davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
Ezi/test_Up	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: package_name dtype: string - name: review dtype: string - name: date dtype: string - name: star dtype: int64 - name: version_id dtype: int64 splits: - name: train num_bytes: 1508 num_examples: 5 - name: test num_bytes: 956 num_examples: 5 download_size: 9453 dataset_size: 2464 ---
g-ronimo/oasst2_top1_en	--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 10491824 num_examples: 5419 download_size: 5658552 dataset_size: 10491824 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 --- # Dataset Card for "oasst2_top1_en" * Top 1% conversations of https://huggingface.co/datasets/OpenAssistant/oasst2 * language-filtered: en * generated using https://github.com/blancsw/deep_4_all/blob/main/datasets/oasst/convert.py
open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B	--- pretty_name: Evaluation run of vicgalle/SystemHermes-2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [vicgalle/SystemHermes-2-7B](https://huggingface.co/vicgalle/SystemHermes-2-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T11:03:07.781697](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B/blob/main/results_2024-03-21T11-03-07.781697.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\": 0.6338248776795873,\n\ \ \"acc_stderr\": 0.03237220063508142,\n \"acc_norm\": 0.6354461960257087,\n\ \ \"acc_norm_stderr\": 0.03302051993311256,\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5641556907529288,\n\ \ \"mc2_stderr\": 0.015439070980915756\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6228668941979523,\n \"acc_stderr\": 0.0141633668961926,\n\ \ \"acc_norm\": 0.6501706484641638,\n \"acc_norm_stderr\": 0.013936809212158296\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6506671977693687,\n\ \ \"acc_stderr\": 0.0047578490234119605,\n \"acc_norm\": 0.8404700258912567,\n\ \ \"acc_norm_stderr\": 0.003654212329516619\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.042849586397534015,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.042849586397534015\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.53,\n\ \ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.037242495958177295,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.037242495958177295\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4649122807017544,\n\ \ \"acc_stderr\": 0.04692008381368909,\n \"acc_norm\": 0.4649122807017544,\n\ \ \"acc_norm_stderr\": 0.04692008381368909\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778405,\n \"\ acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778405\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7774193548387097,\n\ \ \"acc_stderr\": 0.023664216671642514,\n \"acc_norm\": 0.7774193548387097,\n\ \ \"acc_norm_stderr\": 0.023664216671642514\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6282051282051282,\n \"acc_stderr\": 0.024503472557110936,\n\ \ \"acc_norm\": 0.6282051282051282,\n \"acc_norm_stderr\": 0.024503472557110936\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \ \ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8275229357798165,\n \"acc_stderr\": 0.016197807956848036,\n \"\ acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.016197807956848036\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"\ acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676173,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676173\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137276,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137276\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\ \ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.04058042015646034,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.04058042015646034\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8160919540229885,\n\ \ \"acc_stderr\": 0.013853724170922524,\n \"acc_norm\": 0.8160919540229885,\n\ \ \"acc_norm_stderr\": 0.013853724170922524\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526502,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526502\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.311731843575419,\n\ \ \"acc_stderr\": 0.015491756531894638,\n \"acc_norm\": 0.311731843575419,\n\ \ \"acc_norm_stderr\": 0.015491756531894638\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.024404394928087866,\n\ \ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.024404394928087866\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600713,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600713\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303956,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303956\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n\ \ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806304,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806304\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139968,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139968\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7960199004975125,\n\ \ \"acc_stderr\": 0.02849317624532607,\n \"acc_norm\": 0.7960199004975125,\n\ \ \"acc_norm_stderr\": 0.02849317624532607\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368036,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368036\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5641556907529288,\n\ \ \"mc2_stderr\": 0.015439070980915756\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698327\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6156178923426838,\n \ \ \"acc_stderr\": 0.013399219253698184\n }\n}\n```" repo_url: https://huggingface.co/vicgalle/SystemHermes-2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|arc:challenge\|25_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-21T11-03-07.781697.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|gsm8k\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hellaswag\|10_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T11-03-07.781697.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T11-03-07.781697.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '/details_harness\|winogrande\|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-21T11-03-07.781697.parquet' - config_name: results data_files: - split: 2024_03_21T11_03_07.781697 path: - results_2024-03-21T11-03-07.781697.parquet - split: latest path: - results_2024-03-21T11-03-07.781697.parquet --- # Dataset Card for Evaluation run of vicgalle/SystemHermes-2-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [vicgalle/SystemHermes-2-7B](https://huggingface.co/vicgalle/SystemHermes-2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T11:03:07.781697](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B/blob/main/results_2024-03-21T11-03-07.781697.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": 0.6338248776795873, "acc_stderr": 0.03237220063508142, "acc_norm": 0.6354461960257087, "acc_norm_stderr": 0.03302051993311256, "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5641556907529288, "mc2_stderr": 0.015439070980915756 }, "harness\|arc:challenge\|25": { "acc": 0.6228668941979523, "acc_stderr": 0.0141633668961926, "acc_norm": 0.6501706484641638, "acc_norm_stderr": 0.013936809212158296 }, "harness\|hellaswag\|10": { "acc": 0.6506671977693687, "acc_stderr": 0.0047578490234119605, "acc_norm": 0.8404700258912567, "acc_norm_stderr": 0.003654212329516619 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.562962962962963, "acc_stderr": 0.042849586397534015, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.042849586397534015 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.047609522856952344, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952344 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6069364161849711, "acc_stderr": 0.037242495958177295, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.037242495958177295 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4649122807017544, "acc_stderr": 0.04692008381368909, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.04692008381368909 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778405, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778405 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642514, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642514 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709443, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6282051282051282, "acc_stderr": 0.024503472557110936, "acc_norm": 0.6282051282051282, "acc_norm_stderr": 0.024503472557110936 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.680672268907563, "acc_stderr": 0.030283995525884396, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.030283995525884396 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8275229357798165, "acc_stderr": 0.016197807956848036, "acc_norm": 0.8275229357798165, "acc_norm_stderr": 0.016197807956848036 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8088235294117647, "acc_stderr": 0.027599174300640766, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.027599174300640766 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676173, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676173 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6681614349775785, "acc_stderr": 0.031602951437766785, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.031602951437766785 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7786259541984732, "acc_stderr": 0.036412970813137276, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.036412970813137276 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.03957835471980981, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.03957835471980981 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7914110429447853, "acc_stderr": 0.031921934489347235, "acc_norm": 0.7914110429447853, "acc_norm_stderr": 0.031921934489347235 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.04058042015646034, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.04058042015646034 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8160919540229885, "acc_stderr": 0.013853724170922524, "acc_norm": 0.8160919540229885, "acc_norm_stderr": 0.013853724170922524 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7254335260115607, "acc_stderr": 0.02402774515526502, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.02402774515526502 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.311731843575419, "acc_stderr": 0.015491756531894638, "acc_norm": 0.311731843575419, "acc_norm_stderr": 0.015491756531894638 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.761437908496732, "acc_stderr": 0.024404394928087866, "acc_norm": 0.761437908496732, "acc_norm_stderr": 0.024404394928087866 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600713, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600713 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5035460992907801, "acc_stderr": 0.02982674915328092, "acc_norm": 0.5035460992907801, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303956, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303956 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6617647058823529, "acc_stderr": 0.028739328513983572, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.028739328513983572 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806304, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806304 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.746938775510204, "acc_stderr": 0.02783302387139968, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.02783302387139968 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7960199004975125, "acc_stderr": 0.02849317624532607, "acc_norm": 0.7960199004975125, "acc_norm_stderr": 0.02849317624532607 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197768, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197768 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8128654970760234, "acc_stderr": 0.029913127232368036, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.029913127232368036 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5641556907529288, "mc2_stderr": 0.015439070980915756 }, "harness\|winogrande\|5": { "acc": 0.7734806629834254, "acc_stderr": 0.011764149054698327 }, "harness\|gsm8k\|5": { "acc": 0.6156178923426838, "acc_stderr": 0.013399219253698184 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More 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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]
globis-university/aozorabunko-clean	--- license: cc-by-4.0 task_categories: - text-generation - text-classification language: - ja size_categories: - 10K<n<100K --- # Overview This dataset provides a convenient and user-friendly format of data from [Aozora Bunko (青空文庫)](https://www.aozora.gr.jp/), a website that compiles public-domain books in Japan, ideal for Machine Learning applications. [For Japanese] 日本語での概要説明を Qiita に記載しました: https://qiita.com/akeyhero/items/b53eae1c0bc4d54e321f # Methodology The code to reproduce this dataset is made available on GitHub: [globis-org/aozorabunko-exctractor](https://github.com/globis-org/aozorabunko-extractor). ## 1. Data collection We firstly downloaded the [CSV file that lists all works](https://www.aozora.gr.jp/index_pages/person_all.html). The information extracted from this CSV is incorporated into the `meta` field. Next, we filtered out any books not categorized as public domain. We retrieved the main text of each book corresponding to every row in the CSV and incorporated it into the `text` field in UTF-8. ## 2. Deduplication We removed entries where the `図書カードURL` (Library card URL) in this CSV did not match with the `作品ID` (Work ID) and `人物ID` (Person ID). In addition, entries with text identical to previously encountered text were discarded. ## 3. Cleaning The data in the `text` field was then cleaned in the following sequence: 1. Convert new lines to `\n` 2. Remove headers 3. Remove footnotes and add them to the `footnote` field 4. Convert inserted notes into regular parenthetical text 5. Remove ruby (phonetic guides) 6. Convert specific characters, such as external characters and iteration marks, into standard Unicode characters 7. Remove any remaining markup 8. Remove leading and trailing new lines and horizontal rules # Tips If you prefer to employ only modern Japanese, you can filter entries with: `row["meta"]["文字遣い種別"] == "新字新仮名"`. # Example ```py >>> from datasets import load_dataset >>> ds = load_dataset('globis-university/aozorabunko-clean') >>> ds DatasetDict({ train: Dataset({ features: ['text', 'footnote', 'meta'], num_rows: 16951 }) }) >>> ds = ds.filter(lambda row: row['meta']['文字遣い種別'] == '新字新仮名') # only modern Japanese >>> ds DatasetDict({ train: Dataset({ features: ['text', 'footnote', 'meta'], num_rows: 10246 }) }) >>> book = ds['train'][0] # one of the works >>> book['meta']['作品名'] 'ウェストミンスター寺院' >>> text = book['text'] # main content >>> len(text) 10639 >>> print(text[:100]) 深いおどろきにうたれて、名高いウェストミンスターに真鍮や石の記念碑となってすべての王侯貴族が集まっているのをみれば、今はさげすみも、ほこりも、見栄もない。善にかえった貴人の姿、華美と俗世の ``` # License CC BY 4.0
CyberHarem/li_sushang_honkai3	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of li_sushang (Houkai 3rd) This is the dataset of li_sushang (Houkai 3rd), containing 190 images and their tags. The core tags of this character are `brown_hair, long_hair, bangs, breasts, hair_ornament, brown_eyes, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:--------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 190 \| 359.57 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 190 \| 172.46 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 456 \| 361.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 190 \| 301.38 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 456 \| 552.81 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/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/li_sushang_honkai3', 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 \| 13 \| ![](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, bare_shoulders, china_dress, closed_mouth, fingerless_gloves, looking_at_viewer, smile, solo, white_dress, white_gloves, elbow_gloves \| \| 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, bare_shoulders, china_dress, closed_mouth, elbow_gloves, fingerless_gloves, holding_sword, looking_at_viewer, smile, solo, white_dress, white_gloves \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blush, nipples, cum_in_pussy, hetero, solo_focus, multiple_penises, open_mouth, vaginal, 3boys, cum_on_breasts, double_handjob, ejaculation, gangbang, navel, nude, piercing, pubic_hair, pubic_tattoo, spread_legs, thighhighs, tongue_out \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| china_dress \| closed_mouth \| fingerless_gloves \| looking_at_viewer \| smile \| solo \| white_dress \| white_gloves \| elbow_gloves \| holding_sword \| blush \| nipples \| cum_in_pussy \| hetero \| solo_focus \| multiple_penises \| open_mouth \| vaginal \| 3boys \| cum_on_breasts \| double_handjob \| ejaculation \| gangbang \| navel \| nude \| piercing \| pubic_hair \| pubic_tattoo \| spread_legs \| thighhighs \| tongue_out \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:--------------\|:---------------\|:--------------------\|:--------------------\|:--------\|:-------\|:--------------\|:---------------\|:---------------\|:----------------\|:--------\|:----------\|:---------------\|:---------\|:-------------\|:-------------------\|:-------------\|:----------\|:--------\|:-----------------\|:-----------------\|:--------------\|:-----------\|:--------\|:-------\|:-----------\|:-------------\|:---------------\|:--------------\|:-------------\|:-------------\| \| 0 \| 13 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](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 \| X \|
d0rj/hh-rlhf-ru	--- language_creators: - translated language: - ru multilinguality: - monolingual size_categories: - 100K<n<1M pretty_name: HH for RLHF (ru) source_datasets: - Anthropic/hh-rlhf license: mit tags: - human-feedback - ChatGPT - reward dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 573845356.0 num_examples: 160800 - name: test num_bytes: 30792414.0 num_examples: 8552 download_size: 281014419 dataset_size: 604637770.0 --- # Dataset Card for "hh-rlhf-ru" This is translated version of [Anthropic/hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) into Russian.
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647_random	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_1_bs_16 num_bytes: 86816473.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 90734475.125 num_examples: 5647 download_size: 169654260 dataset_size: 177550948.25 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KETI-AIR/KITD_SAMPLE	--- license: cc-by-nc-sa-4.0 dataset_info: features: - name: input dtype: string - name: output dtype: string - name: data_name dtype: string - name: data_split_name dtype: string - name: data_index_by_user dtype: int32 splits: - name: FULL num_bytes: 5331202141 num_examples: 5409719 - name: SAMPLE num_bytes: 9462273 num_examples: 4242 download_size: 2014165075 dataset_size: 5340664414 ---
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-135000	--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 661472 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---
pa-shk/sberquad	--- dataset_info: - config_name: docs features: - name: doc dtype: string splits: - name: train num_bytes: 18450007 num_examples: 13489 download_size: 9682240 dataset_size: 18450007 - config_name: qrels features: - name: query dtype: string - name: relevant_docs sequence: int64 splits: - name: train num_bytes: 6111481 num_examples: 45326 - name: validation num_bytes: 677073 num_examples: 5036 - name: test num_bytes: 3226461 num_examples: 23935 download_size: 4620507 dataset_size: 10015015 configs: - config_name: docs data_files: - split: train path: docs/train-* - config_name: qrels data_files: - split: train path: qrels/train-* - split: validation path: qrels/validation-* - split: test path: qrels/test-* ---
defog/wikisql	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 5525298 num_examples: 1000 download_size: 761250 dataset_size: 5525298 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wikisql" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yzhuang/metatree_pendigits	--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float64 - name: y dtype: int64 splits: - name: train num_bytes: 1143744 num_examples: 7728 - name: validation num_bytes: 483072 num_examples: 3264 download_size: 1332707 dataset_size: 1626816 --- # Dataset Card for "metatree_pendigits" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
anan-2024/twitter_dataset_1712979666	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 22965 num_examples: 51 download_size: 12493 dataset_size: 22965 configs: - config_name: default data_files: - split: train path: data/train-* ---
bdice/rapids-codegen	--- dataset_info: features: - name: repo_id dtype: string - name: file_path dtype: string - name: content dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 378315950 num_examples: 16827 download_size: 151107014 dataset_size: 378315950 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "rapids-codegen" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chanwit/ultrachat_200k_filtered	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train_sft num_bytes: 1396561957 num_examples: 207646 - name: test_sft num_bytes: 154634580 num_examples: 23082 download_size: 813170063 dataset_size: 1551196537 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* ---
tner/wikiann	--- language: - ace - bg - da - fur - ilo - lij - mzn - qu - su - vi - af - bh - de - fy - io - lmo - nap - rm - sv - vls - als - bn - diq - ga - is - ln - nds - ro - sw - vo - am - bo - dv - gan - it - lt - ne - ru - szl - wa - an - br - el - gd - ja - lv - nl - rw - ta - war - ang - bs - eml - gl - jbo - nn - sa - te - wuu - ar - ca - en - gn - jv - mg - no - sah - tg - xmf - arc - eo - gu - ka - mhr - nov - scn - th - yi - arz - cdo - es - hak - kk - mi - oc - sco - tk - yo - as - ce - et - he - km - min - or - sd - tl - zea - ast - ceb - eu - hi - kn - mk - os - sh - tr - ay - ckb - ext - hr - ko - ml - pa - si - tt - az - co - fa - hsb - ksh - mn - pdc - ug - ba - crh - fi - hu - ku - mr - pl - sk - uk - zh - bar - cs - hy - ky - ms - pms - sl - ur - csb - fo - ia - la - mt - pnb - so - uz - cv - fr - id - lb - mwl - ps - sq - vec - be - cy - frr - ig - li - my - pt - sr multilinguality: - multilingual size_categories: - 10K<100k task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: WikiAnn --- # Dataset Card for "tner/wikiann" ## Dataset Description - Repository: [T-NER](https://github.com/asahi417/tner) - Paper: [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/) - Dataset: WikiAnn - Domain: Wikipedia - Number of Entity: 3 ### Dataset Summary WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. - Entity Types: `LOC`, `ORG`, `PER` ## Dataset Structure ### Data Instances An example of `train` of `ja` looks as follows. ``` { 'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'], 'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json). ```python { "B-LOC": 0, "B-ORG": 1, "B-PER": 2, "I-LOC": 3, "I-ORG": 4, "I-PER": 5, "O": 6 } ``` ### Data Splits \| language \| train \| validation \| test \| \|:-------------\|--------:\|-------------:\|-------:\| \| ace \| 100 \| 100 \| 100 \| \| bg \| 20000 \| 10000 \| 10000 \| \| da \| 20000 \| 10000 \| 10000 \| \| fur \| 100 \| 100 \| 100 \| \| ilo \| 100 \| 100 \| 100 \| \| lij \| 100 \| 100 \| 100 \| \| mzn \| 100 \| 100 \| 100 \| \| qu \| 100 \| 100 \| 100 \| \| su \| 100 \| 100 \| 100 \| \| vi \| 20000 \| 10000 \| 10000 \| \| af \| 5000 \| 1000 \| 1000 \| \| bh \| 100 \| 100 \| 100 \| \| de \| 20000 \| 10000 \| 10000 \| \| fy \| 1000 \| 1000 \| 1000 \| \| io \| 100 \| 100 \| 100 \| \| lmo \| 100 \| 100 \| 100 \| \| nap \| 100 \| 100 \| 100 \| \| rm \| 100 \| 100 \| 100 \| \| sv \| 20000 \| 10000 \| 10000 \| \| vls \| 100 \| 100 \| 100 \| \| als \| 100 \| 100 \| 100 \| \| bn \| 10000 \| 1000 \| 1000 \| \| diq \| 100 \| 100 \| 100 \| \| ga \| 1000 \| 1000 \| 1000 \| \| is \| 1000 \| 1000 \| 1000 \| \| ln \| 100 \| 100 \| 100 \| \| nds \| 100 \| 100 \| 100 \| \| ro \| 20000 \| 10000 \| 10000 \| \| sw \| 1000 \| 1000 \| 1000 \| \| vo \| 100 \| 100 \| 100 \| \| am \| 100 \| 100 \| 100 \| \| bo \| 100 \| 100 \| 100 \| \| dv \| 100 \| 100 \| 100 \| \| gan \| 100 \| 100 \| 100 \| \| it \| 20000 \| 10000 \| 10000 \| \| lt \| 10000 \| 10000 \| 10000 \| \| ne \| 100 \| 100 \| 100 \| \| ru \| 20000 \| 10000 \| 10000 \| \| szl \| 100 \| 100 \| 100 \| \| wa \| 100 \| 100 \| 100 \| \| an \| 1000 \| 1000 \| 1000 \| \| br \| 1000 \| 1000 \| 1000 \| \| el \| 20000 \| 10000 \| 10000 \| \| gd \| 100 \| 100 \| 100 \| \| ja \| 20000 \| 10000 \| 10000 \| \| lv \| 10000 \| 10000 \| 10000 \| \| nl \| 20000 \| 10000 \| 10000 \| \| rw \| 100 \| 100 \| 100 \| \| ta \| 15000 \| 1000 \| 1000 \| \| war \| 100 \| 100 \| 100 \| \| ang \| 100 \| 100 \| 100 \| \| bs \| 15000 \| 1000 \| 1000 \| \| eml \| 100 \| 100 \| 100 \| \| gl \| 15000 \| 10000 \| 10000 \| \| jbo \| 100 \| 100 \| 100 \| \| map-bms \| 100 \| 100 \| 100 \| \| nn \| 20000 \| 1000 \| 1000 \| \| sa \| 100 \| 100 \| 100 \| \| te \| 1000 \| 1000 \| 1000 \| \| wuu \| 100 \| 100 \| 100 \| \| ar \| 20000 \| 10000 \| 10000 \| \| ca \| 20000 \| 10000 \| 10000 \| \| en \| 20000 \| 10000 \| 10000 \| \| gn \| 100 \| 100 \| 100 \| \| jv \| 100 \| 100 \| 100 \| \| mg \| 100 \| 100 \| 100 \| \| no \| 20000 \| 10000 \| 10000 \| \| sah \| 100 \| 100 \| 100 \| \| tg \| 100 \| 100 \| 100 \| \| xmf \| 100 \| 100 \| 100 \| \| arc \| 100 \| 100 \| 100 \| \| cbk-zam \| 100 \| 100 \| 100 \| \| eo \| 15000 \| 10000 \| 10000 \| \| gu \| 100 \| 100 \| 100 \| \| ka \| 10000 \| 10000 \| 10000 \| \| mhr \| 100 \| 100 \| 100 \| \| nov \| 100 \| 100 \| 100 \| \| scn \| 100 \| 100 \| 100 \| \| th \| 20000 \| 10000 \| 10000 \| \| yi \| 100 \| 100 \| 100 \| \| arz \| 100 \| 100 \| 100 \| \| cdo \| 100 \| 100 \| 100 \| \| es \| 20000 \| 10000 \| 10000 \| \| hak \| 100 \| 100 \| 100 \| \| kk \| 1000 \| 1000 \| 1000 \| \| mi \| 100 \| 100 \| 100 \| \| oc \| 100 \| 100 \| 100 \| \| sco \| 100 \| 100 \| 100 \| \| tk \| 100 \| 100 \| 100 \| \| yo \| 100 \| 100 \| 100 \| \| as \| 100 \| 100 \| 100 \| \| ce \| 100 \| 100 \| 100 \| \| et \| 15000 \| 10000 \| 10000 \| \| he \| 20000 \| 10000 \| 10000 \| \| km \| 100 \| 100 \| 100 \| \| min \| 100 \| 100 \| 100 \| \| or \| 100 \| 100 \| 100 \| \| sd \| 100 \| 100 \| 100 \| \| tl \| 10000 \| 1000 \| 1000 \| \| zea \| 100 \| 100 \| 100 \| \| ast \| 1000 \| 1000 \| 1000 \| \| ceb \| 100 \| 100 \| 100 \| \| eu \| 10000 \| 10000 \| 10000 \| \| hi \| 5000 \| 1000 \| 1000 \| \| kn \| 100 \| 100 \| 100 \| \| mk \| 10000 \| 1000 \| 1000 \| \| os \| 100 \| 100 \| 100 \| \| sh \| 20000 \| 10000 \| 10000 \| \| tr \| 20000 \| 10000 \| 10000 \| \| zh-classical \| 100 \| 100 \| 100 \| \| ay \| 100 \| 100 \| 100 \| \| ckb \| 1000 \| 1000 \| 1000 \| \| ext \| 100 \| 100 \| 100 \| \| hr \| 20000 \| 10000 \| 10000 \| \| ko \| 20000 \| 10000 \| 10000 \| \| ml \| 10000 \| 1000 \| 1000 \| \| pa \| 100 \| 100 \| 100 \| \| si \| 100 \| 100 \| 100 \| \| tt \| 1000 \| 1000 \| 1000 \| \| zh-min-nan \| 100 \| 100 \| 100 \| \| az \| 10000 \| 1000 \| 1000 \| \| co \| 100 \| 100 \| 100 \| \| fa \| 20000 \| 10000 \| 10000 \| \| hsb \| 100 \| 100 \| 100 \| \| ksh \| 100 \| 100 \| 100 \| \| mn \| 100 \| 100 \| 100 \| \| pdc \| 100 \| 100 \| 100 \| \| simple \| 20000 \| 1000 \| 1000 \| \| ug \| 100 \| 100 \| 100 \| \| zh-yue \| 20000 \| 10000 \| 10000 \| \| ba \| 100 \| 100 \| 100 \| \| crh \| 100 \| 100 \| 100 \| \| fi \| 20000 \| 10000 \| 10000 \| \| hu \| 20000 \| 10000 \| 10000 \| \| ku \| 100 \| 100 \| 100 \| \| mr \| 5000 \| 1000 \| 1000 \| \| pl \| 20000 \| 10000 \| 10000 \| \| sk \| 20000 \| 10000 \| 10000 \| \| uk \| 20000 \| 10000 \| 10000 \| \| zh \| 20000 \| 10000 \| 10000 \| \| bar \| 100 \| 100 \| 100 \| \| cs \| 20000 \| 10000 \| 10000 \| \| fiu-vro \| 100 \| 100 \| 100 \| \| hy \| 15000 \| 1000 \| 1000 \| \| ky \| 100 \| 100 \| 100 \| \| ms \| 20000 \| 1000 \| 1000 \| \| pms \| 100 \| 100 \| 100 \| \| sl \| 15000 \| 10000 \| 10000 \| \| ur \| 20000 \| 1000 \| 1000 \| \| bat-smg \| 100 \| 100 \| 100 \| \| csb \| 100 \| 100 \| 100 \| \| fo \| 100 \| 100 \| 100 \| \| ia \| 100 \| 100 \| 100 \| \| la \| 5000 \| 1000 \| 1000 \| \| mt \| 100 \| 100 \| 100 \| \| pnb \| 100 \| 100 \| 100 \| \| so \| 100 \| 100 \| 100 \| \| uz \| 1000 \| 1000 \| 1000 \| \| be-x-old \| 5000 \| 1000 \| 1000 \| \| cv \| 100 \| 100 \| 100 \| \| fr \| 20000 \| 10000 \| 10000 \| \| id \| 20000 \| 10000 \| 10000 \| \| lb \| 5000 \| 1000 \| 1000 \| \| mwl \| 100 \| 100 \| 100 \| \| ps \| 100 \| 100 \| 100 \| \| sq \| 5000 \| 1000 \| 1000 \| \| vec \| 100 \| 100 \| 100 \| \| be \| 15000 \| 1000 \| 1000 \| \| cy \| 10000 \| 1000 \| 1000 \| \| frr \| 100 \| 100 \| 100 \| \| ig \| 100 \| 100 \| 100 \| \| li \| 100 \| 100 \| 100 \| \| my \| 100 \| 100 \| 100 \| \| pt \| 20000 \| 10000 \| 10000 \| \| sr \| 20000 \| 10000 \| 10000 \| \| vep \| 100 \| 100 \| 100 \| ### Citation Information ``` @inproceedings{pan-etal-2017-cross, title = "Cross-lingual Name Tagging and Linking for 282 Languages", author = "Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P17-1178", doi = "10.18653/v1/P17-1178", pages = "1946--1958", abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.", } ```
