nude | feet | lying | mosaic_censoring | pillow | 1boy | hetero | nipples | penis | sex | spread_legs | vaginal | nude | solo_focus | cowgirl_position | cum_in_pussy | girl_on_top | on_back | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------|:---------------|:-----------------|:---------|:-----------|:-------------|:---------------------|:----------|:--------------|:-----------|:--------------------|:-------|:------------------|:--------------------|:-----------------------|:--------------------|:-------------|:-----------------|:--------------------|:---------------|:-------------|:-------------------|:---------------|:--------|:------------|:--------|:-----------------|:---------------|:---------------|:--------|:----------------|:----------------|:-------------|:------------|:-----------------------|:---------------|:-------------|:---------------|:------------|:-------------------|:----------------|:--------------------|:------------------|:-------------------|:----------------|:--------------|:----------------|:---------|:------------------|:--------------------|:---------|:--------------|:-----------------|:----------------|:--------------------|:----------|:---------|:-------------------|:--------|:-----------|:-----------|:------|:--------|:--------|:-----------------|:-----------|:----------|:--------|:--------------|:------------------|:---------------|:-----------------|:---------|:----------|:-----------|:-----------|:-------|:------|:--------------|:--------|:--------|:-----------|:------------------|:-------|:--------|:-------------------|:---------|:-------|:---------|:----------|:--------|:------|:--------------|:----------|:-------|:-------------|:-------------------|:---------------|:--------------|:----------| | 0 | 5 | ![](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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | X | X | X | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 14 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | X | X | | X | | X | X | X | X | X | X | X | | X | | | | X | X | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 10 | ![](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 | X | | X | | | | X | X | X | | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | X | X | X | X | X | | X | | X | | X | | X | X | | | X | | X | X | | | | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 | ![](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 | 9 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | | X | X | | | | X | | X | | X | X | X | X | | | | | | X | | | | | | | X | X | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | | | X | X | | X | | | | X | | X | | X | | X | | | X | X | | | | | | | | X | | | | | X | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 9 | ![](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 | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 23 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | 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mahdibaghbanzadeh/GUE_tf_4

--- dataset_info: features: - name: sequence dtype: string - name: labels dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 2147000 num_examples: 19000 - name: val num_bytes: 113000 num_examples: 1000 - name: test num_bytes: 113000 num_examples: 1000 download_size: 1070830 dataset_size: 2373000 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---

CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880

--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices num_bytes: 692068 num_examples: 1880 - name: fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_ensemble_specific_rices num_bytes: 1329559 num_examples: 1880 - name: fewshot_1_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 1330297 num_examples: 1880 download_size: 997973 dataset_size: 3351924 --- # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_C_T_A_T_SPECIFIC_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Vitorbr2009/voz-indio-treinada

--- license: openrail ---

cmeraki/hindi_eval_general_mcq

--- language: - hi license: cc size_categories: - 1K<n<10K task_categories: - question-answering dataset_info: features: - name: QUESTION dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: TARGET dtype: string - name: SUBJECT dtype: string - name: GRADE dtype: string - name: TOPIC dtype: string - name: SPLIT dtype: string splits: - name: train num_bytes: 881272.5942028986 num_examples: 1653 download_size: 380835 dataset_size: 881272.5942028986 configs: - config_name: default data_files: - split: train path: data/train-* tags: - maths - physics ---

ShoukanLabs/OpenNiji-380001_415000

--- dataset_info: features: - name: image dtype: image - name: url dtype: string - name: prompt dtype: string - name: style dtype: string splits: - name: train num_bytes: 56237791750.702 num_examples: 34999 download_size: 0 dataset_size: 56237791750.702 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "OpenNiji-380001_415000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alpayariyak/something

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 62321431 num_examples: 56037 download_size: 30816818 dataset_size: 62321431 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "something" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yuvalkirstain/PickaPic-ft-pairwise

--- dataset_info: features: - name: good_image dtype: image - name: bad_image dtype: image - name: text dtype: string - name: good_url dtype: string - name: bad_url dtype: string splits: - name: train num_bytes: 8329737112.221633 num_examples: 10313 - name: test num_bytes: 347307956.7783673 num_examples: 430 download_size: 8675583639 dataset_size: 8677045069.0 --- # Dataset Card for "PickaPic-ft-pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yardeny/processed_bert_context_len_128

--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 12001893540.0 num_examples: 15387043 download_size: 4048532411 dataset_size: 12001893540.0 --- # Dataset Card for "processed_bert_context_len_128" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

manot/pothole-segmentation

--- task_categories: - object-detection tags: - roboflow - roboflow2huggingface --- <div align="center"> <img width="640" alt="manot/pothole-segmentation" src="https://huggingface.co/datasets/manot/pothole-segmentation/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['potholes', 'object', 'pothole', 'potholes'] ``` ### Number of Images ```json {'valid': 157, 'test': 80, 'train': 582} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("manot/pothole-segmentation", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d/dataset/3](https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d/dataset/3?ref=roboflow2huggingface) ### Citation ``` @misc{ road-damage-xvt2d_dataset, title = { road damage Dataset }, type = { Open Source Dataset }, author = { abdulmohsen fahad }, howpublished = { \\url{ https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d } }, url = { https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { jun }, note = { visited on 2023-06-13 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on June 13, 2023 at 8:47 AM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com The dataset includes 819 images. Potholes are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.

psroy/mini-platypus-two

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245925 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---