CyberHarem/yukikaze_kantaicollection	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yukikaze/雪風 (Kantai Collection) This is the dataset of yukikaze/雪風 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `brown_hair, short_hair, brown_eyes, headgear, hair_ornament, 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 \| 500 \| 529.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 329.03 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1130 \| 689.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 478.69 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1130 \| 933.05 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/yukikaze_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blue_sailor_collar, open_mouth, simple_background, smile, solo, speaking_tube_headset, upper_teeth_only, looking_at_viewer, round_teeth, sailor_dress, white_background, yellow_neckerchief, twitter_username, upper_body \| \| 1 \| 19 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, hair_flower, open_mouth, sailor_dress, smile, solo, upper_teeth_only, grey_neckerchief, white_dress, black_sailor_collar, long_sleeves, round_teeth, speaking_tube_headset, blue_sailor_collar, cherry_blossoms, simple_background, white_background, anchor_symbol, pink_flower, blush, cowboy_shot, full_body \| \| 2 \| 10 \| ![](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, hair_flower, sailor_dress, solo, cherry_blossoms, grey_neckerchief, upper_body, white_dress, long_sleeves, simple_background, white_background, black_sailor_collar, grey_necktie, smile, speaking_tube_headset, blue_sailor_collar, blush, looking_at_viewer, closed_mouth, pink_flower \| \| 3 \| 14 \| ![](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, sailor_dress, smile, solo, binoculars, open_mouth, looking_at_viewer, salute, school_uniform \| \| 4 \| 8 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, looking_at_viewer, sailor_dress, solo, white_panties, open_mouth, smile, binoculars, character_name \| \| 5 \| 9 \| ![](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) \| 2girls, open_mouth, sailor_dress, binoculars, blonde_hair, blush, long_hair, smile, white_panties \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, anchor_symbol, cowboy_shot, looking_at_viewer, open_mouth, smile, solo, speaking_tube_headset, straw_hat, sun_hat, sundress, white_dress, collarbone, upper_teeth_only, bow, official_alternate_costume, bag, blush, hair_between_eyes, hat_flower, jewelry, off-shoulder_dress, round_teeth, simple_background, sunflower, white_background \| \| 7 \| 8 \| ![](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, anchor_symbol, blue_sky, cloud, day, looking_at_viewer, open_mouth, outdoors, solo, speaking_tube_headset, straw_hat, sun_hat, sundress, white_dress, smile, sunflower, bow, upper_teeth_only, collarbone, hat_flower, upper_body, yellow_flower \| \| 8 \| 6 \| ![](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, double_bun, official_alternate_costume, open_mouth, solo, looking_at_viewer, smile, upper_teeth_only, white_shirt, blush, upper_body \| \| 9 \| 20 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, solo, looking_at_viewer, white_jacket, school_swimsuit, hooded_jacket, speaking_tube_headset, smile, hoodie, name_tag, open_mouth, blush, collarbone, long_sleeves, swimsuit_under_clothes, blue_one-piece_swimsuit, hair_between_eyes, teeth, black_one-piece_swimsuit, sitting \| \| 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) \| denim_jacket, hair_flower, official_alternate_costume, 1girl, blue_headwear, cowboy_shot, open_mouth, smile, solo, bag, blue_jacket, round_teeth, upper_teeth_only, white_skirt, beret, black_headwear, breast_pocket, cherry_blossoms, long_sleeves, white_dress \| \| 11 \| 5 \| ![](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, open_mouth, solo_focus, bottomless, navel, penis, sailor_dress, sex, small_breasts, spread_legs, vaginal, covered_nipples, cum_in_pussy, see-through, socks, bar_censor, loli, mosaic_censoring, neckerchief, pov, school_uniform, simple_background, sweat, teeth, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blue_sailor_collar \| open_mouth \| simple_background \| smile \| solo \| speaking_tube_headset \| upper_teeth_only \| looking_at_viewer \| round_teeth \| sailor_dress \| white_background \| yellow_neckerchief \| twitter_username \| upper_body \| hair_flower \| grey_neckerchief \| white_dress \| black_sailor_collar \| long_sleeves \| cherry_blossoms \| anchor_symbol \| pink_flower \| blush \| cowboy_shot \| full_body \| grey_necktie \| closed_mouth \| binoculars \| salute \| school_uniform \| white_panties \| character_name \| 2girls \| blonde_hair \| long_hair \| straw_hat \| sun_hat \| sundress \| collarbone \| bow \| official_alternate_costume \| bag \| hair_between_eyes \| hat_flower \| jewelry \| off-shoulder_dress \| sunflower \| blue_sky \| cloud \| day \| outdoors \| yellow_flower \| double_bun \| white_shirt \| white_jacket \| school_swimsuit \| hooded_jacket \| hoodie \| name_tag \| swimsuit_under_clothes \| blue_one-piece_swimsuit \| teeth \| black_one-piece_swimsuit \| sitting \| denim_jacket \| blue_headwear \| blue_jacket \| white_skirt \| beret \| black_headwear \| breast_pocket \| 1boy \| hetero \| solo_focus \| bottomless \| navel \| penis \| sex \| small_breasts \| spread_legs \| vaginal \| covered_nipples \| cum_in_pussy \| see-through \| socks \| bar_censor \| loli \| mosaic_censoring \| neckerchief \| pov \| sweat \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:---------------------\|:-------------\|:--------------------\|:--------\|:-------\|:------------------------\|:-------------------\|:--------------------\|:--------------\|:---------------\|:-------------------\|:---------------------\|:-------------------\|:-------------\|:--------------\|:-------------------\|:--------------\|:----------------------\|:---------------\|:------------------\|:----------------\|:--------------\|:--------\|:--------------\|:------------\|:---------------\|:---------------\|:-------------\|:---------\|:-----------------\|:----------------\|:-----------------\|:---------\|:--------------\|:------------\|:------------\|:----------\|:-----------\|:-------------\|:------\|:-----------------------------\|:------\|:--------------------\|:-------------\|:----------\|:---------------------\|:------------\|:-----------\|:--------\|:------\|:-----------\|:----------------\|:-------------\|:--------------\|:---------------\|:------------------\|:----------------\|:---------\|:-----------\|:-------------------------\|:--------------------------\|:--------\|:---------------------------\|:----------\|:---------------\|:----------------\|:--------------\|:--------------\|:--------\|:-----------------\|:----------------\|:-------\|:---------\|:-------------\|:-------------\|:--------\|:--------\|:------\|:----------------\|:--------------\|:----------\|:------------------\|:---------------\|:--------------\|:--------\|:-------------\|:-------\|:-------------------\|:--------------\|:------\|:--------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 19 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 14 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 8 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| \| X \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 8 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 20 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| X \| \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 5 \| ![](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 \| X \| X \|
freshpearYoon/train_free_42	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 9604916520 num_examples: 10000 download_size: 1451750277 dataset_size: 9604916520 configs: - config_name: default data_files: - split: train path: data/train-* ---
presencesw/pubmed_envi_stage_2_o	--- dataset_info: features: - name: en dtype: string - name: vi dtype: string splits: - name: train num_bytes: 24041184334.24373 num_examples: 10888215 download_size: 10807299020 dataset_size: 24041184334.24373 configs: - config_name: default data_files: - split: train path: data/train-* ---
mmaak/HealthCareMagic-llama2-5k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5739555 num_examples: 5000 download_size: 3223162 dataset_size: 5739555 configs: - config_name: default data_files: - split: train path: data/train-* ---
Yeerchiu/mmm_lmd_16bars	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2016007300 num_examples: 115171 download_size: 326574922 dataset_size: 2016007300 configs: - config_name: default data_files: - split: train path: data/train-* ---
Star3073/Test_Interview	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 12365 num_examples: 15 download_size: 11573 dataset_size: 12365 configs: - config_name: default data_files: - split: train path: data/train-* ---
haturusinghe/sinhala_off-sinhala-to-english	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 5826095 num_examples: 38123 - name: test num_bytes: 340032 num_examples: 2219 download_size: 2331565 dataset_size: 6166127 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
CyberHarem/maury_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of maury/モーリー/莫里 (Azur Lane) This is the dataset of maury/モーリー/莫里 (Azur Lane), containing 29 images and their tags. The core tags of this character are `hair_ornament, hairclip, ahoge, long_hair, blonde_hair, yellow_eyes, hair_between_eyes, 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 \| 29 \| 24.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 29 \| 17.53 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 71 \| 38.71 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 29 \| 23.06 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 71 \| 46.32 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/maury_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 21 \| ![](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, blush, looking_at_viewer, open_mouth, sleeveless_dress, white_sailor_collar, wristband, :d, bare_shoulders, blue_dress, brown_eyes, sailor_dress, collarbone, simple_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| blush \| looking_at_viewer \| open_mouth \| sleeveless_dress \| white_sailor_collar \| wristband \| :d \| bare_shoulders \| blue_dress \| brown_eyes \| sailor_dress \| collarbone \| simple_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------\|:--------------------\|:-------------\|:-------------------\|:----------------------\|:------------\|:-----\|:-----------------\|:-------------\|:-------------\|:---------------\|:-------------\|:--------------------\| \| 0 \| 21 \| ![](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 \|
innodatalabs/rt-inod-finance	--- license: cc-by-sa-4.0 language: en task_categories: - text-generation - translation - summarization - question-answering - sentence-similarity tags: - red teaming labels: domain: finance genre: business docs skill: paraphrasing, Q&A, summarization, translation safety: factuality, toxicity dataset_info: - config_name: default data_files: - split: test path: innodata_finance_test.jsonl features: - name: messages list: - name: role dtype: string - name: content dtype: string - name: expected dtype: string - name: id dtype: string --- # FINANCE dataset Red teaming human-crafted finance dataset.
arbitropy/quac_prompt	--- dataset_info: features: - name: story dtype: string - name: questions sequence: string - name: answers sequence: string - name: source dtype: string - name: prompt sequence: string splits: - name: train num_bytes: 262740905 num_examples: 11567 - name: validation num_bytes: 25350167 num_examples: 1000 download_size: 65921336 dataset_size: 288091072 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-f4ef6e-41949145080	--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: ARTeLab/it5-summarization-fanpage metrics: [] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # 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: ARTeLab/it5-summarization-fanpage * Dataset: cnn_dailymail * Config: 3.0.0 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@neuromentor](https://huggingface.co/neuromentor) for evaluating this model.
vwxyzjn/openhermes-dev__mistralai_Mistral-7B-Instruct-v0.1__1706885434	--- dataset_info: features: - name: source dtype: string - name: skip_prompt_formatting dtype: bool - name: title dtype: 'null' - name: custom_instruction dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: 'null' - name: system_prompt dtype: 'null' - name: idx dtype: 'null' - name: id dtype: 'null' - name: model dtype: 'null' - name: topic dtype: 'null' - name: avatarUrl dtype: 'null' - name: model_name dtype: 'null' - name: language dtype: 'null' - name: views dtype: 'null' - name: hash dtype: 'null' - name: category dtype: string - name: prompt dtype: string - name: chosen_policy 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: rejected_policy dtype: string splits: - name: test_prefs num_bytes: 1823 num_examples: 1 - name: train_prefs num_bytes: 128821 num_examples: 23 download_size: 129887 dataset_size: 130644 configs: - config_name: default data_files: - split: test_prefs path: data/test_prefs-* - split: train_prefs path: data/train_prefs-* ---
jkorsvik/nowiki_abstract_second_scrape_20230201	--- dataset_info: features: - name: url dtype: string - name: date_scraped dtype: string - name: headline dtype: string - name: category dtype: string - name: ingress dtype: string - name: article dtype: string - name: abstract dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 841217948 num_examples: 614918 download_size: 211286623 dataset_size: 841217948 --- # Dataset Card for "nowiki_abstract_second_scrape_20230201" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
stjokerli/TextToText_mnli	--- license: mit ---
open-llm-leaderboard/details_garage-bAInd__Camel-Platypus2-70B	--- pretty_name: Evaluation run of garage-bAInd/Camel-Platypus2-70B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [garage-bAInd/Camel-Platypus2-70B](https://huggingface.co/garage-bAInd/Camel-Platypus2-70B)\ \ 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_garage-bAInd__Camel-Platypus2-70B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-16T06:37:05.018958](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Camel-Platypus2-70B/blob/main/results_2023-10-16T06-37-05.018958.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\": 0.5069211409395973,\n\ \ \"em_stderr\": 0.0051199774044148345,\n \"f1\": 0.559724203020135,\n\ \ \"f1_stderr\": 0.004829732229468497,\n \"acc\": 0.5345469918434537,\n\ \ \"acc_stderr\": 0.01116294273345166\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.5069211409395973,\n \"em_stderr\": 0.0051199774044148345,\n\ \ \"f1\": 0.559724203020135,\n \"f1_stderr\": 0.004829732229468497\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.2395754359363154,\n \ \ \"acc_stderr\": 0.01175686434407741\n },\n \"harness\|winogrande\|5\":\ \ {\n \"acc\": 0.829518547750592,\n \"acc_stderr\": 0.010569021122825909\n\ \ }\n}\n```" repo_url: https://huggingface.co/garage-bAInd/Camel-Platypus2-70B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|arc:challenge\|25_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-18T00:04:49.359575.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T09_15_03.498663 path: - '/details_harness\|drop\|3_2023-09-23T09-15-03.498663.parquet' - split: 2023_10_16T06_37_05.018958 path: - '/details_harness\|drop\|3_2023-10-16T06-37-05.018958.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-16T06-37-05.018958.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T09_15_03.498663 path: - '/details_harness\|gsm8k\|5_2023-09-23T09-15-03.498663.parquet' - split: 2023_10_16T06_37_05.018958 path: - '/details_harness\|gsm8k\|5_2023-10-16T06-37-05.018958.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-16T06-37-05.018958.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hellaswag\|10_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-18T00:04:49.359575.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_18T00_04_49.359575 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-18T00:04:49.359575.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T09_15_03.498663 path: - '/details_harness\|winogrande\|5_2023-09-23T09-15-03.498663.parquet' - split: 2023_10_16T06_37_05.018958 path: - '/details_harness\|winogrande\|5_2023-10-16T06-37-05.018958.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-16T06-37-05.018958.parquet' - config_name: results data_files: - split: 2023_08_18T00_04_49.359575 path: - results_2023-08-18T00:04:49.359575.parquet - split: 2023_09_23T09_15_03.498663 path: - results_2023-09-23T09-15-03.498663.parquet - split: 2023_10_16T06_37_05.018958 path: - results_2023-10-16T06-37-05.018958.parquet - split: latest path: - results_2023-10-16T06-37-05.018958.parquet --- # Dataset Card for Evaluation run of garage-bAInd/Camel-Platypus2-70B ## Dataset Description - Homepage: - Repository: https://huggingface.co/garage-bAInd/Camel-Platypus2-70B - 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 [garage-bAInd/Camel-Platypus2-70B](https://huggingface.co/garage-bAInd/Camel-Platypus2-70B) 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_garage-bAInd__Camel-Platypus2-70B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-16T06:37:05.018958](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Camel-Platypus2-70B/blob/main/results_2023-10-16T06-37-05.018958.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": 0.5069211409395973, "em_stderr": 0.0051199774044148345, "f1": 0.559724203020135, "f1_stderr": 0.004829732229468497, "acc": 0.5345469918434537, "acc_stderr": 0.01116294273345166 }, "harness\|drop\|3": { "em": 0.5069211409395973, "em_stderr": 0.0051199774044148345, "f1": 0.559724203020135, "f1_stderr": 0.004829732229468497 }, "harness\|gsm8k\|5": { "acc": 0.2395754359363154, "acc_stderr": 0.01175686434407741 }, "harness\|winogrande\|5": { "acc": 0.829518547750592, "acc_stderr": 0.010569021122825909 } } ``` ### 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]
jilp00/water-diplomacy-transcripts	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 316351 num_examples: 223 download_size: 173329 dataset_size: 316351 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/nanakusa_nichika_theidolmstershinycolors	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nanakusa_nichika/七草にちか (THE iDOLM@STER: SHINY COLORS) This is the dataset of nanakusa_nichika/七草にちか (THE iDOLM@STER: SHINY COLORS), containing 365 images and their tags. The core tags of this character are `green_hair, short_hair, green_eyes, 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 \| 365 \| 578.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 365 \| 287.94 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 870 \| 631.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 365 \| 491.70 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 870 \| 994.16 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/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/nanakusa_nichika_theidolmstershinycolors', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, looking_at_viewer, solo, white_shirt, blush, hairclip, long_sleeves, sweater, bag, cardigan, holding, simple_background, smile, white_background, white_skirt \| \| 1 \| 35 \| ![](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) \| looking_at_viewer, 1girl, earrings, long_sleeves, frills, solo, nail_polish, smile, neck_ribbon, simple_background, upper_body, blush, jacket, very_long_hair, white_background, red_nails, white_shirt, black_ribbon, belt, open_mouth \| \| 2 \| 9 \| ![](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, bowtie, looking_at_viewer, school_uniform, simple_background, solo, sweater_vest, upper_body, blush, short_sleeves, white_background, white_shirt, striped_bow \| \| 3 \| 14 \| ![](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, bowtie, plaid_skirt, school_uniform, short_sleeves, sweater_vest, looking_at_viewer, pleated_skirt, solo, simple_background, white_shirt, blush, white_background, smile, socks, full_body \| \| 4 \| 12 \| ![](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) \| frills, maid_headdress, solo, 1girl, looking_at_viewer, puffy_short_sleeves, wrist_cuffs, blush, smile, hair_ribbon, short_twintails, simple_background, black_bow, bowtie, enmaided, maid_apron, white_background, upper_body \| \| 5 \| 11 \| ![](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, hair_ornament, smile, solo, white_gloves, idol, looking_at_viewer, blush, dress, open_mouth, blue_skirt, detached_collar, simple_background, sweat, white_background \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, earrings, green_jacket, looking_at_viewer, solo, simple_background, sleeveless, white_background, bare_shoulders, midriff, navel, off_shoulder, ponytail, smile, upper_body, black_gloves, blush, closed_mouth, crop_top, fishnets, medium_breasts, necklace, open_jacket, very_long_hair \| \| 7 \| 9 \| ![](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, simple_background, white_background, blush, looking_at_viewer, navel, solo, collarbone, medium_breasts, panties \| \| 8 \| 18 \| ![](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, looking_at_viewer, outdoors, solo, blush, blue_sky, day, medium_breasts, collarbone, eyewear_on_head, hairclip, heart-shaped_eyewear, navel, ocean, sunglasses, visor_cap, beach, bracelet, cleavage, polka_dot_bikini, twintails, brown_eyes, brown_hair, cloud, smile \| \| 9 \| 7 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, blush, hetero, penis, solo_focus, 1boy, nipples, cum_in_pussy, mosaic_censoring, open_mouth, sex, on_back, spread_legs, vaginal, medium_breasts, missionary, navel, nude, pubic_hair, small_breasts \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| white_shirt \| blush \| hairclip \| long_sleeves \| sweater \| bag \| cardigan \| holding \| simple_background \| smile \| white_background \| white_skirt \| earrings \| frills \| nail_polish \| neck_ribbon \| upper_body \| jacket \| very_long_hair \| red_nails \| black_ribbon \| belt \| open_mouth \| bowtie \| school_uniform \| sweater_vest \| short_sleeves \| striped_bow \| plaid_skirt \| pleated_skirt \| socks \| full_body \| maid_headdress \| puffy_short_sleeves \| wrist_cuffs \| hair_ribbon \| short_twintails \| black_bow \| enmaided \| maid_apron \| hair_ornament \| white_gloves \| idol \| dress \| blue_skirt \| detached_collar \| sweat \| green_jacket \| sleeveless \| bare_shoulders \| midriff \| navel \| off_shoulder \| ponytail \| black_gloves \| closed_mouth \| crop_top \| fishnets \| medium_breasts \| necklace \| open_jacket \| collarbone \| panties \| outdoors \| blue_sky \| day \| eyewear_on_head \| heart-shaped_eyewear \| ocean \| sunglasses \| visor_cap \| beach \| bracelet \| cleavage \| polka_dot_bikini \| twintails \| brown_eyes \| brown_hair \| cloud \| hetero \| penis \| solo_focus \| 1boy \| nipples \| cum_in_pussy \| mosaic_censoring \| sex \| on_back \| spread_legs \| vaginal \| missionary \| nude \| pubic_hair \| small_breasts \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:--------------\|:--------\|:-----------\|:---------------\|:----------\|:------\|:-----------\|:----------\|:--------------------\|:--------\|:-------------------\|:--------------\|:-----------\|:---------\|:--------------\|:--------------\|:-------------\|:---------\|:-----------------\|:------------\|:---------------\|:-------\|:-------------\|:---------\|:-----------------\|:---------------\|:----------------\|:--------------\|:--------------\|:----------------\|:--------\|:------------\|:-----------------\|:----------------------\|:--------------\|:--------------\|:------------------\|:------------\|:-----------\|:-------------\|:----------------\|:---------------\|:-------\|:--------\|:-------------\|:------------------\|:--------\|:---------------\|:-------------\|:-----------------\|:----------\|:--------\|:---------------\|:-----------\|:---------------\|:---------------\|:-----------\|:-----------\|:-----------------\|:-----------\|:--------------\|:-------------\|:----------\|:-----------\|:-----------\|:------\|:------------------\|:-----------------------\|:--------\|:-------------\|:------------\|:--------\|:-----------\|:-----------\|:-------------------\|:------------\|:-------------\|:-------------\|:--------\|:---------\|:--------\|:-------------\|:-------\|:----------\|:---------------\|:-------------------\|:------\|:----------\|:--------------\|:----------\|:-------------\|:-------\|:-------------\|:----------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 35 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| \| X \| \| \| \| \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 14 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 11 \| ![](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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| X \| \| X \| \| \| \| \| \| \| X \| X \| X \| \| X \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 18 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 7 \| ![](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 \|
CyberHarem/himari_bluearchive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of himari/明星ヒマリ/日鞠 (Blue Archive) This is the dataset of himari/明星ヒマリ/日鞠 (Blue Archive), containing 500 images and their tags. The core tags of this character are `mole, long_hair, pointy_ears, mole_under_eye, halo, hairband, purple_eyes, hair_ornament, black_hairband, hair_flower, grey_hair, white_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:--------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 1.01 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/himari_bluearchive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 845.87 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/himari_bluearchive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1308 \| 1.66 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/himari_bluearchive/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/himari_bluearchive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, black_gloves, hair_tubes, long_sleeves, looking_at_viewer, sitting, solo, smile, white_jacket, blush, simple_background, white_background, closed_mouth, wheelchair, white_flower \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blush, simple_background, smile, upper_body, white_background, white_flower, hair_tubes, looking_at_viewer, solo, white_jacket, open_mouth, long_sleeves, black_gloves \| \| 2 \| 31 \| ![](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, barefoot, hair_tubes, solo, toes, long_sleeves, looking_at_viewer, black_gloves, blush, white_jacket, simple_background, sitting, full_body, toenails, black_leggings, legs, soles, white_background, closed_mouth, foot_focus, white_flower, smile, striped_hairband, pants \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, bare_shoulders, blush, hair_tubes, looking_at_viewer, small_breasts, solo, alternate_costume, collarbone, navel, simple_background, sitting, smile, stomach, flower, string_bikini, bare_arms, black_bikini, closed_mouth, side-tie_bikini_bottom, striped_hairband, wheelchair, white_bikini \| \| 4 \| 15 \| ![](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) \| 1boy, 1girl, blush, hetero, solo_focus, flower, hair_tubes, nipples, open_mouth, penis, sex, small_breasts, completely_nude, cum, sweat, vaginal, bar_censor, navel, on_back \| \| 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, alternate_costume, fake_animal_ears, playboy_bunny, rabbit_ears, small_breasts, solo, strapless_leotard, detached_collar, looking_at_viewer, bare_shoulders, black_bowtie, black_pantyhose, blush, hair_tubes, open_mouth, rabbit_tail, smile, white_leotard, wrist_cuffs, ass, black_gloves, fake_tail, simple_background, white_background, white_flower \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_gloves \| hair_tubes \| long_sleeves \| looking_at_viewer \| sitting \| solo \| smile \| white_jacket \| blush \| simple_background \| white_background \| closed_mouth \| wheelchair \| white_flower \| upper_body \| open_mouth \| barefoot \| toes \| full_body \| toenails \| black_leggings \| legs \| soles \| foot_focus \| striped_hairband \| pants \| bare_shoulders \| small_breasts \| alternate_costume \| collarbone \| navel \| stomach \| flower \| string_bikini \| bare_arms \| black_bikini \| side-tie_bikini_bottom \| white_bikini \| 1boy \| hetero \| solo_focus \| nipples \| penis \| sex \| completely_nude \| cum \| sweat \| vaginal \| bar_censor \| on_back \| fake_animal_ears \| playboy_bunny \| rabbit_ears \| strapless_leotard \| detached_collar \| black_bowtie \| black_pantyhose \| rabbit_tail \| white_leotard \| wrist_cuffs \| ass \| fake_tail \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-------------\|:---------------\|:--------------------\|:----------\|:-------\|:--------\|:---------------\|:--------\|:--------------------\|:-------------------\|:---------------\|:-------------\|:---------------\|:-------------\|:-------------\|:-----------\|:-------\|:------------\|:-----------\|:-----------------\|:-------\|:--------\|:-------------\|:-------------------\|:--------\|:-----------------\|:----------------\|:--------------------\|:-------------\|:--------\|:----------\|:---------\|:----------------\|:------------\|:---------------\|:-------------------------\|:---------------\|:-------\|:---------\|:-------------\|:----------\|:--------\|:------\|:------------------\|:------\|:--------\|:----------\|:-------------\|:----------\|:-------------------\|:----------------\|:--------------\|:--------------------\|:------------------\|:---------------\|:------------------\|:--------------\|:----------------\|:--------------\|:------\|:------------\| \| 0 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 31 \| ![](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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| \| X \| X \| X \| X \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 15 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
AI-B/CHI	--- license: unlicense tags: - UNA - DPO - ULMA pretty_name: CHI --- This repository outlines the methodology for creating training sets aimed at aligning a language model with a specific character and persona. The process involves utilizing a Direct Preference Optimization (DPO) dataset to steer the model towards embodying the defined character and persona traits. Following this, a Unified Neutral Alignment (UNA) dataset is employed to moderate any excessive sentiments resulting from the DPO training. The final step involves merging the model realigned with the UNA dataset into the original DPO-trained model, forming a Unified Language Model Alignment (ULMA). ### DPO Training Set (Target Character and Persona) 1. Define Character and Persona: Precisely define the traits, behaviors, and speech patterns of the intended character and persona, including language style, tone, typical responses, and unique characteristics. 2. Dataset Construction: Develop a dataset that reflects these characteristics through dialogues, monologues, and interactions typical of the persona. Ensure the dataset's diversity to encompass various scenarios and responses. 3. Annotation: Label each dataset instance with preference scores or binary labels, indicating its alignment with the target persona for effective DPO implementation. ### UNA Training Set (Neutralizing Extremes) 1. Identify Extremes: Identify extreme positive or negative sentiments in the context of your character, such as overly aggressive or excessively submissive language. 2. Neutral Dataset: Build a dataset representing neutral interactions and responses, focusing on language and replies that are balanced and free from identified extremes. 3. Annotation for Neutrality: Annotate the dataset to promote a neutral, balanced language style, possibly employing a point-wise preference approach similar to DPO. ### Training and Merging Models 1. Train Separate Models: Train one model using the DPO dataset and subsequently realign it using the UNA dataset. Each model will learn distinct aspects: character alignment and neutralization of extremes. 2. Merging Models: Combining two independently trained models into a single unified model is complex and often requires sophisticated techniques and deep understanding of model architectures. For this, we employ the `LazyMergeKit`. 3. Evaluation and Adjustment: Post-merging, assess the unified model to verify if it achieves the intended balance. Iterative refinement of the training datasets and merging process might be necessary based on evaluation outcomes.
SerahAKojenu/Assignment1	--- dataset_info: features: - name: longitude dtype: float64 - name: latitude dtype: float64 - name: housing_median_age dtype: float64 - name: total_rooms dtype: float64 - name: total_bedrooms dtype: float64 - name: population dtype: float64 - name: households dtype: float64 - name: median_income dtype: float64 - name: median_house_value dtype: float64 - name: ocean_proximity dtype: string splits: - name: train num_bytes: 1737680 num_examples: 20640 download_size: 0 dataset_size: 1737680 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Assignment1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa	--- configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_ic_qa path: data/train_ic_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_ic_qa path: data/eval_ic_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: all_docs path: data/all_docs-* - split: all_docs_eval path: data/all_docs_eval-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train_qa num_bytes: 697367 num_examples: 6000 - name: train_ic_qa num_bytes: 4540536 num_examples: 6000 - name: train_recite_qa num_bytes: 4546536 num_examples: 6000 - name: eval_qa num_bytes: 752802 num_examples: 6489 - name: eval_ic_qa num_bytes: 4906186 num_examples: 6489 - name: eval_recite_qa num_bytes: 4912675 num_examples: 6489 - name: all_docs num_bytes: 7126313 num_examples: 10925 - name: all_docs_eval num_bytes: 7125701 num_examples: 10925 - name: train num_bytes: 11672849 num_examples: 16925 - name: validation num_bytes: 4912675 num_examples: 6489 download_size: 31822578 dataset_size: 51193640 --- # Dataset Card for "lmind_nq_train6000_eval6489_v1_recite_qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akanametov/minions-dataset	--- license: mit ---
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_T_D_PNP_GENERIC_OCR_rices_ns_1000	--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text num_bytes: 12334750 num_examples: 1000 download_size: 2117039 dataset_size: 12334750 --- # Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_D_PNP_GENERIC_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
talentlabs/training-data-blog-writer_v03-09-2023	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: title dtype: string - name: article dtype: string - name: text dtype: string splits: - name: train num_bytes: 48639092 num_examples: 9504 download_size: 30032406 dataset_size: 48639092 --- # Dataset Card for "training-data-blog-writer_v03-09-2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MMInstruction/ArxivQA	--- license: cc-by-sa-4.0 task_categories: - image-to-text language: - en tags: - 'vision-language ' - vqa pretty_name: ArxivQA size_categories: - 10K<n<100K --- # Dataset Card for Mutlimodal Arxiv QA ## Dataset Loading Instruction Each line of the `arxivqa.jsonl` file is an example: ``` {"id": "cond-mat-2862", "image": "images/0805.4509_1.jpg", "options": ["A) The ordering temperatures for all materials are above the normalized temperature \\( T/T_c \\) of 1.2.", "B) The magnetic ordering temperatures decrease for Dy, Tb, and Ho as the normalized temperature \\( T/T_c \\) approaches 1.", "C) The magnetic ordering temperatures for all materials are the same across the normalized temperature \\( T/T_c \\).", "D) The magnetic ordering temperature is highest for Yttrium (Y) and decreases for Dy, Tb, and Ho."], "question": "What can be inferred about the magnetic ordering temperatures of the materials tested as shown in the graph?", "label": "B", "rationale": "The graph shows a sharp decline in frequency as the normalized temperature \\( T/T_c \\) approaches 1 for Dy, Tb, and Ho, indicating that their magnetic ordering temperatures decrease. No such data is shown for Yttrium (Y), thus we can't infer it has the highest magnetic ordering temperature." } ``` - Download the `arxivqa.json` and `images.tgz` to your machine. - Decompress images: `tar -xzvf images.tgz`. - Loading the dataset and process the sample according to your need. ```python3 import json with open("arxivqa.jsonl", 'r') as fr: arxiv_qa = [ json.loads(line.strip()) for line in fr] sample = arxiv_qa[0] print(sample["image"]) # image file ``` ## Dataset details Dataset type: ArxivQA is a set of GPT4V-generated VQA samples based on figures from Arxiv Papers. Papers or resources for more information: https://mm-arxiv.github.io/ License: CC-BY-SA-4.0; and it should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use Intended use: Primary intended uses: The primary use of ArxivQA is research on large multimodal models. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo	--- pretty_name: Evaluation run of macadeliccc/laser-dolphin-mixtral-2x7b-dpo dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [macadeliccc/laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)\ \ 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_macadeliccc__laser-dolphin-mixtral-2x7b-dpo\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-14T01:13:57.359475](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo/blob/main/results_2024-01-14T01-13-57.359475.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\": 0.6323249282667325,\n\ \ \"acc_stderr\": 0.03235123186693868,\n \"acc_norm\": 0.63602882598941,\n\ \ \"acc_norm_stderr\": 0.03299471578731984,\n \"mc1\": 0.4418604651162791,\n\ \ \"mc1_stderr\": 0.01738476747898622,\n \"mc2\": 0.6075861082832835,\n\ \ \"mc2_stderr\": 0.015099206529299735\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6245733788395904,\n \"acc_stderr\": 0.014150631435111728,\n\ \ \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892978\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6661023700458076,\n\ \ \"acc_stderr\": 0.004706398252382464,\n \"acc_norm\": 0.8579964150567616,\n\ \ \"acc_norm_stderr\": 0.0034834044902359936\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\ \ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7361111111111112,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6127167630057804,\n\ \ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n\ \ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.502127659574468,\n \"acc_stderr\": 0.03268572658667492,\n\ \ \"acc_norm\": 0.502127659574468,\n \"acc_norm_stderr\": 0.03268572658667492\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\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440679,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440679\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7645161290322581,\n\ \ \"acc_stderr\": 0.02413763242933771,\n \"acc_norm\": 0.7645161290322581,\n\ \ \"acc_norm_stderr\": 0.02413763242933771\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026704,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026704\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121437,\n\ \ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121437\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6307692307692307,\n \"acc_stderr\": 0.024468615241478923,\n\ \ \"acc_norm\": 0.6307692307692307,\n \"acc_norm_stderr\": 0.024468615241478923\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \ \ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8275229357798165,\n \"acc_stderr\": 0.016197807956848033,\n \"\ acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.016197807956848033\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.803921568627451,\n\ \ \"acc_stderr\": 0.027865942286639325,\n \"acc_norm\": 0.803921568627451,\n\ \ \"acc_norm_stderr\": 0.027865942286639325\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n\ \ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\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\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\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\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8122605363984674,\n\ \ \"acc_stderr\": 0.013964393769899129,\n \"acc_norm\": 0.8122605363984674,\n\ \ \"acc_norm_stderr\": 0.013964393769899129\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.024027745155265012,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.024027745155265012\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3664804469273743,\n\ \ \"acc_stderr\": 0.016115235504865467,\n \"acc_norm\": 0.3664804469273743,\n\ \ \"acc_norm_stderr\": 0.016115235504865467\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\ \ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6913183279742765,\n\ \ \"acc_stderr\": 0.026236965881153266,\n \"acc_norm\": 0.6913183279742765,\n\ \ \"acc_norm_stderr\": 0.026236965881153266\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.44654498044328556,\n\ \ \"acc_stderr\": 0.012697046024399682,\n \"acc_norm\": 0.44654498044328556,\n\ \ \"acc_norm_stderr\": 0.012697046024399682\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6507352941176471,\n \"acc_stderr\": 0.028959755196824876,\n\ \ \"acc_norm\": 0.6507352941176471,\n \"acc_norm_stderr\": 0.028959755196824876\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6568627450980392,\n \"acc_stderr\": 0.019206606848825362,\n \ \ \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.019206606848825362\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\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\": 0.8011695906432749,\n \"acc_stderr\": 0.03061111655743253,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.03061111655743253\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4418604651162791,\n\ \ \"mc1_stderr\": 0.01738476747898622,\n \"mc2\": 0.6075861082832835,\n\ \ \"mc2_stderr\": 0.015099206529299735\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7900552486187845,\n \"acc_stderr\": 0.01144628062926263\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.4829416224412434,\n \ \ \"acc_stderr\": 0.013764467123761318\n }\n}\n```" repo_url: https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo 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_14T01_13_57.359475 path: - '/details_harness\|arc:challenge\|25_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-14T01-13-57.359475.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|gsm8k\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hellaswag\|10_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-14T01-13-57.359475.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-14T01-13-57.359475.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '/details_harness\|winogrande\|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-14T01-13-57.359475.parquet' - config_name: results data_files: - split: 2024_01_14T01_13_57.359475 path: - results_2024-01-14T01-13-57.359475.parquet - split: latest path: - results_2024-01-14T01-13-57.359475.parquet --- # Dataset Card for Evaluation run of macadeliccc/laser-dolphin-mixtral-2x7b-dpo <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [macadeliccc/laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo) 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_macadeliccc__laser-dolphin-mixtral-2x7b-dpo", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-14T01:13:57.359475](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo/blob/main/results_2024-01-14T01-13-57.359475.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": 0.6323249282667325, "acc_stderr": 0.03235123186693868, "acc_norm": 0.63602882598941, "acc_norm_stderr": 0.03299471578731984, "mc1": 0.4418604651162791, "mc1_stderr": 0.01738476747898622, "mc2": 0.6075861082832835, "mc2_stderr": 0.015099206529299735 }, "harness\|arc:challenge\|25": { "acc": 0.6245733788395904, "acc_stderr": 0.014150631435111728, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.013847460518892978 }, "harness\|hellaswag\|10": { "acc": 0.6661023700458076, "acc_stderr": 0.004706398252382464, "acc_norm": 0.8579964150567616, "acc_norm_stderr": 0.0034834044902359936 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595852, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595852 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.502127659574468, "acc_stderr": 0.03268572658667492, "acc_norm": 0.502127659574468, "acc_norm_stderr": 0.03268572658667492 }, "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": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440679, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440679 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7645161290322581, "acc_stderr": 0.02413763242933771, "acc_norm": 0.7645161290322581, "acc_norm_stderr": 0.02413763242933771 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7929292929292929, "acc_stderr": 0.02886977846026704, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026704 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8808290155440415, "acc_stderr": 0.023381935348121437, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.023381935348121437 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6307692307692307, "acc_stderr": 0.024468615241478923, "acc_norm": 0.6307692307692307, "acc_norm_stderr": 0.024468615241478923 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.28888888888888886, "acc_stderr": 0.027634907264178544, "acc_norm": 0.28888888888888886, "acc_norm_stderr": 0.027634907264178544 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.03038835355188679, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.03038835355188679 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8275229357798165, "acc_stderr": 0.016197807956848033, "acc_norm": 0.8275229357798165, "acc_norm_stderr": 0.016197807956848033 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639325, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639325 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "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": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "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": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "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": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8846153846153846, "acc_stderr": 0.020930193185179333, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.020930193185179333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8122605363984674, "acc_stderr": 0.013964393769899129, "acc_norm": 0.8122605363984674, "acc_norm_stderr": 0.013964393769899129 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7254335260115607, "acc_stderr": 0.024027745155265012, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.024027745155265012 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3664804469273743, "acc_stderr": 0.016115235504865467, "acc_norm": 0.3664804469273743, "acc_norm_stderr": 0.016115235504865467 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818733, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818733 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6913183279742765, "acc_stderr": 0.026236965881153266, "acc_norm": 0.6913183279742765, "acc_norm_stderr": 0.026236965881153266 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.450354609929078, "acc_stderr": 0.029680105565029036, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.029680105565029036 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.44654498044328556, "acc_stderr": 0.012697046024399682, "acc_norm": 0.44654498044328556, "acc_norm_stderr": 0.012697046024399682 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6507352941176471, "acc_stderr": 0.028959755196824876, "acc_norm": 0.6507352941176471, "acc_norm_stderr": 0.028959755196824876 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6568627450980392, "acc_stderr": 0.019206606848825362, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.019206606848825362 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.746938775510204, "acc_stderr": 0.027833023871399677, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399677 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "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": 0.8011695906432749, "acc_stderr": 0.03061111655743253, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.03061111655743253 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4418604651162791, "mc1_stderr": 0.01738476747898622, "mc2": 0.6075861082832835, "mc2_stderr": 0.015099206529299735 }, "harness\|winogrande\|5": { "acc": 0.7900552486187845, "acc_stderr": 0.01144628062926263 }, "harness\|gsm8k\|5": { "acc": 0.4829416224412434, "acc_stderr": 0.013764467123761318 } } ``` ## 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]
Yamei/TVCG_entity_classify	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: id dtype: int64 - name: entity_type dtype: string - name: entity_type_high dtype: string - name: label dtype: class_label: names: '0': method '1': evaluation '2': data '3': background '4': Author '5': DBPedia '6': Affiliation '7': Paper '8': Journal splits: - name: train num_bytes: 3565888.348566884 num_examples: 47281 - name: test num_bytes: 891528.6514331156 num_examples: 11821 download_size: 1857856 dataset_size: 4457417.0 --- # Dataset Card for "TVCG_entity_classify" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tellarin-ai/ntx_llm_inst_hindi	--- license: cc-by-sa-4.0 language: - hi task_categories: - token-classification --- # Dataset Card for NTX v1 in the Aya format - Hindi subset This dataset is a format conversion for the Hindi data from the original NTX into the Aya instruction format and it's released here under the CC-BY-SA 4.0 license. ## Dataset Details For the original NTX dataset, the conversion to the Aya instructions format, or more details, please refer to the full dataset in instruction form (https://huggingface.co/datasets/tellarin-ai/ntx_llm_instructions) or to the paper below. NOTE: Unfortunately, due to a conversion issue with numerical expressions, this version here only includes the temporal expressions part of NTX. ## Citation If you utilize this dataset version, feel free to cite/footnote the complete version at https://huggingface.co/datasets/tellarin-ai/ntx_llm_instructions, but please also cite the original dataset publication. BibTeX: ``` @preprint{chen2023dataset, title={Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions}, author={Sanxing Chen and Yongqiang Chen and Börje F. Karlsson}, year={2023}, eprint={2303.18103}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
MBZUAI/LaMini-Hallucination	--- dataset_info: features: - name: question dtype: string - name: category dtype: string splits: - name: test num_bytes: 2785 num_examples: 40 download_size: 3220 dataset_size: 2785 --- # Dataset Card for "LaMini-Hallucination" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) # Citation ``` @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```
yzhuang/metatree_vehicle_sensIT	--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float32 - name: y dtype: int64 splits: - name: train num_bytes: 28973280 num_examples: 68984 - name: validation num_bytes: 12408480 num_examples: 29544 download_size: 60104700 dataset_size: 41381760 --- # Dataset Card for "metatree_vehicle_sensIT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
richardr1126/spider-context-validation	--- language: - en license: - cc-by-4.0 source_datasets: - spider pretty_name: Spider Context Validation tags: - text-to-sql - SQL - spider - validation - eval - spider-eval dataset_info: features: - name: db_id dtype: string - name: question dtype: string - name: db_info dtype: string - name: ground_truth dtype: string --- # Dataset Card for Spider Context Validation ### Dataset Summary Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases. This dataset was created to validate spider-fine-tuned LLMs with database context. ### Yale Lily Spider Leaderboards The leaderboard can be seen at https://yale-lily.github.io/spider ### Languages The text in the dataset is in English. ### Licensing Information The spider dataset is licensed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) ### Citation ``` @article{yu2018spider, title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task}, author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others}, journal={arXiv preprint arXiv:1809.08887}, year={2018} } ```
AUTOMATIC/jaicards	--- license: mit task_categories: - conversational - text-generation size_categories: - 100K<n<1M --- # janitorai-cards This dataset contains 190k cards that I received from janitorai, from a source that wished to remain anonymous. My addition to this data is conversion of cards to [v2 character card](https://github.com/malfoyslastname/character-card-spec-v2/blob/main/README.md) format, and a local webpage that can be used to explore the dataset. ### Webpage ![](screenshot.png) Ther webpage lets you browse cards, search by text, fitler by tags and order by date/name/popularity. To use the webpage, put [index.html](index.html) into a directory, and download and extract archives into same directory: [0123.zip](0123.zip), [4567.zip](4567.zip), [89ab.zip](89ab.zip), [cdef.zip](cdef.zip), and [html.zip](html.zip). After that, just open [index.html](index.html) in the browser. The directory structure should look like this: ``` 📁 ┣━━ 📄 index.html ┣━━ 📁 cards ┃ ┣━━ 📁 0 ┃ ┣━━ 📁 1 ┃ ┃ ... ┃ ┗━━ 📁 f ┗━━ 📁 html ┣━━ 📄 allcards.js ┣━━ 📄 cards.js ┗━━ 📄 cardsmeta.js ``` For performance reasons, the webpage only loads 10000 most popular cards when you open it. To view all, click the "Load all" button in the top row. Caveat: instead of downloading the card, it opens it in a new page—you have to save it yourself. I can't figure out how to get the download to work. ### Files - [0123.zip](0123.zip), [4567.zip](4567.zip), [89ab.zip](89ab.zip), [cdef.zip](cdef.zip) - archives with v2 character cards, tested to work with SillyTavern. - [cards-js.7z](cards-js.7z) - all v2 character cards in json format, without images, tested to work with SillyTavern. - [index.html](index.html) - webpage for browsing cards. - [html.zip](html.zip) - files with information about cards - it's needed for the webpage to function. - [orig.7z](orig.7z) - original json files with cards from janitorai - not compatible with any software.
Maciel/e-commerce-sample-images	--- license: apache-2.0 ---
camilaslz/LEOFELIPE	--- license: openrail ---
DataLinguistic/MutiDataset	--- license: apache-2.0 ---
autoevaluate/autoeval-eval-project-jnlpba-c103d433-1295449602	--- type: predictions tags: - autotrain - evaluation datasets: - jnlpba eval_info: task: entity_extraction model: siddharthtumre/biobert-ner metrics: [] dataset_name: jnlpba dataset_config: jnlpba dataset_split: validation col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: siddharthtumre/biobert-ner * Dataset: jnlpba * Config: jnlpba * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@siddharthtumre](https://huggingface.co/siddharthtumre) for evaluating this model.
heliosprime/twitter_dataset_1713189721	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 15533 num_examples: 42 download_size: 16373 dataset_size: 15533 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713189721" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mnli_existential_got	--- 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: 183289 num_examples: 843 - name: dev_mismatched num_bytes: 163475 num_examples: 689 - name: test_matched num_bytes: 182488 num_examples: 842 - name: test_mismatched num_bytes: 155896 num_examples: 712 - name: train num_bytes: 7382865 num_examples: 33251 download_size: 5028376 dataset_size: 8068013 --- # Dataset Card for "MULTI_VALUE_mnli_existential_got" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_chlee10__T3Q-Platypus-SOLAR	--- pretty_name: Evaluation run of chlee10/T3Q-Platypus-SOLAR dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chlee10/T3Q-Platypus-SOLAR](https://huggingface.co/chlee10/T3Q-Platypus-SOLAR)\ \ 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_chlee10__T3Q-Platypus-SOLAR\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-12T05:48:31.143734](https://huggingface.co/datasets/open-llm-leaderboard/details_chlee10__T3Q-Platypus-SOLAR/blob/main/results_2024-03-12T05-48-31.143734.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\": 0.5426000137755855,\n\ \ \"acc_stderr\": 0.03377258305011606,\n \"acc_norm\": 0.543700672611483,\n\ \ \"acc_norm_stderr\": 0.034480897315487986,\n \"mc1\": 0.35495716034271724,\n\ \ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5066765475632212,\n\ \ \"mc2_stderr\": 0.01508715500679457\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520769,\n\ \ \"acc_norm\": 0.6186006825938567,\n \"acc_norm_stderr\": 0.014194389086685247\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6440948018323043,\n\ \ \"acc_stderr\": 0.004778081784542405,\n \"acc_norm\": 0.8417645887273452,\n\ \ \"acc_norm_stderr\": 0.0036421571661623495\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n\ \ \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5622641509433962,\n \"acc_stderr\": 0.030533338430467523,\n\ \ \"acc_norm\": 0.5622641509433962,\n \"acc_norm_stderr\": 0.030533338430467523\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6111111111111112,\n\ \ \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n\ \ \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5375722543352601,\n\ \ \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.5375722543352601,\n\ \ \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808779,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808779\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4851063829787234,\n \"acc_stderr\": 0.032671518489247764,\n\ \ \"acc_norm\": 0.4851063829787234,\n \"acc_norm_stderr\": 0.032671518489247764\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.30701754385964913,\n\ \ \"acc_stderr\": 0.0433913832257986,\n \"acc_norm\": 0.30701754385964913,\n\ \ \"acc_norm_stderr\": 0.0433913832257986\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3915343915343915,\n \"acc_stderr\": 0.025138091388851105,\n \"\ acc_norm\": 0.3915343915343915,\n \"acc_norm_stderr\": 0.025138091388851105\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2619047619047619,\n\ \ \"acc_stderr\": 0.03932537680392869,\n \"acc_norm\": 0.2619047619047619,\n\ \ \"acc_norm_stderr\": 0.03932537680392869\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.6290322580645161,\n \"acc_stderr\": 0.02748054188795359,\n \"\ acc_norm\": 0.6290322580645161,\n \"acc_norm_stderr\": 0.02748054188795359\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969566,\n \"\ acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969566\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\ \ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7171717171717171,\n \"acc_stderr\": 0.03208779558786752,\n \"\ acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.03208779558786752\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7616580310880829,\n \"acc_stderr\": 0.030748905363909895,\n\ \ \"acc_norm\": 0.7616580310880829,\n \"acc_norm_stderr\": 0.030748905363909895\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.47692307692307695,\n \"acc_stderr\": 0.025323990861736118,\n\ \ \"acc_norm\": 0.47692307692307695,\n \"acc_norm_stderr\": 0.025323990861736118\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340492,\n \ \ \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340492\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.49159663865546216,\n \"acc_stderr\": 0.03247390276569669,\n\ \ \"acc_norm\": 0.49159663865546216,\n \"acc_norm_stderr\": 0.03247390276569669\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.6972477064220184,\n \"acc_stderr\": 0.019698711434756343,\n \"\ acc_norm\": 0.6972477064220184,\n \"acc_norm_stderr\": 0.019698711434756343\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.3101851851851852,\n \"acc_stderr\": 0.03154696285656629,\n \"\ acc_norm\": 0.3101851851851852,\n \"acc_norm_stderr\": 0.03154696285656629\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7254901960784313,\n \"acc_stderr\": 0.03132179803083291,\n \"\ acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.03132179803083291\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159287,\n \ \ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159287\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6457399103139013,\n\ \ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n\ \ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.043285772152629715,\n\ \ \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.043285772152629715\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7272727272727273,\n \"acc_stderr\": 0.04065578140908705,\n \"\ acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04065578140908705\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.5644171779141104,\n \"acc_stderr\": 0.038956324641389366,\n\ \ \"acc_norm\": 0.5644171779141104,\n \"acc_norm_stderr\": 0.038956324641389366\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.35714285714285715,\n\ \ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\ \ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7991452991452992,\n\ \ \"acc_stderr\": 0.026246772946890488,\n \"acc_norm\": 0.7991452991452992,\n\ \ \"acc_norm_stderr\": 0.026246772946890488\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.7662835249042146,\n\ \ \"acc_stderr\": 0.015133383278988836,\n \"acc_norm\": 0.7662835249042146,\n\ \ \"acc_norm_stderr\": 0.015133383278988836\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6271676300578035,\n \"acc_stderr\": 0.02603389061357629,\n\ \ \"acc_norm\": 0.6271676300578035,\n \"acc_norm_stderr\": 0.02603389061357629\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2547486033519553,\n\ \ \"acc_stderr\": 0.014572650383409153,\n \"acc_norm\": 0.2547486033519553,\n\ \ \"acc_norm_stderr\": 0.014572650383409153\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.02849199358617156,\n\ \ \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.02849199358617156\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6109324758842444,\n\ \ \"acc_stderr\": 0.02769033753648537,\n \"acc_norm\": 0.6109324758842444,\n\ \ \"acc_norm_stderr\": 0.02769033753648537\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6820987654320988,\n \"acc_stderr\": 0.02591006352824088,\n\ \ \"acc_norm\": 0.6820987654320988,\n \"acc_norm_stderr\": 0.02591006352824088\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4148936170212766,\n \"acc_stderr\": 0.029392236584612503,\n \ \ \"acc_norm\": 0.4148936170212766,\n \"acc_norm_stderr\": 0.029392236584612503\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3956975228161669,\n\ \ \"acc_stderr\": 0.012489290735449018,\n \"acc_norm\": 0.3956975228161669,\n\ \ \"acc_norm_stderr\": 0.012489290735449018\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.43014705882352944,\n \"acc_stderr\": 0.030074971917302875,\n\ \ \"acc_norm\": 0.43014705882352944,\n \"acc_norm_stderr\": 0.030074971917302875\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5588235294117647,\n \"acc_stderr\": 0.020087362076702857,\n \ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.020087362076702857\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.04494290866252089,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.04494290866252089\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.5102040816326531,\n \"acc_stderr\": 0.03200255347893783,\n\ \ \"acc_norm\": 0.5102040816326531,\n \"acc_norm_stderr\": 0.03200255347893783\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6616915422885572,\n\ \ \"acc_stderr\": 0.03345563070339193,\n \"acc_norm\": 0.6616915422885572,\n\ \ \"acc_norm_stderr\": 0.03345563070339193\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.04605661864718381,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4397590361445783,\n\ \ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\ \ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.032467217651178264,\n\ \ \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.032467217651178264\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.35495716034271724,\n\ \ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5066765475632212,\n\ \ \"mc2_stderr\": 0.01508715500679457\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.823993685872139,\n \"acc_stderr\": 0.010703090882320705\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.43745261561789234,\n \ \ \"acc_stderr\": 0.013664299060751915\n }\n}\n```" repo_url: https://huggingface.co/chlee10/T3Q-Platypus-SOLAR leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|arc:challenge\|25_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-12T05-48-31.143734.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|gsm8k\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hellaswag\|10_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-12T05-48-31.143734.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-12T05-48-31.143734.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '/details_harness\|winogrande\|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-12T05-48-31.143734.parquet' - config_name: results data_files: - split: 2024_03_12T05_48_31.143734 path: - results_2024-03-12T05-48-31.143734.parquet - split: latest path: - results_2024-03-12T05-48-31.143734.parquet --- # Dataset Card for Evaluation run of chlee10/T3Q-Platypus-SOLAR <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [chlee10/T3Q-Platypus-SOLAR](https://huggingface.co/chlee10/T3Q-Platypus-SOLAR) 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_chlee10__T3Q-Platypus-SOLAR", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-12T05:48:31.143734](https://huggingface.co/datasets/open-llm-leaderboard/details_chlee10__T3Q-Platypus-SOLAR/blob/main/results_2024-03-12T05-48-31.143734.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": 0.5426000137755855, "acc_stderr": 0.03377258305011606, "acc_norm": 0.543700672611483, "acc_norm_stderr": 0.034480897315487986, "mc1": 0.35495716034271724, "mc1_stderr": 0.0167508623813759, "mc2": 0.5066765475632212, "mc2_stderr": 0.01508715500679457 }, "harness\|arc:challenge\|25": { "acc": 0.5750853242320819, "acc_stderr": 0.014445698968520769, "acc_norm": 0.6186006825938567, "acc_norm_stderr": 0.014194389086685247 }, "harness\|hellaswag\|10": { "acc": 0.6440948018323043, "acc_stderr": 0.004778081784542405, "acc_norm": 0.8417645887273452, "acc_norm_stderr": 0.0036421571661623495 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5622641509433962, "acc_stderr": 0.030533338430467523, "acc_norm": 0.5622641509433962, "acc_norm_stderr": 0.030533338430467523 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04076663253918567, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04076663253918567 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5375722543352601, "acc_stderr": 0.0380168510452446, "acc_norm": 0.5375722543352601, "acc_norm_stderr": 0.0380168510452446 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808779, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808779 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4851063829787234, "acc_stderr": 0.032671518489247764, "acc_norm": 0.4851063829787234, "acc_norm_stderr": 0.032671518489247764 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.30701754385964913, "acc_stderr": 0.0433913832257986, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.0433913832257986 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3915343915343915, "acc_stderr": 0.025138091388851105, "acc_norm": 0.3915343915343915, "acc_norm_stderr": 0.025138091388851105 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2619047619047619, "acc_stderr": 0.03932537680392869, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.03932537680392869 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6290322580645161, "acc_stderr": 0.02748054188795359, "acc_norm": 0.6290322580645161, "acc_norm_stderr": 0.02748054188795359 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3497536945812808, "acc_stderr": 0.03355400904969566, "acc_norm": 0.3497536945812808, "acc_norm_stderr": 0.03355400904969566 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7212121212121212, "acc_stderr": 0.03501438706296781, "acc_norm": 0.7212121212121212, "acc_norm_stderr": 0.03501438706296781 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7171717171717171, "acc_stderr": 0.03208779558786752, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.03208779558786752 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7616580310880829, "acc_stderr": 0.030748905363909895, "acc_norm": 0.7616580310880829, "acc_norm_stderr": 0.030748905363909895 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.47692307692307695, "acc_stderr": 0.025323990861736118, "acc_norm": 0.47692307692307695, "acc_norm_stderr": 0.025323990861736118 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2851851851851852, "acc_stderr": 0.027528599210340492, "acc_norm": 0.2851851851851852, "acc_norm_stderr": 0.027528599210340492 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.49159663865546216, "acc_stderr": 0.03247390276569669, "acc_norm": 0.49159663865546216, "acc_norm_stderr": 0.03247390276569669 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6972477064220184, "acc_stderr": 0.019698711434756343, "acc_norm": 0.6972477064220184, "acc_norm_stderr": 0.019698711434756343 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.3101851851851852, "acc_stderr": 0.03154696285656629, "acc_norm": 0.3101851851851852, "acc_norm_stderr": 0.03154696285656629 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7254901960784313, "acc_stderr": 0.03132179803083291, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.03132179803083291 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7637130801687764, "acc_stderr": 0.027652153144159287, "acc_norm": 0.7637130801687764, "acc_norm_stderr": 0.027652153144159287 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6457399103139013, "acc_stderr": 0.032100621541349864, "acc_norm": 0.6457399103139013, "acc_norm_stderr": 0.032100621541349864 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5801526717557252, "acc_stderr": 0.043285772152629715, "acc_norm": 0.5801526717557252, "acc_norm_stderr": 0.043285772152629715 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04065578140908705, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04065578140908705 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497751, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497751 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5644171779141104, "acc_stderr": 0.038956324641389366, "acc_norm": 0.5644171779141104, "acc_norm_stderr": 0.038956324641389366 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04547960999764376, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04547960999764376 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6699029126213593, "acc_stderr": 0.0465614711001235, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.0465614711001235 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7991452991452992, "acc_stderr": 0.026246772946890488, "acc_norm": 0.7991452991452992, "acc_norm_stderr": 0.026246772946890488 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7662835249042146, "acc_stderr": 0.015133383278988836, "acc_norm": 0.7662835249042146, "acc_norm_stderr": 0.015133383278988836 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6271676300578035, "acc_stderr": 0.02603389061357629, "acc_norm": 0.6271676300578035, "acc_norm_stderr": 0.02603389061357629 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2547486033519553, "acc_stderr": 0.014572650383409153, "acc_norm": 0.2547486033519553, "acc_norm_stderr": 0.014572650383409153 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5490196078431373, "acc_stderr": 0.02849199358617156, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.02849199358617156 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6109324758842444, "acc_stderr": 0.02769033753648537, "acc_norm": 0.6109324758842444, "acc_norm_stderr": 0.02769033753648537 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6820987654320988, "acc_stderr": 0.02591006352824088, "acc_norm": 0.6820987654320988, "acc_norm_stderr": 0.02591006352824088 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4148936170212766, "acc_stderr": 0.029392236584612503, "acc_norm": 0.4148936170212766, "acc_norm_stderr": 0.029392236584612503 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3956975228161669, "acc_stderr": 0.012489290735449018, "acc_norm": 0.3956975228161669, "acc_norm_stderr": 0.012489290735449018 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.43014705882352944, "acc_stderr": 0.030074971917302875, "acc_norm": 0.43014705882352944, "acc_norm_stderr": 0.030074971917302875 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5588235294117647, "acc_stderr": 0.020087362076702857, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.020087362076702857 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.04494290866252089, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.04494290866252089 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5102040816326531, "acc_stderr": 0.03200255347893783, "acc_norm": 0.5102040816326531, "acc_norm_stderr": 0.03200255347893783 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6616915422885572, "acc_stderr": 0.03345563070339193, "acc_norm": 0.6616915422885572, "acc_norm_stderr": 0.03345563070339193 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.7, "acc_stderr": 0.04605661864718381, "acc_norm": 0.7, "acc_norm_stderr": 0.04605661864718381 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7660818713450293, "acc_stderr": 0.032467217651178264, "acc_norm": 0.7660818713450293, "acc_norm_stderr": 0.032467217651178264 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.35495716034271724, "mc1_stderr": 0.0167508623813759, "mc2": 0.5066765475632212, "mc2_stderr": 0.01508715500679457 }, "harness\|winogrande\|5": { "acc": 0.823993685872139, "acc_stderr": 0.010703090882320705 }, "harness\|gsm8k\|5": { "acc": 0.43745261561789234, "acc_stderr": 0.013664299060751915 } } ``` ## 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]
mattymchen/refinedweb-3m	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7834920949 num_examples: 3000000 download_size: 4904877808 dataset_size: 7834920949 --- # Dataset Card for "refinedweb-3m" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
QizhiPei/BioT5_finetune_dataset	--- license: mit language: - en --- ## References For more information, please refer to our paper and GitHub repository. Paper: [BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations](https://arxiv.org/abs/2310.07276) GitHub: [BioT5](https://github.com/QizhiPei/BioT5) Authors: Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan
davanstrien/testpapercomments-ds	--- dataset_info: features: - name: paper_url dtype: string - name: comment dtype: string splits: - name: train num_bytes: 502672 num_examples: 456 download_size: 0 dataset_size: 502672 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "testpapercomments-ds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
orgcatorg/stripes	--- configs: - config_name: Africa data_files: - split: train path: Africa/train-* - config_name: Asia-Pacific data_files: - split: train path: Asia-Pacific/train-* - config_name: Europe data_files: - split: train path: Europe/train-* - config_name: Middle East data_files: - split: train path: Middle East/train-* - config_name: US data_files: - split: train path: US/train-* dataset_info: - config_name: Africa features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 873549 num_examples: 175 download_size: 530795 dataset_size: 873549 - config_name: Asia-Pacific features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 2597100 num_examples: 596 download_size: 1526683 dataset_size: 2597100 - config_name: Europe features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 6333893 num_examples: 1241 download_size: 3748163 dataset_size: 6333893 - config_name: Middle East features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 6203258 num_examples: 958 download_size: 3539626 dataset_size: 6203258 - config_name: US features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 6964806 num_examples: 1220 download_size: 4135894 dataset_size: 6964806 --- # Dataset Card for "stripes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yzhuang/autotree_automl_10000_eye_movements_sgosdt_l256_dim10_d3_sd0	--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: input_y_clean sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 236440000 num_examples: 10000 - name: validation num_bytes: 236440000 num_examples: 10000 download_size: 155715478 dataset_size: 472880000 --- # Dataset Card for "autotree_automl_10000_eye_movements_sgosdt_l256_dim10_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
heliosprime/twitter_dataset_1713038771	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 11220 num_examples: 25 download_size: 8392 dataset_size: 11220 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713038771" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
communityai/Open-Orca___1million-gpt-4-200k	--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 371133122.45702064 num_examples: 200000 download_size: 196792432 dataset_size: 371133122.45702064 configs: - config_name: default data_files: - split: train path: data/train-* ---
tacoz/audCatImages	--- license: openrail ---
vekkt/french_CEFR	--- license: mit ---
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_198	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1071458640.0 num_examples: 210420 download_size: 1093799592 dataset_size: 1071458640.0 --- # Dataset Card for "chunk_198" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
5mN/Sum-assistant-v1	--- task_categories: - question-answering language: - en - hr pretty_name: Univeristy of Mostar Assistant ---
one-sec-cv12/chunk_160	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 22251248064.5 num_examples: 231668 download_size: 20138689166 dataset_size: 22251248064.5 --- # Dataset Card for "chunk_160" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Luizagrod23/RyotaSakuraba	--- license: openrail ---
ddonuts/recurrent-events	--- license: other ---
SauravMaheshkar/NDC-substances-25	--- license: unknown task_categories: - graph-ml tags: - chemistry configs: - config_name: transductive data_files: - split: train path: "processed/transductive/train_df.csv" - split: valid path: "processed/transductive/val_df.csv" - split: test path: "processed/transductive/test_df.csv" - config_name: inductive data_files: - split: train path: "processed/inductive/train_df.csv" - split: valid path: "processed/inductive/val_df.csv" - split: test path: "processed/inductive/test_df.csv" - config_name: raw data_files: "raw/*.txt" --- Source Paper: https://arxiv.org/abs/1802.06916 ### Usage ``` from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset dataset = CornellTemporalHyperGraphDataset(root = "./", name="NDC-substances-25", split="train") ``` ### Citation ```misc @article{Benson-2018-simplicial, author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon}, title = {Simplicial closure and higher-order link prediction}, year = {2018}, doi = {10.1073/pnas.1800683115}, publisher = {National Academy of Sciences}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences} } ```
Ve11ichor/Song_SA_np_input	--- license: apache-2.0 task_categories: - text-classification language: - zh size_categories: - 1K<n<10K ---
aisc-team-d1/PMC_Data	--- configs: - config_name: default data_files: - split: train path: train-* - split: test path: test-* dataset_info: features: - name: PMC_id dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: inline dtype: string - name: img_ref dtype: string splits: - name: train num_bytes: 634222272 num_examples: 316838 - name: test num_bytes: 253538916 num_examples: 120836 download_size: 139781550 dataset_size: 887761188 --- # PMC-CaseReport Dataset - [PMC-CaseReport_original Dataset](#pmc-casereport-dataset) - [Daraset Structure](#dataset-structure) - [Sample](#sample) This is the text parts and the figure parts can be dowloaded from https://pan.baidu.com/s/1Src_rhXsaOFp8zJ_3zMFsQ?pwd=p3ne. ## Dataset Structure PMC-CaseReport (Filtered version: 317K VQA pairs for taining and of 121K for testing images). The dataset can be loading following huggingface datasets rule: ``` from datasets import load_dataset dataset = load_dataset("chaoyi-wu/PMC-CaseReport_original") ``` - ## Sample A case in dataset is shown bellow, \| PMC_id \| PMC9052276 \| \| -------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ \| \| context \| We report the case of a 73-year-old female who presented to the ER with left-sided body weakness of unclear duration.She had an ischemic stroke four years prior with no residual neurologic deficits, a myocardial infarction requiring coronary artery bypass grafting (CABG) two years prior, hypertension, and dementia. Her vital signs were blood pressure (BP) 117/78 mmHg, pulse 121 beats per minute, temperature 98.9 F, respiratory rate (RR) 18 cycles/minute, and oxygen saturation (SpO2) of 97% on ambient air.She was disoriented to place and time with a Glasgow Coma Score (GCS) of 14 (E4V4M6).Her speech was slurred, cranial nerves (CN) 2-12 were grossly intact, motor strength on the left upper and lower extremities was 0/5 and on the right upper and lower extremities was 4/5, and the sensation was preserved in all extremities.The patient had a National Institutes of Health Stroke Scale (NIHSS) score of 16 and a Modified Rankin Score (mRS) of 5 points.A non-contrast head CT scan revealed evidence of old lacuna infarcts in the basal ganglia and thalamus.No intracranial hemorrhage or acute infarct was found.CT perfusion was not done as our center lacks the resources needed to perform that. \| \| inline \| A brain MRI scan showed an acute pontine stroke (Figures and old infarcts \| \| question \| What did the brain MRI scan reveal? \| \| answer \| The brain MRI scan showed an acute pontine stroke and old infarcts. \| \| img_ref \| "['FIG1', 'FIG3', 'FIG4']" \| \| \| Explanation to each key - PMC_id: corresponding PMC paper id. - context: the context in case report before discussing about the image. - inline: the inline sentence in original paper for referring and should not be input into network - question: the genrated question. - answer: the correct answer. - img_ref: the list for related img id. You can get the image form our PMC figure parts, and fig is named unified as ```PMCxxxxxxx_figid.jpg``` like ```PMC9052276_FIG1.jpg``` Note that, we have not filter the context strictly. Thus, in few cases the answer may be leaked in context. Besides, our PMC figures are collected before this datasets, and during the time window, some papers have been updated. Thus some figures may be missed in our figure base.
AayushShah/SQL_Merged_IDs_and_Text	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: NATURAL_LANG dtype: string - name: SCHEMA dtype: string - name: SQL dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 1089459820.9581463 num_examples: 270986 - name: test num_bytes: 121052878.04185376 num_examples: 30110 download_size: 101851785 dataset_size: 1210512699.0 --- # Dataset Card for "SQL_Merged_IDs_and_Text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
voidful/wikihow_chat	--- dataset_info: features: - name: article_id dtype: int64 - name: question dtype: string - name: answer dtype: string - name: related_document_urls_wayback_snapshots sequence: string - name: split dtype: int64 - name: cluster dtype: int64 - name: dialog dtype: string splits: - name: train num_bytes: 19620137 num_examples: 8235 - name: test num_bytes: 5507274 num_examples: 2333 - name: validation num_bytes: 2810866 num_examples: 1178 download_size: 14161836 dataset_size: 27938277 --- # Dataset Card for "wikihow_chat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Azie88/COVID_Vaccine_Tweet_sentiment_analysis_roberta	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels dtype: int64 splits: - name: train num_bytes: 1827789 num_examples: 7999 - name: eval num_bytes: 527000 num_examples: 2000 download_size: 569069 dataset_size: 2354789 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---
nanyy1025/pubmed_rct_20k	--- license: openrail ---
multimodalart/latent-majesty-diffusion-settings	--- license: mit --- A collection of default settings for the text-to-image model [Latent Majesty Diffusion](https://colab.research.google.com/github/multimodalart/majesty-diffusion/blob/main/latent.ipynb). If you love your settings, please add yours by going to the `Files and versions` tab and hitting upload. ![How to upload](https://i.imgur.com/5Exa76X.png) Also please add a description on what your settings excel (it's okay if they are general purpose too) ![How to describe](https://i.imgur.com/zPY2xfm.png)
GBaker/MedQA-USMLE-4-options-hf-cosine-similarity	--- license: cc-by-sa-4.0 --- Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large) This version is augmented with context retrieved from the textbooks provided with the original dataset using cosine similarity. <h4>Citation information:</h4> @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} }
Prajapat/banking_conversation_falcon	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 221593 num_examples: 800 download_size: 106357 dataset_size: 221593 configs: - config_name: default data_files: - split: train path: data/train-* ---
jlmarrugom/gin-img-datasets	--- license: apache-2.0 ---
RyokoAI/Sensei	--- license: cc0-1.0 ---
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_4_500	--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 1792 num_examples: 63 download_size: 0 dataset_size: 1792 --- # Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_4_500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Svenni551/toxic-full-uncensored-v3.1	--- dataset_info: features: - name: prompt dtype: string - name: output dtype: string splits: - name: train num_bytes: 42273 num_examples: 43 download_size: 28694 dataset_size: 42273 configs: - config_name: default data_files: - split: train path: data/train-* ---
mHossain/final_train_v4_test_1040000	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: input_text dtype: string - name: target_text dtype: string - name: prefix dtype: string splits: - name: train num_bytes: 7345201.5 num_examples: 18000 - name: test num_bytes: 816133.5 num_examples: 2000 download_size: 3516028 dataset_size: 8161335.0 --- # Dataset Card for "final_train_v4_test_1040000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wardenga/lsoie	--- annotations_creators: - machine-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - unknown source_datasets: - extended\|qa_srl task_categories: - text-retrieval task_ids: [] pretty_name: LSOIE tags: - Open Information Extraction --- # Dataset Card for LSOIE ## 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://github.com/Jacobsolawetz/large-scale-oie - Repository: https://github.com/Jacobsolawetz/large-scale-oie - Paper: https://arxiv.org/abs/2101.11177 - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary The Large Scale Open Information Extraction Dataset (LSOIE), is a dataset 20 times larger than the next largest human-annotated Open Information Extraction (OIE) dataset. LSOIE is a built upon the QA-SRL 2.0 dataset by transforming the list of Questions and answers for each predicate to a tuple representing a fact. ### Supported Tasks and Leaderboards Open Information Extraction ### Languages The text in this dataset is english. ## Dataset Structure ### Data Instances A datapoint comprises one fact together with the sentence it was extracted from. There can be multiple facts for each Sentence. Each fact is represented by a tuple $(a_0, p, a_1,\dots a_n)$ where $a_0$ is the head entity $p$ is the predicate and $a_1, \dots,a_n$ represent the tail. ### Data Fields - word_ids : sequence of indices (int) representing tokens in a sentence, - words : a sequence of strings, the tokens in the sentence, - pred : the predicate of the fact, - pred_ids : ids of the tokens in the predicate, - head_pred_id : id of the head token in the predicate, - sent_id : sentence id, - run_id : , - label : Sequence of tags (BIO) representing the fact, e.g. if the fact is given by $(a_0, p, a_1, \dots, a_n) $ ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information [Needs More Information]
hamedhf/nlp_twitter_analysis	--- license: mit task_categories: - text-classification language: - fa - en ---
zyy111/aihuihua	--- license: openrail ---
vishal-burman/moe_misspellings	--- dataset_info: features: - name: correct_word dtype: string - name: incorrect_words sequence: string splits: - name: train num_bytes: 278157870 num_examples: 2720843 download_size: 163693499 dataset_size: 278157870 --- # Dataset Card for "moe_misspellings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
harouzie/vi_question_generation	--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answers dtype: string - name: id dtype: string splits: - name: train num_bytes: 211814961.2307449 num_examples: 174499 - name: test num_bytes: 26477628.80776531 num_examples: 21813 - name: valid num_bytes: 26476414.961489797 num_examples: 21812 download_size: 142790671 dataset_size: 264769005 task_categories: - question-answering - text2text-generation language: - vi pretty_name: Vietnamese Dataset for Extractive Question Answering and Question Generation size_categories: - 100K<n<1M ---
Denviny/LORA	--- license: other ---
recastai/databricks-dolly-15k-chatml	--- language: - en dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 34692013 num_examples: 15011 download_size: 15166632 dataset_size: 34692013 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering - text2text-generation --- # Dataset Card for "databricks-dolly-15k-chatml" ## Dataset Summary This dataset has been created by Re:cast AI to transform the existing dataset [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) into a [chatml](https://huggingface.co/docs/transformers/main/en/chat_templating) friendly format for use in SFT tasks with pretrained models. ## Dataset Structure ```python messages = [ { "content": "You are an expert Q&A system that is trusted around the world. You always... etc.", "role": "system" }, { "content": "(Optional) Context information is below.\n----------------\nVirgin Australia, the... etc.", "role": "user" }, { "content": "Virgin Australia commenced services on 31 August 2000... etc.", "role": "assistant" } ] ] ``` ## Usage ```python from datasets import load_dataset dataset = load_dataset("recastai/databricks-dolly-15k-chatml", split="train") ``` ## Processing applied to original dataset ```python INSTRUCTIONS = """You are an expert Q&A system that is trusted around the world. You always answer the user's query in a helpful and friendly way. Some rules you always follow: 1. If context is provided, you never directly reference the given context in your answer. 2. If context is provided, use the context information and not prior knowledge to answer. 3. Avoid statements like 'Based on the context, ...' or 'The context information ...' or 'The answer to the user's query...' or anything along those lines. 4. If no context is provided use your internal knowledge to answer.""" # databricks-dolly-15k features: # - instruction: The user query/question # - context: (optional) context to use to help the assistant # - response: The assistant's response to the query/question # key_mapping = dict( query = "instruction", context = "context", response = "response" ) def process_chatml_fn(example, validation=False): """ Processing specific to databricks-dolly-15k into a chat format. """ user_content = ( "(Optional) Context information is below.\n" "----------------\n" "{context}\n" "----------------\n" "Answer the following query.\n" "{query}\n" ) assistant_content = "{response}" message = [ {"role": "system", "content": INSTRUCTIONS}, {"role": "user", "content": user_content.format(context=example[key_mapping['context']], query=example[key_mapping['query']])}, {"role": "assistant", "content": assistant_content.format(response=example[key_mapping['response']])} ] return message ```
MLNTeam-Unical/NFT-70M_transactions	--- dataset_info: features: - name: num_sales dtype: int64 - name: fees_seller dtype: float64 - name: fees_opensea dtype: float64 - name: fees_seller_usd dtype: float64 - name: fees_opensea_usd dtype: float64 - name: tx_timestamp dtype: string - name: price dtype: float64 - name: gain dtype: float64 - name: usd_price dtype: float64 - name: usd_gain dtype: float64 - name: token dtype: string - name: to_eth dtype: float64 - name: to_usd dtype: float64 - name: created_date dtype: string - name: chain dtype: string - name: token_type dtype: string - name: asset_contract_type dtype: string - name: asset_type dtype: string - name: payout_collection_address dtype: int64 - name: from_account dtype: int64 - name: to_account dtype: int64 - name: seller_account dtype: int64 - name: winner_account dtype: int64 - name: contract_address dtype: int64 - name: nft_image dtype: int64 - name: collection_image dtype: int64 - name: token_id dtype: int64 - name: nft_name dtype: int64 - name: nft_description dtype: int64 - name: collection_name dtype: int64 - name: collection_description dtype: int64 splits: - name: train num_bytes: 21291348001 num_examples: 70972143 download_size: 6633664673 dataset_size: 21291348001 size_categories: - 10M<n<100M license: cc-by-nc-4.0 task_categories: - time-series-forecasting - text-classification - feature-extraction - text-generation - zero-shot-classification - text2text-generation - sentence-similarity - image-classification - image-to-text - text-to-image - text-retrieval language: - en tags: - Non-fungible Tokens - Crypto - Web3 - Art - Multimodal Learning pretty_name: NFT-70M_transactions --- # Dataset Card for "NFT-70M_transactions" ## Dataset summary The NFT-70M_transactions dataset is the largest and most up-to-date collection of Non-Fungible Tokens (NFT) transactions between 2021 and 2023 sourced from [OpenSea](https://opensea.io), the leading trading platform in the Web3 ecosystem. With more than 70M transactions enriched with metadata, this dataset is conceived to support a wide range of tasks, ranging from sequential and transactional data processing/analysis to graph-based modeling of the complex relationships between traders. Besides, the availability of textual and image contents further amplifies the modeling capabilities and usage opportunities of this dataset, making it a unique and comprehensive multimodal source of information for delving into the NFT landscape. This dataset can serve as a benchmark for various innovative and impactful tasks within the crypto landscape, such as projecting NFT prices or detecting fraudolent and wash trading activities. Furthermore, the multimodal nature of the dataset fosters the development of classification models, as well as textual and visual generative models. ## Data anonymization We point out that the collected NFT transactions and metadata from OpenSea are publicly distributed on blockchain. For our purposes of re-distribution, we are also committed to ensure non-disclosure of information that might lead to identifying the NFT creators, in order to be compliant with privacy-preserving requirements and to avoid violation of data protection regulations and of property rights. In this respect, we carried out three actions: - Values of all variables describing non-sensitive information were kept in their original form; - Values of all variables describing sensitive information were anonymized, in a one-way, non-revertible mode; - URLs of image data and textual contents (i.e., NFT images and their descriptions) were replaced by identifiers to numerical vectors that represent an encrypted representation (i.e., embeddings) of the image/text contents obtained via neural network models. Such embeddings are eventually provided in place of their original image and text data, and can be found in the [NFT-70M_image](https://huggingface.co/datasets/MLNTeam-Unical/NFT-70M_image) and [NFT-70M_text](https://huggingface.co/datasets/MLNTeam-Unical/NFT-70M_text) supplementary datasets, respectively. ## Data Fields \| Variable \| Type \| Description \| Processing \| Notes \| \|--------------------------\|-------------\|-----------------------------------------------------------------------------------------------------------\|------------------\|-----------------------------------\| \| token_id \| String \| The id of the NFT — this value is unique within the same collection \| Anonymized \| Original values were replaced by hash-codes \| \| num_sales \| Integer \| A progressive integer indicating the number of successful transactions involving the NFT up to the current timestamp (cf. tx_timestamp) \| Original \| Not sensitive variable \| \| nft_name \| Vector ID \| The name of the NFT \| Anonymized \| Original values were encrypted via neural textual embedding \| \| nft_description \| Vector ID \| The description of the NFT as provided by the creator \| Anonymized \| Original values were encrypted via neural textual embedding \| \| nft_image \| Vector ID \| The ID for accessing the NFT image vector \| Anonymized \| Original values were encrypted via neural visual embedding \| \| collection_name \| Vector ID \| The ID for accessing the Collection name vector \| Anonymized \| Original values were encrypted via neural textual embedding \| \| collection_description \| Vector ID \| The ID for accessing the Collection description vector \| Anonymized \| Original values were encrypted via neural textual embedding \| \| collection_image \| Vector ID \| The ID for accessing the Collection image vector \| Anonymized \| Original values were encrypted via neural visual embedding \| \| fees_seller \| Float \| The absolute amount of fees the seller has gained from this transaction expressed in token \| Original \| Not sensitive variable \| \| fees_opensea \| Float \| The absolute amount of fees OpenSea has gained from this transaction expressed in token \| Original \| Not sensitive variable \| \| fees_seller_usd \| Float \| The absolute amount of fees the seller has gained from this transaction expressed in US dollars (USD) \| Original \| Not sensitive variable \| \| fees_opensea_usd \| Float \| The absolute amount of fees OpenSea has gained from this transaction expressed in US dollars (USD) \| Original \| Not sensitive variable \| \| payout_collection_address\| String \| The wallet address where seller fees are deposited \| Anonymized \| Original values were replaced by hash-codes \| \| tx_timestamp \| String \| Timestamp of the transaction expressed in yyyy-mm-ddTHH:MM:SS \| Original \| Not sensitive variable \| \| price \| Float \| The price of the transaction expressed in token \| Original \| Not sensitive variable \| \| gain \| Float \| The gain after fees (i.e., gain = price - fees_opensea * price - fees_seller * price) \| Original \| Not sensitive variable \| \| usd_price \| Float \| The price of the transaction expressed in US dollars (USD) \| Original \| Not sensitive variable \| \| usd_gain \| Float \| The difference between the price and the fees expressed in US dollars (USD) \| Original \| Not sensitive variable \| \| token \| Categorical \| The token type used to pay the transaction \| Original \| Not sensitive variable \| \| to_eth \| Float \| The conversion rate to convert tokens into Ethereum at the current timestamp, such that eth = price * to_eth \| Original \| Not sensitive variable \| \| to_usd \| Float \| The conversion rate to convert tokens into US dollars (USD) at the current timestamp, such that usd = price * to_usd \| Original \| Not sensitive variable \| \| from_account \| String \| The address that sends the payment (i.e., winner/buyer) \| Anonymized \| Original values were replaced by hash-codes \| \| to_account \| String \| The address that receives the payment (it often corresponds to the contract linked to the asset) \| Anonymized \| Original values were replaced by hash-codes \| \| seller_account \| String \| The address of the NFT seller \| Anonymized \| Original values were replaced by hash-codes \| \| winner_account \| String \| The address of the NFT buyer \| Anonymized \| Original values were replaced by hash-codes \| \| contract_address \| String \| The contract address on the blockchain \| Anonymized \| Original values were replaced by hash-codes \| \| created_date \| Timestamp \| The date of creation of the contract \| Original \| Not sensitive variable \| \| chain \| Categorical \| The blockchain where the transaction occurs \| Original \| Not sensitive variable \| \| token_type \| Categorical \| The schema of the token, i.e., ERC721 or ERC1155 \| Original \| Not sensitive variable \| \| asset_contract_type \| Categorical \| The asset typology, i.e., non-fungible or semi-fungible \| Original \| Not sensitive variable \| \| asset_type \| Categorical \| Whether the asset was involved in a simple or bundle transaction \| Original \| Not sensitive variable \| ## How to use Data provided within this repository can be straightforwardly loaded via the datasets library as follows: ```python from datasets import load_dataset dataset = load_dataset("MLNTeam-Unical/NFT-70M_transactions") ``` Complementary data involving textual and visual embeddings can be integrated as follows: ```python from datasets import load_dataset import numpy as np transactions_dataset=load_dataset("MLNTeam-Unical/NFT-70M_transactions") image_dataset=load_dataset("MLNTeam-Unical/NFT-70M_image") text_dataset=load_dataset("MLNTeam-Unical/NFT-70M_text") # Mapping from image_id to the row_index within the image dataset image_id2row_index={int(id):k for k,id in enumerate(image_dataset["train"]["id"])} # Mapping from text_id to row_index within the text dataset text_id2row_index={int(id):k for k,id in enumerate(text_dataset["train"]["id"])} def get_image_embedding(image_id,image_id2row_index,image_dataset): # If the mapping contains the image, the embedding exists idx_emb=image_id2row_index.get(int(image_id),None) if idx_emb: # If the embedding exists, return it return np.array(image_dataset["train"].select([idx_emb])["emb"][0]) else: return None def get_text_embedding(text_id,text_id2row_index,text_dataset): # If the mapping contains the text, the embedding exists idx_emb=text_id2row_index.get(int(text_id),None) if idx_emb: # If the embedding exists, return it return np.array(text_dataset["train"].select([idx_emb])["emb"][0]) else: return None ### USAGE EXAMPLE ### # Select transaction_id transaction_id=120 # Get the image_id (e.g., collection_image or nft_image) id_image=transactions_dataset["train"].select([transaction_id])["collection_image"][0] # Get the image image_embedding=get_image_embedding(id_image,image_id2row_index,image_dataset) # Get the text_id id_text=transactions_dataset["train"].select([transaction_id])["collection_description"][0] # Get the text text_embedding=get_text_embedding(id_text,text_id2row_index,text_dataset) ``` ## Ethical use of data and informed consent This data repository is made available for research and informational purposes only. Any finding that might be drawn from the data provided within this repository should be intended to support decision-making regarding actions made on NFTs, and not to replace the human specialists. The authors are not responsible for any issues related to trading failures based on the data provided within this repository. ## Terms of Usage Please cite the following papers in any research product whose findings are based on the data provided within this repository: - L. La Cava, D. Costa, A. Tagarelli: SONAR: Web-based Tool for Multimodal Exploration of Non-Fungible Token Inspiration Networks. In: Proc. ACM SIGIR 2023. Taipei, Taiwan, July 23-27 2023. DOI: https://doi.org/10.1145/3539618.3591821 - L. La Cava, D. Costa, A. Tagarelli: Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens. CoRR abs/2303.17031 (2023). DOI: https://doi.org/10.48550/arXiv.2303.17031 - D. Costa, L. La Cava, A. Tagarelli: Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction. In: Proc. ACM WebConf 2023, pp. 1875-1885. Austin, TX, USA, 30 April 2023 – 4 May 2023. DOI: https://doi.org/10.1145/3543507.3583520 Data within this repository were fetched using the REST APIs provided by OpenSea. You should also acknowledge [OpenSea API]("https://docs.opensea.io/reference/api-overview). ## Liability statement The authors hereby declare that they are not responsible for any harmful or objectionable content that may be contained within the data provided within this repository. Users of the dataset are expected to exercise due diligence and responsibility when using the data, including but not limited to: (i) Content Review: Users should review the dataset's contents carefully and assess its suitability for their intended purposes; (ii) Compliance: Users are responsible for ensuring that their use of the dataset complies with all applicable laws, regulations, and ethical standards; (iii) Data Processing: Users may need to apply data preprocessing, filtering, or other techniques to remove or address any objectionable or harmful content as needed. The authors of this dataset disclaim any liability for the accuracy, completeness, or suitability of the data and shall not be held responsible for any consequences resulting from the use or misuse of the dataset. By accessing and using this dataset, users acknowledge and accept this disclaimer.
dbaezaj/lince_ner_dataset	--- dataset_info: features: - name: id dtype: int64 - name: words sequence: string - name: lid sequence: string - name: labels sequence: string ---
open-llm-leaderboard/details_fionazhang__fine-tune-mistral-environment-merge	--- pretty_name: Evaluation run of fionazhang/fine-tune-mistral-environment-merge dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [fionazhang/fine-tune-mistral-environment-merge](https://huggingface.co/fionazhang/fine-tune-mistral-environment-merge)\ \ 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_fionazhang__fine-tune-mistral-environment-merge\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-29T01:47:21.122290](https://huggingface.co/datasets/open-llm-leaderboard/details_fionazhang__fine-tune-mistral-environment-merge/blob/main/results_2024-01-29T01-47-21.122290.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\": 0.6354317163514616,\n\ \ \"acc_stderr\": 0.032307072675482454,\n \"acc_norm\": 0.6419311935208026,\n\ \ \"acc_norm_stderr\": 0.03296064982960984,\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.4397408572877062,\n\ \ \"mc2_stderr\": 0.014194431681893268\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5725255972696246,\n \"acc_stderr\": 0.014456862944650649,\n\ \ \"acc_norm\": 0.6262798634812287,\n \"acc_norm_stderr\": 0.014137708601759086\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.635929097789285,\n\ \ \"acc_stderr\": 0.00480185288132974,\n \"acc_norm\": 0.8365863373829915,\n\ \ \"acc_norm_stderr\": 0.0036898701424130753\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6074074074074074,\n\ \ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\ \ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.038947344870133176,\n\ \ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.038947344870133176\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\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n\ \ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.653179190751445,\n\ \ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\ \ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155254,\n \"\ acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155254\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.043902592653775614,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.043902592653775614\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7548387096774194,\n\ \ \"acc_stderr\": 0.024472243840895525,\n \"acc_norm\": 0.7548387096774194,\n\ \ \"acc_norm_stderr\": 0.024472243840895525\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812142,\n\ \ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812142\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.02432173848460235,\n \ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.02432173848460235\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \ \ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.030956636328566548,\n\ \ \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.030956636328566548\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8201834862385321,\n \"acc_stderr\": 0.01646534546739154,\n \"\ acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.01646534546739154\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.033981108902946366,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.033981108902946366\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7843137254901961,\n \"acc_stderr\": 0.02886743144984932,\n \"\ acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.02886743144984932\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.03114679648297246,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.03114679648297246\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.034465133507525975,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.034465133507525975\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.036401182719909456,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.036401182719909456\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822585,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822585\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8071519795657727,\n\ \ \"acc_stderr\": 0.014108533515757431,\n \"acc_norm\": 0.8071519795657727,\n\ \ \"acc_norm_stderr\": 0.014108533515757431\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577615,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577615\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.36089385474860336,\n\ \ \"acc_stderr\": 0.016062290671110462,\n \"acc_norm\": 0.36089385474860336,\n\ \ \"acc_norm_stderr\": 0.016062290671110462\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.0248480182638752,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.0248480182638752\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632938,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632938\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.02500646975579921,\n\ \ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.02500646975579921\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4471968709256845,\n\ \ \"acc_stderr\": 0.012698825252435108,\n \"acc_norm\": 0.4471968709256845,\n\ \ \"acc_norm_stderr\": 0.012698825252435108\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000314,\n \ \ \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000314\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\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291296,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291296\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.4397408572877062,\n\ \ \"mc2_stderr\": 0.014194431681893268\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7892659826361483,\n \"acc_stderr\": 0.011462046419710681\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3525398028809704,\n \ \ \"acc_stderr\": 0.013159909755930323\n }\n}\n```" repo_url: https://huggingface.co/fionazhang/fine-tune-mistral-environment-merge 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_29T01_47_21.122290 path: - '/details_harness\|arc:challenge\|25_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-29T01-47-21.122290.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|gsm8k\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hellaswag\|10_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-29T01-47-21.122290.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-29T01-47-21.122290.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '/details_harness\|winogrande\|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-29T01-47-21.122290.parquet' - config_name: results data_files: - split: 2024_01_29T01_47_21.122290 path: - results_2024-01-29T01-47-21.122290.parquet - split: latest path: - results_2024-01-29T01-47-21.122290.parquet --- # Dataset Card for Evaluation run of fionazhang/fine-tune-mistral-environment-merge <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [fionazhang/fine-tune-mistral-environment-merge](https://huggingface.co/fionazhang/fine-tune-mistral-environment-merge) 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_fionazhang__fine-tune-mistral-environment-merge", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-29T01:47:21.122290](https://huggingface.co/datasets/open-llm-leaderboard/details_fionazhang__fine-tune-mistral-environment-merge/blob/main/results_2024-01-29T01-47-21.122290.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": 0.6354317163514616, "acc_stderr": 0.032307072675482454, "acc_norm": 0.6419311935208026, "acc_norm_stderr": 0.03296064982960984, "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.4397408572877062, "mc2_stderr": 0.014194431681893268 }, "harness\|arc:challenge\|25": { "acc": 0.5725255972696246, "acc_stderr": 0.014456862944650649, "acc_norm": 0.6262798634812287, "acc_norm_stderr": 0.014137708601759086 }, "harness\|hellaswag\|10": { "acc": 0.635929097789285, "acc_stderr": 0.00480185288132974, "acc_norm": 0.8365863373829915, "acc_norm_stderr": 0.0036898701424130753 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6447368421052632, "acc_stderr": 0.038947344870133176, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.038947344870133176 }, "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": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155254, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155254 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.043902592653775614, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.043902592653775614 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7548387096774194, "acc_stderr": 0.024472243840895525, "acc_norm": 0.7548387096774194, "acc_norm_stderr": 0.024472243840895525 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7515151515151515, "acc_stderr": 0.03374402644139403, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.03374402644139403 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.029376616484945633, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.029376616484945633 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8808290155440415, "acc_stderr": 0.02338193534812142, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.02338193534812142 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6410256410256411, "acc_stderr": 0.02432173848460235, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.02432173848460235 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251976, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251976 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6512605042016807, "acc_stderr": 0.030956636328566548, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.030956636328566548 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8201834862385321, "acc_stderr": 0.01646534546739154, "acc_norm": 0.8201834862385321, "acc_norm_stderr": 0.01646534546739154 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.033981108902946366, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.033981108902946366 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7843137254901961, "acc_stderr": 0.02886743144984932, "acc_norm": 0.7843137254901961, "acc_norm_stderr": 0.02886743144984932 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.03114679648297246, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.03114679648297246 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8091603053435115, "acc_stderr": 0.034465133507525975, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.034465133507525975 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.036401182719909456, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.036401182719909456 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822585, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822585 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165616, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165616 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8071519795657727, "acc_stderr": 0.014108533515757431, "acc_norm": 0.8071519795657727, "acc_norm_stderr": 0.014108533515757431 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577615, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577615 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.36089385474860336, "acc_stderr": 0.016062290671110462, "acc_norm": 0.36089385474860336, "acc_norm_stderr": 0.016062290671110462 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7483660130718954, "acc_stderr": 0.0248480182638752, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.0248480182638752 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632938, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632938 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7191358024691358, "acc_stderr": 0.02500646975579921, "acc_norm": 0.7191358024691358, "acc_norm_stderr": 0.02500646975579921 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4471968709256845, "acc_stderr": 0.012698825252435108, "acc_norm": 0.4471968709256845, "acc_norm_stderr": 0.012698825252435108 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462923, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462923 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6650326797385621, "acc_stderr": 0.019094228167000314, "acc_norm": 0.6650326797385621, "acc_norm_stderr": 0.019094228167000314 }, "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": 0.7183673469387755, "acc_stderr": 0.028795185574291296, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291296 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801302, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801302 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.4397408572877062, "mc2_stderr": 0.014194431681893268 }, "harness\|winogrande\|5": { "acc": 0.7892659826361483, "acc_stderr": 0.011462046419710681 }, "harness\|gsm8k\|5": { "acc": 0.3525398028809704, "acc_stderr": 0.013159909755930323 } } ``` ## 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]
Megnis/python_code_instructions_27k_Saiga	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 83038004 num_examples: 27224 download_size: 30871544 dataset_size: 83038004 configs: - config_name: default data_files: - split: train path: data/train-* ---
EmmaGthn/Moji_balanced	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: gender dtype: int64 splits: - name: train num_bytes: 6618798 num_examples: 53016 - name: test num_bytes: 256593 num_examples: 2000 download_size: 2582045 dataset_size: 6875391 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
CyberHarem/galleon_granbluefantasy	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of galleon/ガレヲン (Granblue Fantasy) This is the dataset of galleon/ガレヲン (Granblue Fantasy), containing 325 images and their tags. The core tags of this character are `brown_hair, long_hair, animal_ears, horns, breasts, pointy_ears, extra_ears, bangs, multicolored_hair, large_breasts, streaked_hair, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 325 \| 557.67 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 325 \| 293.91 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 815 \| 666.28 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 325 \| 483.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 815 \| 988.03 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/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/galleon_granbluefantasy', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, black_dress, closed_eyes, detached_sleeves, frilled_sleeves, solo, white_gloves, bare_shoulders, blush \| \| 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, closed_eyes, detached_sleeves, frilled_sleeves, solo, white_gloves, asymmetrical_hair, upper_body \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, asymmetrical_hair, asymmetrical_legwear, closed_eyes, detached_sleeves, frilled_sleeves, solo, thigh_strap, white_gloves, pelvic_curtain, black_dress, full_body, hair_between_eyes, single_thighhigh \| \| 3 \| 16 \| ![](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) \| 1boy, 1girl, closed_eyes, hetero, solo_focus, blush, detached_sleeves, white_gloves, nipples, paizuri, huge_breasts, mosaic_censoring, kissing_penis, nude \| \| 4 \| 17 \| ![](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, blindfold, cleavage, solo, blue_hair, smile, thigh_strap, long_sleeves, mask, nail_polish, closed_mouth, facing_viewer, parted_lips \| \| 5 \| 9 \| ![](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, bare_shoulders, cleavage, closed_eyes, navel, solo, bikini, hair_between_eyes, thighs, blush, blue_hair, collarbone, thigh_strap, wet \| \| 6 \| 11 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, closed_eyes, solo, cleavage, collared_shirt, long_sleeves, white_shirt, blue_hair, blush, smile, collarbone, hair_between_eyes, naked_shirt, navel, sitting \| \| 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, closed_eyes, completely_nude, hair_between_eyes, solo, smile, collarbone, nipples, artist_name, barefoot, blue_hair, blush, closed_mouth, lips, navel \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_dress \| closed_eyes \| detached_sleeves \| frilled_sleeves \| solo \| white_gloves \| bare_shoulders \| blush \| asymmetrical_hair \| upper_body \| asymmetrical_legwear \| thigh_strap \| pelvic_curtain \| full_body \| hair_between_eyes \| single_thighhigh \| 1boy \| hetero \| solo_focus \| nipples \| paizuri \| huge_breasts \| mosaic_censoring \| kissing_penis \| nude \| blindfold \| cleavage \| blue_hair \| smile \| long_sleeves \| mask \| nail_polish \| closed_mouth \| facing_viewer \| parted_lips \| navel \| bikini \| thighs \| collarbone \| wet \| collared_shirt \| white_shirt \| naked_shirt \| sitting \| completely_nude \| artist_name \| barefoot \| lips \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------\|:--------------\|:-------------------\|:------------------\|:-------\|:---------------\|:-----------------\|:--------\|:--------------------\|:-------------\|:-----------------------\|:--------------\|:-----------------\|:------------\|:--------------------\|:-------------------\|:-------\|:---------\|:-------------\|:----------\|:----------\|:---------------\|:-------------------\|:----------------\|:-------\|:------------\|:-----------\|:------------\|:--------\|:---------------\|:-------\|:--------------\|:---------------\|:----------------\|:--------------\|:--------\|:---------\|:---------\|:-------------\|:------\|:-----------------\|:--------------\|:--------------\|:----------\|:------------------\|:--------------\|:-----------\|:-------\| \| 0 \| 8 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| \| X \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 16 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 17 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 9 \| ![](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 \| X \| X \| \| \| \| \| \| \| \| \| \| 6 \| 11 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| X \| \| \| X \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| X \| \| \| X \| \| X \| X \| X \| X \| \| \| \| \| \| 7 \| 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 \|
joseluhf11/oct-object-detection-v2-merge	--- dataset_info: features: - name: image dtype: image - name: objects struct: - name: bbox sequence: sequence: int64 - name: categories sequence: string splits: - name: train num_bytes: 153967507.25 num_examples: 1246 download_size: 71637288 dataset_size: 153967507.25 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "oct-object-detection-v2-merge" Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease. Changes from from v1 are images are grouped into a single row for the same class detection object, and also join with merge method overlapping boxes. merge means, get the whole area covered by both boxes. [Source datataset](https://doi.org/10.1101/2023.03.29.534704)
chirunder/MixSnips_for_DecoderOnly_90-10_split-HALF	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: text dtype: string splits: - name: train num_bytes: 17739996.800127994 num_examples: 22500 - name: test num_bytes: 1971899.199872005 num_examples: 2501 download_size: 7061034 dataset_size: 19711896.0 --- # Dataset Card for "MixSnips_for_DecoderOnly_90-10_split-HALF" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nlpUc3mStudents/mental-risk-d	--- dataset_info: features: - name: subject_id dtype: string - name: id_message dtype: int64 - name: date dtype: string - name: message dtype: string - name: suffer_in_favour dtype: float64 - name: suffer_against dtype: float64 - name: suffer_other dtype: float64 - name: control dtype: float64 splits: - name: train num_bytes: 949991 num_examples: 6248 - name: test num_bytes: 91047 num_examples: 624 download_size: 486498 dataset_size: 1041038 --- # Dataset Card for "mental-risk-d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Ezi/test_Up

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: package_name dtype: string - name: review dtype: string - name: date dtype: string - name: star dtype: int64 - name: version_id dtype: int64 splits: - name: train num_bytes: 1508 num_examples: 5 - name: test num_bytes: 956 num_examples: 5 download_size: 9453 dataset_size: 2464 ---

g-ronimo/oasst2_top1_en

--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 10491824 num_examples: 5419 download_size: 5658552 dataset_size: 10491824 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 --- # Dataset Card for "oasst2_top1_en" * Top 1% conversations of https://huggingface.co/datasets/OpenAssistant/oasst2 * language-filtered: en * generated using https://github.com/blancsw/deep_4_all/blob/main/datasets/oasst/convert.py

open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B

--- pretty_name: Evaluation run of vicgalle/SystemHermes-2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [vicgalle/SystemHermes-2-7B](https://huggingface.co/vicgalle/SystemHermes-2-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T11:03:07.781697](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B/blob/main/results_2024-03-21T11-03-07.781697.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\": 0.6338248776795873,\n\ \ \"acc_stderr\": 0.03237220063508142,\n \"acc_norm\": 0.6354461960257087,\n\ \ \"acc_norm_stderr\": 0.03302051993311256,\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5641556907529288,\n\ \ \"mc2_stderr\": 0.015439070980915756\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6228668941979523,\n \"acc_stderr\": 0.0141633668961926,\n\ \ \"acc_norm\": 0.6501706484641638,\n \"acc_norm_stderr\": 0.013936809212158296\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6506671977693687,\n\ \ \"acc_stderr\": 0.0047578490234119605,\n \"acc_norm\": 0.8404700258912567,\n\ \ \"acc_norm_stderr\": 0.003654212329516619\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.042849586397534015,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.042849586397534015\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\ \ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n\ \ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\ \ \"acc_stderr\": 0.037242495958177295,\n \"acc_norm\": 0.6069364161849711,\n\ \ \"acc_norm_stderr\": 0.037242495958177295\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\ \ \"acc_stderr\": 0.04692008381368909,\n \"acc_norm\": 0.4649122807017544,\n\ \ \"acc_norm_stderr\": 0.04692008381368909\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778405,\n \"\ acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778405\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n\ \ \"acc_stderr\": 0.023664216671642514,\n \"acc_norm\": 0.7774193548387097,\n\ \ \"acc_norm_stderr\": 0.023664216671642514\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6282051282051282,\n \"acc_stderr\": 0.024503472557110936,\n\ \ \"acc_norm\": 0.6282051282051282,\n \"acc_norm_stderr\": 0.024503472557110936\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \ \ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8275229357798165,\n \"acc_stderr\": 0.016197807956848036,\n \"\ acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.016197807956848036\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"\ acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676173,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676173\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\ \ \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n\ \ \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.036412970813137276,\n\ \ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.036412970813137276\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\ \ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.04058042015646034,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.04058042015646034\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n\ \ \"acc_stderr\": 0.013853724170922524,\n \"acc_norm\": 0.8160919540229885,\n\ \ \"acc_norm_stderr\": 0.013853724170922524\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526502,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526502\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.311731843575419,\n\ \ \"acc_stderr\": 0.015491756531894638,\n \"acc_norm\": 0.311731843575419,\n\ \ \"acc_norm_stderr\": 0.015491756531894638\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.024404394928087866,\n\ \ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.024404394928087866\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600713,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600713\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303956,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303956\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n\ \ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806304,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806304\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139968,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139968\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7960199004975125,\n\ \ \"acc_stderr\": 0.02849317624532607,\n \"acc_norm\": 0.7960199004975125,\n\ \ \"acc_norm_stderr\": 0.02849317624532607\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368036,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368036\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3880048959608323,\n\ \ \"mc1_stderr\": 0.017058761501347972,\n \"mc2\": 0.5641556907529288,\n\ \ \"mc2_stderr\": 0.015439070980915756\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698327\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6156178923426838,\n \ \ \"acc_stderr\": 0.013399219253698184\n }\n}\n```" repo_url: https://huggingface.co/vicgalle/SystemHermes-2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|arc:challenge|25_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-21T11-03-07.781697.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|gsm8k|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hellaswag|10_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-03-07.781697.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-03-07.781697.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T11-03-07.781697.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T11_03_07.781697 path: - '**/details_harness|winogrande|5_2024-03-21T11-03-07.781697.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-21T11-03-07.781697.parquet' - config_name: results data_files: - split: 2024_03_21T11_03_07.781697 path: - results_2024-03-21T11-03-07.781697.parquet - split: latest path: - results_2024-03-21T11-03-07.781697.parquet --- # Dataset Card for Evaluation run of vicgalle/SystemHermes-2-7B  Dataset automatically created during the evaluation run of model [vicgalle/SystemHermes-2-7B](https://huggingface.co/vicgalle/SystemHermes-2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T11:03:07.781697](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__SystemHermes-2-7B/blob/main/results_2024-03-21T11-03-07.781697.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": 0.6338248776795873, "acc_stderr": 0.03237220063508142, "acc_norm": 0.6354461960257087, "acc_norm_stderr": 0.03302051993311256, "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5641556907529288, "mc2_stderr": 0.015439070980915756 }, "harness|arc:challenge|25": { "acc": 0.6228668941979523, "acc_stderr": 0.0141633668961926, "acc_norm": 0.6501706484641638, "acc_norm_stderr": 0.013936809212158296 }, "harness|hellaswag|10": { "acc": 0.6506671977693687, "acc_stderr": 0.0047578490234119605, "acc_norm": 0.8404700258912567, "acc_norm_stderr": 0.003654212329516619 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.562962962962963, "acc_stderr": 0.042849586397534015, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.042849586397534015 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.047609522856952344, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952344 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6069364161849711, "acc_stderr": 0.037242495958177295, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.037242495958177295 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4649122807017544, "acc_stderr": 0.04692008381368909, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.04692008381368909 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5172413793103449, "acc_stderr": 0.04164188720169375, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.04164188720169375 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778405, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778405 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7774193548387097, "acc_stderr": 0.023664216671642514, "acc_norm": 0.7774193548387097, "acc_norm_stderr": 0.023664216671642514 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709443, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6282051282051282, "acc_stderr": 0.024503472557110936, "acc_norm": 0.6282051282051282, "acc_norm_stderr": 0.024503472557110936 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.680672268907563, "acc_stderr": 0.030283995525884396, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.030283995525884396 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8275229357798165, "acc_stderr": 0.016197807956848036, "acc_norm": 0.8275229357798165, "acc_norm_stderr": 0.016197807956848036 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8088235294117647, "acc_stderr": 0.027599174300640766, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.027599174300640766 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676173, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676173 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6681614349775785, "acc_stderr": 0.031602951437766785, "acc_norm": 0.6681614349775785, "acc_norm_stderr": 0.031602951437766785 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7786259541984732, "acc_stderr": 0.036412970813137276, "acc_norm": 0.7786259541984732, "acc_norm_stderr": 0.036412970813137276 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.03957835471980981, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.03957835471980981 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7914110429447853, "acc_stderr": 0.031921934489347235, "acc_norm": 0.7914110429447853, "acc_norm_stderr": 0.031921934489347235 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.04058042015646034, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.04058042015646034 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8160919540229885, "acc_stderr": 0.013853724170922524, "acc_norm": 0.8160919540229885, "acc_norm_stderr": 0.013853724170922524 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7254335260115607, "acc_stderr": 0.02402774515526502, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.02402774515526502 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.311731843575419, "acc_stderr": 0.015491756531894638, "acc_norm": 0.311731843575419, "acc_norm_stderr": 0.015491756531894638 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.761437908496732, "acc_stderr": 0.024404394928087866, "acc_norm": 0.761437908496732, "acc_norm_stderr": 0.024404394928087866 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600713, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600713 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5035460992907801, "acc_stderr": 0.02982674915328092, "acc_norm": 0.5035460992907801, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303956, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303956 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6617647058823529, "acc_stderr": 0.028739328513983572, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.028739328513983572 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806304, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806304 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.746938775510204, "acc_stderr": 0.02783302387139968, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.02783302387139968 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7960199004975125, "acc_stderr": 0.02849317624532607, "acc_norm": 0.7960199004975125, "acc_norm_stderr": 0.02849317624532607 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197768, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197768 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.029913127232368036, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.029913127232368036 }, "harness|truthfulqa:mc|0": { "mc1": 0.3880048959608323, "mc1_stderr": 0.017058761501347972, "mc2": 0.5641556907529288, "mc2_stderr": 0.015439070980915756 }, "harness|winogrande|5": { "acc": 0.7734806629834254, "acc_stderr": 0.011764149054698327 }, "harness|gsm8k|5": { "acc": 0.6156178923426838, "acc_stderr": 0.013399219253698184 } } ``` ## 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]

globis-university/aozorabunko-clean

--- license: cc-by-4.0 task_categories: - text-generation - text-classification language: - ja size_categories: - 10K<n<100K --- # Overview This dataset provides a convenient and user-friendly format of data from [Aozora Bunko (青空文庫)](https://www.aozora.gr.jp/), a website that compiles public-domain books in Japan, ideal for Machine Learning applications. [For Japanese] 日本語での概要説明を Qiita に記載しました: https://qiita.com/akeyhero/items/b53eae1c0bc4d54e321f # Methodology The code to reproduce this dataset is made available on GitHub: [globis-org/aozorabunko-exctractor](https://github.com/globis-org/aozorabunko-extractor). ## 1. Data collection We firstly downloaded the [CSV file that lists all works](https://www.aozora.gr.jp/index_pages/person_all.html). The information extracted from this CSV is incorporated into the `meta` field. Next, we filtered out any books not categorized as public domain. We retrieved the main text of each book corresponding to every row in the CSV and incorporated it into the `text` field in UTF-8. ## 2. Deduplication We removed entries where the `図書カードURL` (Library card URL) in this CSV did not match with the `作品ID` (Work ID) and `人物ID` (Person ID). In addition, entries with text identical to previously encountered text were discarded. ## 3. Cleaning The data in the `text` field was then cleaned in the following sequence: 1. Convert new lines to `\n` 2. Remove headers 3. Remove footnotes and add them to the `footnote` field 4. Convert inserted notes into regular parenthetical text 5. Remove ruby (phonetic guides) 6. Convert specific characters, such as external characters and iteration marks, into standard Unicode characters 7. Remove any remaining markup 8. Remove leading and trailing new lines and horizontal rules # Tips If you prefer to employ only modern Japanese, you can filter entries with: `row["meta"]["文字遣い種別"] == "新字新仮名"`. # Example ```py >>> from datasets import load_dataset >>> ds = load_dataset('globis-university/aozorabunko-clean') >>> ds DatasetDict({ train: Dataset({ features: ['text', 'footnote', 'meta'], num_rows: 16951 }) }) >>> ds = ds.filter(lambda row: row['meta']['文字遣い種別'] == '新字新仮名') # only modern Japanese >>> ds DatasetDict({ train: Dataset({ features: ['text', 'footnote', 'meta'], num_rows: 10246 }) }) >>> book = ds['train'][0] # one of the works >>> book['meta']['作品名'] 'ウェストミンスター寺院' >>> text = book['text'] # main content >>> len(text) 10639 >>> print(text[:100]) 深いおどろきにうたれて、名高いウェストミンスターに真鍮や石の記念碑となってすべての王侯貴族が集まっているのをみれば、今はさげすみも、ほこりも、見栄もない。善にかえった貴人の姿、華美と俗世の ``` # License CC BY 4.0

CyberHarem/li_sushang_honkai3

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of li_sushang (Houkai 3rd) This is the dataset of li_sushang (Houkai 3rd), containing 190 images and their tags. The core tags of this character are `brown_hair, long_hair, bangs, breasts, hair_ornament, brown_eyes, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 190 | 359.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 190 | 172.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 456 | 361.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 190 | 301.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 456 | 552.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/li_sushang_honkai3/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/li_sushang_honkai3', 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 | 13 | ![](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, bare_shoulders, china_dress, closed_mouth, fingerless_gloves, looking_at_viewer, smile, solo, white_dress, white_gloves, elbow_gloves | | 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, bare_shoulders, china_dress, closed_mouth, elbow_gloves, fingerless_gloves, holding_sword, looking_at_viewer, smile, solo, white_dress, white_gloves | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blush, nipples, cum_in_pussy, hetero, solo_focus, multiple_penises, open_mouth, vaginal, 3boys, cum_on_breasts, double_handjob, ejaculation, gangbang, navel, nude, piercing, pubic_hair, pubic_tattoo, spread_legs, thighhighs, tongue_out | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | china_dress | closed_mouth | fingerless_gloves | looking_at_viewer | smile | solo | white_dress | white_gloves | elbow_gloves | holding_sword | blush | nipples | cum_in_pussy | hetero | solo_focus | multiple_penises | open_mouth | vaginal | 3boys | cum_on_breasts | double_handjob | ejaculation | gangbang | navel | nude | piercing | pubic_hair | pubic_tattoo | spread_legs | thighhighs | tongue_out | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------|:---------------|:--------------------|:--------------------|:--------|:-------|:--------------|:---------------|:---------------|:----------------|:--------|:----------|:---------------|:---------|:-------------|:-------------------|:-------------|:----------|:--------|:-----------------|:-----------------|:--------------|:-----------|:--------|:-------|:-----------|:-------------|:---------------|:--------------|:-------------|:-------------| | 0 | 13 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](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 | X |

d0rj/hh-rlhf-ru

--- language_creators: - translated language: - ru multilinguality: - monolingual size_categories: - 100K<n<1M pretty_name: HH for RLHF (ru) source_datasets: - Anthropic/hh-rlhf license: mit tags: - human-feedback - ChatGPT - reward dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 573845356.0 num_examples: 160800 - name: test num_bytes: 30792414.0 num_examples: 8552 download_size: 281014419 dataset_size: 604637770.0 --- # Dataset Card for "hh-rlhf-ru" This is translated version of [Anthropic/hh-rlhf dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) into Russian.

Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647_random

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_1_bs_16 num_bytes: 86816473.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 90734475.125 num_examples: 5647 download_size: 169654260 dataset_size: 177550948.25 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

KETI-AIR/KITD_SAMPLE

--- license: cc-by-nc-sa-4.0 dataset_info: features: - name: input dtype: string - name: output dtype: string - name: data_name dtype: string - name: data_split_name dtype: string - name: data_index_by_user dtype: int32 splits: - name: FULL num_bytes: 5331202141 num_examples: 5409719 - name: SAMPLE num_bytes: 9462273 num_examples: 4242 download_size: 2014165075 dataset_size: 5340664414 ---

kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-135000

--- dataset_info: features: - name: input_ids sequence: sequence: int32 - name: attention_mask sequence: sequence: int8 - name: labels sequence: sequence: int64 splits: - name: train num_bytes: 13336000 num_examples: 1000 download_size: 661472 dataset_size: 13336000 configs: - config_name: default data_files: - split: train path: data/train-* ---

pa-shk/sberquad

--- dataset_info: - config_name: docs features: - name: doc dtype: string splits: - name: train num_bytes: 18450007 num_examples: 13489 download_size: 9682240 dataset_size: 18450007 - config_name: qrels features: - name: query dtype: string - name: relevant_docs sequence: int64 splits: - name: train num_bytes: 6111481 num_examples: 45326 - name: validation num_bytes: 677073 num_examples: 5036 - name: test num_bytes: 3226461 num_examples: 23935 download_size: 4620507 dataset_size: 10015015 configs: - config_name: docs data_files: - split: train path: docs/train-* - config_name: qrels data_files: - split: train path: qrels/train-* - split: validation path: qrels/validation-* - split: test path: qrels/test-* ---

defog/wikisql

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 5525298 num_examples: 1000 download_size: 761250 dataset_size: 5525298 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wikisql" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yzhuang/metatree_pendigits

--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float64 - name: y dtype: int64 splits: - name: train num_bytes: 1143744 num_examples: 7728 - name: validation num_bytes: 483072 num_examples: 3264 download_size: 1332707 dataset_size: 1626816 --- # Dataset Card for "metatree_pendigits" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

anan-2024/twitter_dataset_1712979666

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 22965 num_examples: 51 download_size: 12493 dataset_size: 22965 configs: - config_name: default data_files: - split: train path: data/train-* ---

bdice/rapids-codegen

--- dataset_info: features: - name: repo_id dtype: string - name: file_path dtype: string - name: content dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 378315950 num_examples: 16827 download_size: 151107014 dataset_size: 378315950 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "rapids-codegen" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

chanwit/ultrachat_200k_filtered

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train_sft num_bytes: 1396561957 num_examples: 207646 - name: test_sft num_bytes: 154634580 num_examples: 23082 download_size: 813170063 dataset_size: 1551196537 configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* ---

tner/wikiann

--- language: - ace - bg - da - fur - ilo - lij - mzn - qu - su - vi - af - bh - de - fy - io - lmo - nap - rm - sv - vls - als - bn - diq - ga - is - ln - nds - ro - sw - vo - am - bo - dv - gan - it - lt - ne - ru - szl - wa - an - br - el - gd - ja - lv - nl - rw - ta - war - ang - bs - eml - gl - jbo - nn - sa - te - wuu - ar - ca - en - gn - jv - mg - no - sah - tg - xmf - arc - eo - gu - ka - mhr - nov - scn - th - yi - arz - cdo - es - hak - kk - mi - oc - sco - tk - yo - as - ce - et - he - km - min - or - sd - tl - zea - ast - ceb - eu - hi - kn - mk - os - sh - tr - ay - ckb - ext - hr - ko - ml - pa - si - tt - az - co - fa - hsb - ksh - mn - pdc - ug - ba - crh - fi - hu - ku - mr - pl - sk - uk - zh - bar - cs - hy - ky - ms - pms - sl - ur - csb - fo - ia - la - mt - pnb - so - uz - cv - fr - id - lb - mwl - ps - sq - vec - be - cy - frr - ig - li - my - pt - sr multilinguality: - multilingual size_categories: - 10K<100k task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: WikiAnn --- # Dataset Card for "tner/wikiann" ## Dataset Description - **Repository:** [T-NER](https://github.com/asahi417/tner) - **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/) - **Dataset:** WikiAnn - **Domain:** Wikipedia - **Number of Entity:** 3 ### Dataset Summary WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. - Entity Types: `LOC`, `ORG`, `PER` ## Dataset Structure ### Data Instances An example of `train` of `ja` looks as follows. ``` { 'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'], 'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json). ```python { "B-LOC": 0, "B-ORG": 1, "B-PER": 2, "I-LOC": 3, "I-ORG": 4, "I-PER": 5, "O": 6 } ``` ### Data Splits | language | train | validation | test | |:-------------|--------:|-------------:|-------:| | ace | 100 | 100 | 100 | | bg | 20000 | 10000 | 10000 | | da | 20000 | 10000 | 10000 | | fur | 100 | 100 | 100 | | ilo | 100 | 100 | 100 | | lij | 100 | 100 | 100 | | mzn | 100 | 100 | 100 | | qu | 100 | 100 | 100 | | su | 100 | 100 | 100 | | vi | 20000 | 10000 | 10000 | | af | 5000 | 1000 | 1000 | | bh | 100 | 100 | 100 | | de | 20000 | 10000 | 10000 | | fy | 1000 | 1000 | 1000 | | io | 100 | 100 | 100 | | lmo | 100 | 100 | 100 | | nap | 100 | 100 | 100 | | rm | 100 | 100 | 100 | | sv | 20000 | 10000 | 10000 | | vls | 100 | 100 | 100 | | als | 100 | 100 | 100 | | bn | 10000 | 1000 | 1000 | | diq | 100 | 100 | 100 | | ga | 1000 | 1000 | 1000 | | is | 1000 | 1000 | 1000 | | ln | 100 | 100 | 100 | | nds | 100 | 100 | 100 | | ro | 20000 | 10000 | 10000 | | sw | 1000 | 1000 | 1000 | | vo | 100 | 100 | 100 | | am | 100 | 100 | 100 | | bo | 100 | 100 | 100 | | dv | 100 | 100 | 100 | | gan | 100 | 100 | 100 | | it | 20000 | 10000 | 10000 | | lt | 10000 | 10000 | 10000 | | ne | 100 | 100 | 100 | | ru | 20000 | 10000 | 10000 | | szl | 100 | 100 | 100 | | wa | 100 | 100 | 100 | | an | 1000 | 1000 | 1000 | | br | 1000 | 1000 | 1000 | | el | 20000 | 10000 | 10000 | | gd | 100 | 100 | 100 | | ja | 20000 | 10000 | 10000 | | lv | 10000 | 10000 | 10000 | | nl | 20000 | 10000 | 10000 | | rw | 100 | 100 | 100 | | ta | 15000 | 1000 | 1000 | | war | 100 | 100 | 100 | | ang | 100 | 100 | 100 | | bs | 15000 | 1000 | 1000 | | eml | 100 | 100 | 100 | | gl | 15000 | 10000 | 10000 | | jbo | 100 | 100 | 100 | | map-bms | 100 | 100 | 100 | | nn | 20000 | 1000 | 1000 | | sa | 100 | 100 | 100 | | te | 1000 | 1000 | 1000 | | wuu | 100 | 100 | 100 | | ar | 20000 | 10000 | 10000 | | ca | 20000 | 10000 | 10000 | | en | 20000 | 10000 | 10000 | | gn | 100 | 100 | 100 | | jv | 100 | 100 | 100 | | mg | 100 | 100 | 100 | | no | 20000 | 10000 | 10000 | | sah | 100 | 100 | 100 | | tg | 100 | 100 | 100 | | xmf | 100 | 100 | 100 | | arc | 100 | 100 | 100 | | cbk-zam | 100 | 100 | 100 | | eo | 15000 | 10000 | 10000 | | gu | 100 | 100 | 100 | | ka | 10000 | 10000 | 10000 | | mhr | 100 | 100 | 100 | | nov | 100 | 100 | 100 | | scn | 100 | 100 | 100 | | th | 20000 | 10000 | 10000 | | yi | 100 | 100 | 100 | | arz | 100 | 100 | 100 | | cdo | 100 | 100 | 100 | | es | 20000 | 10000 | 10000 | | hak | 100 | 100 | 100 | | kk | 1000 | 1000 | 1000 | | mi | 100 | 100 | 100 | | oc | 100 | 100 | 100 | | sco | 100 | 100 | 100 | | tk | 100 | 100 | 100 | | yo | 100 | 100 | 100 | | as | 100 | 100 | 100 | | ce | 100 | 100 | 100 | | et | 15000 | 10000 | 10000 | | he | 20000 | 10000 | 10000 | | km | 100 | 100 | 100 | | min | 100 | 100 | 100 | | or | 100 | 100 | 100 | | sd | 100 | 100 | 100 | | tl | 10000 | 1000 | 1000 | | zea | 100 | 100 | 100 | | ast | 1000 | 1000 | 1000 | | ceb | 100 | 100 | 100 | | eu | 10000 | 10000 | 10000 | | hi | 5000 | 1000 | 1000 | | kn | 100 | 100 | 100 | | mk | 10000 | 1000 | 1000 | | os | 100 | 100 | 100 | | sh | 20000 | 10000 | 10000 | | tr | 20000 | 10000 | 10000 | | zh-classical | 100 | 100 | 100 | | ay | 100 | 100 | 100 | | ckb | 1000 | 1000 | 1000 | | ext | 100 | 100 | 100 | | hr | 20000 | 10000 | 10000 | | ko | 20000 | 10000 | 10000 | | ml | 10000 | 1000 | 1000 | | pa | 100 | 100 | 100 | | si | 100 | 100 | 100 | | tt | 1000 | 1000 | 1000 | | zh-min-nan | 100 | 100 | 100 | | az | 10000 | 1000 | 1000 | | co | 100 | 100 | 100 | | fa | 20000 | 10000 | 10000 | | hsb | 100 | 100 | 100 | | ksh | 100 | 100 | 100 | | mn | 100 | 100 | 100 | | pdc | 100 | 100 | 100 | | simple | 20000 | 1000 | 1000 | | ug | 100 | 100 | 100 | | zh-yue | 20000 | 10000 | 10000 | | ba | 100 | 100 | 100 | | crh | 100 | 100 | 100 | | fi | 20000 | 10000 | 10000 | | hu | 20000 | 10000 | 10000 | | ku | 100 | 100 | 100 | | mr | 5000 | 1000 | 1000 | | pl | 20000 | 10000 | 10000 | | sk | 20000 | 10000 | 10000 | | uk | 20000 | 10000 | 10000 | | zh | 20000 | 10000 | 10000 | | bar | 100 | 100 | 100 | | cs | 20000 | 10000 | 10000 | | fiu-vro | 100 | 100 | 100 | | hy | 15000 | 1000 | 1000 | | ky | 100 | 100 | 100 | | ms | 20000 | 1000 | 1000 | | pms | 100 | 100 | 100 | | sl | 15000 | 10000 | 10000 | | ur | 20000 | 1000 | 1000 | | bat-smg | 100 | 100 | 100 | | csb | 100 | 100 | 100 | | fo | 100 | 100 | 100 | | ia | 100 | 100 | 100 | | la | 5000 | 1000 | 1000 | | mt | 100 | 100 | 100 | | pnb | 100 | 100 | 100 | | so | 100 | 100 | 100 | | uz | 1000 | 1000 | 1000 | | be-x-old | 5000 | 1000 | 1000 | | cv | 100 | 100 | 100 | | fr | 20000 | 10000 | 10000 | | id | 20000 | 10000 | 10000 | | lb | 5000 | 1000 | 1000 | | mwl | 100 | 100 | 100 | | ps | 100 | 100 | 100 | | sq | 5000 | 1000 | 1000 | | vec | 100 | 100 | 100 | | be | 15000 | 1000 | 1000 | | cy | 10000 | 1000 | 1000 | | frr | 100 | 100 | 100 | | ig | 100 | 100 | 100 | | li | 100 | 100 | 100 | | my | 100 | 100 | 100 | | pt | 20000 | 10000 | 10000 | | sr | 20000 | 10000 | 10000 | | vep | 100 | 100 | 100 | ### Citation Information ``` @inproceedings{pan-etal-2017-cross, title = "Cross-lingual Name Tagging and Linking for 282 Languages", author = "Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P17-1178", doi = "10.18653/v1/P17-1178", pages = "1946--1958", abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.", } ```

CyberHarem/yukikaze_kantaicollection

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yukikaze/雪風 (Kantai Collection) This is the dataset of yukikaze/雪風 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `brown_hair, short_hair, brown_eyes, headgear, hair_ornament, 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 | 500 | 529.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 329.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1130 | 689.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 478.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1130 | 933.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukikaze_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/yukikaze_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blue_sailor_collar, open_mouth, simple_background, smile, solo, speaking_tube_headset, upper_teeth_only, looking_at_viewer, round_teeth, sailor_dress, white_background, yellow_neckerchief, twitter_username, upper_body | | 1 | 19 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, hair_flower, open_mouth, sailor_dress, smile, solo, upper_teeth_only, grey_neckerchief, white_dress, black_sailor_collar, long_sleeves, round_teeth, speaking_tube_headset, blue_sailor_collar, cherry_blossoms, simple_background, white_background, anchor_symbol, pink_flower, blush, cowboy_shot, full_body | | 2 | 10 | ![](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, hair_flower, sailor_dress, solo, cherry_blossoms, grey_neckerchief, upper_body, white_dress, long_sleeves, simple_background, white_background, black_sailor_collar, grey_necktie, smile, speaking_tube_headset, blue_sailor_collar, blush, looking_at_viewer, closed_mouth, pink_flower | | 3 | 14 | ![](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, sailor_dress, smile, solo, binoculars, open_mouth, looking_at_viewer, salute, school_uniform | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, looking_at_viewer, sailor_dress, solo, white_panties, open_mouth, smile, binoculars, character_name | | 5 | 9 | ![](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) | 2girls, open_mouth, sailor_dress, binoculars, blonde_hair, blush, long_hair, smile, white_panties | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, anchor_symbol, cowboy_shot, looking_at_viewer, open_mouth, smile, solo, speaking_tube_headset, straw_hat, sun_hat, sundress, white_dress, collarbone, upper_teeth_only, bow, official_alternate_costume, bag, blush, hair_between_eyes, hat_flower, jewelry, off-shoulder_dress, round_teeth, simple_background, sunflower, white_background | | 7 | 8 | ![](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, anchor_symbol, blue_sky, cloud, day, looking_at_viewer, open_mouth, outdoors, solo, speaking_tube_headset, straw_hat, sun_hat, sundress, white_dress, smile, sunflower, bow, upper_teeth_only, collarbone, hat_flower, upper_body, yellow_flower | | 8 | 6 | ![](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, double_bun, official_alternate_costume, open_mouth, solo, looking_at_viewer, smile, upper_teeth_only, white_shirt, blush, upper_body | | 9 | 20 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, solo, looking_at_viewer, white_jacket, school_swimsuit, hooded_jacket, speaking_tube_headset, smile, hoodie, name_tag, open_mouth, blush, collarbone, long_sleeves, swimsuit_under_clothes, blue_one-piece_swimsuit, hair_between_eyes, teeth, black_one-piece_swimsuit, sitting | | 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) | denim_jacket, hair_flower, official_alternate_costume, 1girl, blue_headwear, cowboy_shot, open_mouth, smile, solo, bag, blue_jacket, round_teeth, upper_teeth_only, white_skirt, beret, black_headwear, breast_pocket, cherry_blossoms, long_sleeves, white_dress | | 11 | 5 | ![](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, open_mouth, solo_focus, bottomless, navel, penis, sailor_dress, sex, small_breasts, spread_legs, vaginal, covered_nipples, cum_in_pussy, see-through, socks, bar_censor, loli, mosaic_censoring, neckerchief, pov, school_uniform, simple_background, sweat, teeth, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_sailor_collar | open_mouth | simple_background | smile | solo | speaking_tube_headset | upper_teeth_only | looking_at_viewer | round_teeth | sailor_dress | white_background | yellow_neckerchief | twitter_username | upper_body | hair_flower | grey_neckerchief | white_dress | black_sailor_collar | long_sleeves | cherry_blossoms | anchor_symbol | pink_flower | blush | cowboy_shot | full_body | grey_necktie | closed_mouth | binoculars | salute | school_uniform | white_panties | character_name | 2girls | blonde_hair | long_hair | straw_hat | sun_hat | sundress | collarbone | bow | official_alternate_costume | bag | hair_between_eyes | hat_flower | jewelry | off-shoulder_dress | sunflower | blue_sky | cloud | day | outdoors | yellow_flower | double_bun | white_shirt | white_jacket | school_swimsuit | hooded_jacket | hoodie | name_tag | swimsuit_under_clothes | blue_one-piece_swimsuit | teeth | black_one-piece_swimsuit | sitting | denim_jacket | blue_headwear | blue_jacket | white_skirt | beret | black_headwear | breast_pocket | 1boy | hetero | solo_focus | bottomless | navel | penis | sex | small_breasts | spread_legs | vaginal | covered_nipples | cum_in_pussy | see-through | socks | bar_censor | loli | mosaic_censoring | neckerchief | pov | sweat | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------------|:-------------|:--------------------|:--------|:-------|:------------------------|:-------------------|:--------------------|:--------------|:---------------|:-------------------|:---------------------|:-------------------|:-------------|:--------------|:-------------------|:--------------|:----------------------|:---------------|:------------------|:----------------|:--------------|:--------|:--------------|:------------|:---------------|:---------------|:-------------|:---------|:-----------------|:----------------|:-----------------|:---------|:--------------|:------------|:------------|:----------|:-----------|:-------------|:------|:-----------------------------|:------|:--------------------|:-------------|:----------|:---------------------|:------------|:-----------|:--------|:------|:-----------|:----------------|:-------------|:--------------|:---------------|:------------------|:----------------|:---------|:-----------|:-------------------------|:--------------------------|:--------|:---------------------------|:----------|:---------------|:----------------|:--------------|:--------------|:--------|:-----------------|:----------------|:-------|:---------|:-------------|:-------------|:--------|:--------|:------|:----------------|:--------------|:----------|:------------------|:---------------|:--------------|:--------|:-------------|:-------|:-------------------|:--------------|:------|:--------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 19 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 10 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 14 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | | X | X | | | X | | X | | | | | | | | | | | | | | | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | X | X | X | X | X | X | X | X | | X | | | | | | X | | | | X | | X | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 8 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 20 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | X | | X | X | X | | X | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | X | 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 | | | | | | | | | | | | | | | | | | | | | | 11 | 5 | ![](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 | X | X |

freshpearYoon/train_free_42

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 9604916520 num_examples: 10000 download_size: 1451750277 dataset_size: 9604916520 configs: - config_name: default data_files: - split: train path: data/train-* ---

presencesw/pubmed_envi_stage_2_o

--- dataset_info: features: - name: en dtype: string - name: vi dtype: string splits: - name: train num_bytes: 24041184334.24373 num_examples: 10888215 download_size: 10807299020 dataset_size: 24041184334.24373 configs: - config_name: default data_files: - split: train path: data/train-* ---

mmaak/HealthCareMagic-llama2-5k

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5739555 num_examples: 5000 download_size: 3223162 dataset_size: 5739555 configs: - config_name: default data_files: - split: train path: data/train-* ---

Yeerchiu/mmm_lmd_16bars

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2016007300 num_examples: 115171 download_size: 326574922 dataset_size: 2016007300 configs: - config_name: default data_files: - split: train path: data/train-* ---

Star3073/Test_Interview

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 12365 num_examples: 15 download_size: 11573 dataset_size: 12365 configs: - config_name: default data_files: - split: train path: data/train-* ---

haturusinghe/sinhala_off-sinhala-to-english

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 5826095 num_examples: 38123 - name: test num_bytes: 340032 num_examples: 2219 download_size: 2331565 dataset_size: 6166127 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

CyberHarem/maury_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of maury/モーリー/莫里 (Azur Lane) This is the dataset of maury/モーリー/莫里 (Azur Lane), containing 29 images and their tags. The core tags of this character are `hair_ornament, hairclip, ahoge, long_hair, blonde_hair, yellow_eyes, hair_between_eyes, 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 | 29 | 24.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 29 | 17.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 71 | 38.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 29 | 23.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 71 | 46.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maury_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/maury_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 21 | ![](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, blush, looking_at_viewer, open_mouth, sleeveless_dress, white_sailor_collar, wristband, :d, bare_shoulders, blue_dress, brown_eyes, sailor_dress, collarbone, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | looking_at_viewer | open_mouth | sleeveless_dress | white_sailor_collar | wristband | :d | bare_shoulders | blue_dress | brown_eyes | sailor_dress | collarbone | simple_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:-------------|:-------------------|:----------------------|:------------|:-----|:-----------------|:-------------|:-------------|:---------------|:-------------|:--------------------| | 0 | 21 | ![](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 |

innodatalabs/rt-inod-finance

--- license: cc-by-sa-4.0 language: en task_categories: - text-generation - translation - summarization - question-answering - sentence-similarity tags: - red teaming labels: domain: finance genre: business docs skill: paraphrasing, Q&A, summarization, translation safety: factuality, toxicity dataset_info: - config_name: default data_files: - split: test path: innodata_finance_test.jsonl features: - name: messages list: - name: role dtype: string - name: content dtype: string - name: expected dtype: string - name: id dtype: string --- # FINANCE dataset Red teaming human-crafted finance dataset.

arbitropy/quac_prompt

--- dataset_info: features: - name: story dtype: string - name: questions sequence: string - name: answers sequence: string - name: source dtype: string - name: prompt sequence: string splits: - name: train num_bytes: 262740905 num_examples: 11567 - name: validation num_bytes: 25350167 num_examples: 1000 download_size: 65921336 dataset_size: 288091072 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-f4ef6e-41949145080

--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: ARTeLab/it5-summarization-fanpage metrics: [] dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # 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: ARTeLab/it5-summarization-fanpage * Dataset: cnn_dailymail * Config: 3.0.0 * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@neuromentor](https://huggingface.co/neuromentor) for evaluating this model.

vwxyzjn/openhermes-dev__mistralai_Mistral-7B-Instruct-v0.1__1706885434

--- dataset_info: features: - name: source dtype: string - name: skip_prompt_formatting dtype: bool - name: title dtype: 'null' - name: custom_instruction dtype: 'null' - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: 'null' - name: system_prompt dtype: 'null' - name: idx dtype: 'null' - name: id dtype: 'null' - name: model dtype: 'null' - name: topic dtype: 'null' - name: avatarUrl dtype: 'null' - name: model_name dtype: 'null' - name: language dtype: 'null' - name: views dtype: 'null' - name: hash dtype: 'null' - name: category dtype: string - name: prompt dtype: string - name: chosen_policy 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: rejected_policy dtype: string splits: - name: test_prefs num_bytes: 1823 num_examples: 1 - name: train_prefs num_bytes: 128821 num_examples: 23 download_size: 129887 dataset_size: 130644 configs: - config_name: default data_files: - split: test_prefs path: data/test_prefs-* - split: train_prefs path: data/train_prefs-* ---

jkorsvik/nowiki_abstract_second_scrape_20230201

--- dataset_info: features: - name: url dtype: string - name: date_scraped dtype: string - name: headline dtype: string - name: category dtype: string - name: ingress dtype: string - name: article dtype: string - name: abstract dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 841217948 num_examples: 614918 download_size: 211286623 dataset_size: 841217948 --- # Dataset Card for "nowiki_abstract_second_scrape_20230201" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

stjokerli/TextToText_mnli

--- license: mit ---

open-llm-leaderboard/details_garage-bAInd__Camel-Platypus2-70B

--- pretty_name: Evaluation run of garage-bAInd/Camel-Platypus2-70B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [garage-bAInd/Camel-Platypus2-70B](https://huggingface.co/garage-bAInd/Camel-Platypus2-70B)\ \ 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_garage-bAInd__Camel-Platypus2-70B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-16T06:37:05.018958](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Camel-Platypus2-70B/blob/main/results_2023-10-16T06-37-05.018958.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\": 0.5069211409395973,\n\ \ \"em_stderr\": 0.0051199774044148345,\n \"f1\": 0.559724203020135,\n\ \ \"f1_stderr\": 0.004829732229468497,\n \"acc\": 0.5345469918434537,\n\ \ \"acc_stderr\": 0.01116294273345166\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.5069211409395973,\n \"em_stderr\": 0.0051199774044148345,\n\ \ \"f1\": 0.559724203020135,\n \"f1_stderr\": 0.004829732229468497\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2395754359363154,\n \ \ \"acc_stderr\": 0.01175686434407741\n },\n \"harness|winogrande|5\":\ \ {\n \"acc\": 0.829518547750592,\n \"acc_stderr\": 0.010569021122825909\n\ \ }\n}\n```" repo_url: https://huggingface.co/garage-bAInd/Camel-Platypus2-70B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|arc:challenge|25_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-18T00:04:49.359575.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_23T09_15_03.498663 path: - '**/details_harness|drop|3_2023-09-23T09-15-03.498663.parquet' - split: 2023_10_16T06_37_05.018958 path: - '**/details_harness|drop|3_2023-10-16T06-37-05.018958.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-16T06-37-05.018958.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_23T09_15_03.498663 path: - '**/details_harness|gsm8k|5_2023-09-23T09-15-03.498663.parquet' - split: 2023_10_16T06_37_05.018958 path: - '**/details_harness|gsm8k|5_2023-10-16T06-37-05.018958.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-16T06-37-05.018958.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hellaswag|10_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-18T00:04:49.359575.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-management|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T00:04:49.359575.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_18T00_04_49.359575 path: - '**/details_harness|truthfulqa:mc|0_2023-08-18T00:04:49.359575.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-18T00:04:49.359575.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_23T09_15_03.498663 path: - '**/details_harness|winogrande|5_2023-09-23T09-15-03.498663.parquet' - split: 2023_10_16T06_37_05.018958 path: - '**/details_harness|winogrande|5_2023-10-16T06-37-05.018958.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-16T06-37-05.018958.parquet' - config_name: results data_files: - split: 2023_08_18T00_04_49.359575 path: - results_2023-08-18T00:04:49.359575.parquet - split: 2023_09_23T09_15_03.498663 path: - results_2023-09-23T09-15-03.498663.parquet - split: 2023_10_16T06_37_05.018958 path: - results_2023-10-16T06-37-05.018958.parquet - split: latest path: - results_2023-10-16T06-37-05.018958.parquet --- # Dataset Card for Evaluation run of garage-bAInd/Camel-Platypus2-70B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/garage-bAInd/Camel-Platypus2-70B - **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 [garage-bAInd/Camel-Platypus2-70B](https://huggingface.co/garage-bAInd/Camel-Platypus2-70B) 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_garage-bAInd__Camel-Platypus2-70B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-16T06:37:05.018958](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Camel-Platypus2-70B/blob/main/results_2023-10-16T06-37-05.018958.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": 0.5069211409395973, "em_stderr": 0.0051199774044148345, "f1": 0.559724203020135, "f1_stderr": 0.004829732229468497, "acc": 0.5345469918434537, "acc_stderr": 0.01116294273345166 }, "harness|drop|3": { "em": 0.5069211409395973, "em_stderr": 0.0051199774044148345, "f1": 0.559724203020135, "f1_stderr": 0.004829732229468497 }, "harness|gsm8k|5": { "acc": 0.2395754359363154, "acc_stderr": 0.01175686434407741 }, "harness|winogrande|5": { "acc": 0.829518547750592, "acc_stderr": 0.010569021122825909 } } ``` ### 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]

jilp00/water-diplomacy-transcripts

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 316351 num_examples: 223 download_size: 173329 dataset_size: 316351 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/nanakusa_nichika_theidolmstershinycolors

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nanakusa_nichika/七草にちか (THE iDOLM@STER: SHINY COLORS) This is the dataset of nanakusa_nichika/七草にちか (THE iDOLM@STER: SHINY COLORS), containing 365 images and their tags. The core tags of this character are `green_hair, short_hair, green_eyes, 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 | 365 | 578.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 365 | 287.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 870 | 631.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 365 | 491.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 870 | 994.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanakusa_nichika_theidolmstershinycolors/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/nanakusa_nichika_theidolmstershinycolors', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, white_shirt, blush, hairclip, long_sleeves, sweater, bag, cardigan, holding, simple_background, smile, white_background, white_skirt | | 1 | 35 | ![](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) | looking_at_viewer, 1girl, earrings, long_sleeves, frills, solo, nail_polish, smile, neck_ribbon, simple_background, upper_body, blush, jacket, very_long_hair, white_background, red_nails, white_shirt, black_ribbon, belt, open_mouth | | 2 | 9 | ![](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, bowtie, looking_at_viewer, school_uniform, simple_background, solo, sweater_vest, upper_body, blush, short_sleeves, white_background, white_shirt, striped_bow | | 3 | 14 | ![](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, bowtie, plaid_skirt, school_uniform, short_sleeves, sweater_vest, looking_at_viewer, pleated_skirt, solo, simple_background, white_shirt, blush, white_background, smile, socks, full_body | | 4 | 12 | ![](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) | frills, maid_headdress, solo, 1girl, looking_at_viewer, puffy_short_sleeves, wrist_cuffs, blush, smile, hair_ribbon, short_twintails, simple_background, black_bow, bowtie, enmaided, maid_apron, white_background, upper_body | | 5 | 11 | ![](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, hair_ornament, smile, solo, white_gloves, idol, looking_at_viewer, blush, dress, open_mouth, blue_skirt, detached_collar, simple_background, sweat, white_background | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, earrings, green_jacket, looking_at_viewer, solo, simple_background, sleeveless, white_background, bare_shoulders, midriff, navel, off_shoulder, ponytail, smile, upper_body, black_gloves, blush, closed_mouth, crop_top, fishnets, medium_breasts, necklace, open_jacket, very_long_hair | | 7 | 9 | ![](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, simple_background, white_background, blush, looking_at_viewer, navel, solo, collarbone, medium_breasts, panties | | 8 | 18 | ![](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, looking_at_viewer, outdoors, solo, blush, blue_sky, day, medium_breasts, collarbone, eyewear_on_head, hairclip, heart-shaped_eyewear, navel, ocean, sunglasses, visor_cap, beach, bracelet, cleavage, polka_dot_bikini, twintails, brown_eyes, brown_hair, cloud, smile | | 9 | 7 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, blush, hetero, penis, solo_focus, 1boy, nipples, cum_in_pussy, mosaic_censoring, open_mouth, sex, on_back, spread_legs, vaginal, medium_breasts, missionary, navel, nude, pubic_hair, small_breasts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | white_shirt | blush | hairclip | long_sleeves | sweater | bag | cardigan | holding | simple_background | smile | white_background | white_skirt | earrings | frills | nail_polish | neck_ribbon | upper_body | jacket | very_long_hair | red_nails | black_ribbon | belt | open_mouth | bowtie | school_uniform | sweater_vest | short_sleeves | striped_bow | plaid_skirt | pleated_skirt | socks | full_body | maid_headdress | puffy_short_sleeves | wrist_cuffs | hair_ribbon | short_twintails | black_bow | enmaided | maid_apron | hair_ornament | white_gloves | idol | dress | blue_skirt | detached_collar | sweat | green_jacket | sleeveless | bare_shoulders | midriff | navel | off_shoulder | ponytail | black_gloves | closed_mouth | crop_top | fishnets | medium_breasts | necklace | open_jacket | collarbone | panties | outdoors | blue_sky | day | eyewear_on_head | heart-shaped_eyewear | ocean | sunglasses | visor_cap | beach | bracelet | cleavage | polka_dot_bikini | twintails | brown_eyes | brown_hair | cloud | hetero | penis | solo_focus | 1boy | nipples | cum_in_pussy | mosaic_censoring | sex | on_back | spread_legs | vaginal | missionary | nude | pubic_hair | small_breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------------|:--------|:-----------|:---------------|:----------|:------|:-----------|:----------|:--------------------|:--------|:-------------------|:--------------|:-----------|:---------|:--------------|:--------------|:-------------|:---------|:-----------------|:------------|:---------------|:-------|:-------------|:---------|:-----------------|:---------------|:----------------|:--------------|:--------------|:----------------|:--------|:------------|:-----------------|:----------------------|:--------------|:--------------|:------------------|:------------|:-----------|:-------------|:----------------|:---------------|:-------|:--------|:-------------|:------------------|:--------|:---------------|:-------------|:-----------------|:----------|:--------|:---------------|:-----------|:---------------|:---------------|:-----------|:-----------|:-----------------|:-----------|:--------------|:-------------|:----------|:-----------|:-----------|:------|:------------------|:-----------------------|:--------|:-------------|:------------|:--------|:-----------|:-----------|:-------------------|:------------|:-------------|:-------------|:--------|:---------|:--------|:-------------|:-------|:----------|:---------------|:-------------------|:------|:----------|:--------------|:----------|:-------------|:-------|:-------------|:----------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 35 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | X | | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 14 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 11 | ![](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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | | X | | | | | | | X | X | X | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 18 | ![](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 | | | | | | | | | | | | | | | | | 9 | 7 | ![](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 |

CyberHarem/himari_bluearchive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of himari/明星ヒマリ/日鞠 (Blue Archive) This is the dataset of himari/明星ヒマリ/日鞠 (Blue Archive), containing 500 images and their tags. The core tags of this character are `mole, long_hair, pointy_ears, mole_under_eye, halo, hairband, purple_eyes, hair_ornament, black_hairband, hair_flower, grey_hair, white_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 1.01 GiB | [Download](https://huggingface.co/datasets/CyberHarem/himari_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 845.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himari_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1308 | 1.66 GiB | [Download](https://huggingface.co/datasets/CyberHarem/himari_bluearchive/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/himari_bluearchive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, black_gloves, hair_tubes, long_sleeves, looking_at_viewer, sitting, solo, smile, white_jacket, blush, simple_background, white_background, closed_mouth, wheelchair, white_flower | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, simple_background, smile, upper_body, white_background, white_flower, hair_tubes, looking_at_viewer, solo, white_jacket, open_mouth, long_sleeves, black_gloves | | 2 | 31 | ![](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, barefoot, hair_tubes, solo, toes, long_sleeves, looking_at_viewer, black_gloves, blush, white_jacket, simple_background, sitting, full_body, toenails, black_leggings, legs, soles, white_background, closed_mouth, foot_focus, white_flower, smile, striped_hairband, pants | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bare_shoulders, blush, hair_tubes, looking_at_viewer, small_breasts, solo, alternate_costume, collarbone, navel, simple_background, sitting, smile, stomach, flower, string_bikini, bare_arms, black_bikini, closed_mouth, side-tie_bikini_bottom, striped_hairband, wheelchair, white_bikini | | 4 | 15 | ![](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) | 1boy, 1girl, blush, hetero, solo_focus, flower, hair_tubes, nipples, open_mouth, penis, sex, small_breasts, completely_nude, cum, sweat, vaginal, bar_censor, navel, on_back | | 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, alternate_costume, fake_animal_ears, playboy_bunny, rabbit_ears, small_breasts, solo, strapless_leotard, detached_collar, looking_at_viewer, bare_shoulders, black_bowtie, black_pantyhose, blush, hair_tubes, open_mouth, rabbit_tail, smile, white_leotard, wrist_cuffs, ass, black_gloves, fake_tail, simple_background, white_background, white_flower | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | hair_tubes | long_sleeves | looking_at_viewer | sitting | solo | smile | white_jacket | blush | simple_background | white_background | closed_mouth | wheelchair | white_flower | upper_body | open_mouth | barefoot | toes | full_body | toenails | black_leggings | legs | soles | foot_focus | striped_hairband | pants | bare_shoulders | small_breasts | alternate_costume | collarbone | navel | stomach | flower | string_bikini | bare_arms | black_bikini | side-tie_bikini_bottom | white_bikini | 1boy | hetero | solo_focus | nipples | penis | sex | completely_nude | cum | sweat | vaginal | bar_censor | on_back | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | detached_collar | black_bowtie | black_pantyhose | rabbit_tail | white_leotard | wrist_cuffs | ass | fake_tail | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------------|:---------------|:--------------------|:----------|:-------|:--------|:---------------|:--------|:--------------------|:-------------------|:---------------|:-------------|:---------------|:-------------|:-------------|:-----------|:-------|:------------|:-----------|:-----------------|:-------|:--------|:-------------|:-------------------|:--------|:-----------------|:----------------|:--------------------|:-------------|:--------|:----------|:---------|:----------------|:------------|:---------------|:-------------------------|:---------------|:-------|:---------|:-------------|:----------|:--------|:------|:------------------|:------|:--------|:----------|:-------------|:----------|:-------------------|:----------------|:--------------|:--------------------|:------------------|:---------------|:------------------|:--------------|:----------------|:--------------|:------|:------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | X | X | X | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 31 | ![](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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | | X | X | X | X | | X | X | | X | X | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 15 | ![](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 | | | | | | | | | | | | | | 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 | X | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |

AI-B/CHI

--- license: unlicense tags: - UNA - DPO - ULMA pretty_name: CHI --- This repository outlines the methodology for creating training sets aimed at aligning a language model with a specific character and persona. The process involves utilizing a Direct Preference Optimization (DPO) dataset to steer the model towards embodying the defined character and persona traits. Following this, a Unified Neutral Alignment (UNA) dataset is employed to moderate any excessive sentiments resulting from the DPO training. The final step involves merging the model realigned with the UNA dataset into the original DPO-trained model, forming a Unified Language Model Alignment (ULMA). ### DPO Training Set (Target Character and Persona) 1. **Define Character and Persona**: Precisely define the traits, behaviors, and speech patterns of the intended character and persona, including language style, tone, typical responses, and unique characteristics. 2. **Dataset Construction**: Develop a dataset that reflects these characteristics through dialogues, monologues, and interactions typical of the persona. Ensure the dataset's diversity to encompass various scenarios and responses. 3. **Annotation**: Label each dataset instance with preference scores or binary labels, indicating its alignment with the target persona for effective DPO implementation. ### UNA Training Set (Neutralizing Extremes) 1. **Identify Extremes**: Identify extreme positive or negative sentiments in the context of your character, such as overly aggressive or excessively submissive language. 2. **Neutral Dataset**: Build a dataset representing neutral interactions and responses, focusing on language and replies that are balanced and free from identified extremes. 3. **Annotation for Neutrality**: Annotate the dataset to promote a neutral, balanced language style, possibly employing a point-wise preference approach similar to DPO. ### Training and Merging Models 1. **Train Separate Models**: Train one model using the DPO dataset and subsequently realign it using the UNA dataset. Each model will learn distinct aspects: character alignment and neutralization of extremes. 2. **Merging Models**: Combining two independently trained models into a single unified model is complex and often requires sophisticated techniques and deep understanding of model architectures. For this, we employ the `LazyMergeKit`. 3. **Evaluation and Adjustment**: Post-merging, assess the unified model to verify if it achieves the intended balance. Iterative refinement of the training datasets and merging process might be necessary based on evaluation outcomes.

SerahAKojenu/Assignment1

--- dataset_info: features: - name: longitude dtype: float64 - name: latitude dtype: float64 - name: housing_median_age dtype: float64 - name: total_rooms dtype: float64 - name: total_bedrooms dtype: float64 - name: population dtype: float64 - name: households dtype: float64 - name: median_income dtype: float64 - name: median_house_value dtype: float64 - name: ocean_proximity dtype: string splits: - name: train num_bytes: 1737680 num_examples: 20640 download_size: 0 dataset_size: 1737680 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Assignment1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa

--- configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_ic_qa path: data/train_ic_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_ic_qa path: data/eval_ic_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: all_docs path: data/all_docs-* - split: all_docs_eval path: data/all_docs_eval-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train_qa num_bytes: 697367 num_examples: 6000 - name: train_ic_qa num_bytes: 4540536 num_examples: 6000 - name: train_recite_qa num_bytes: 4546536 num_examples: 6000 - name: eval_qa num_bytes: 752802 num_examples: 6489 - name: eval_ic_qa num_bytes: 4906186 num_examples: 6489 - name: eval_recite_qa num_bytes: 4912675 num_examples: 6489 - name: all_docs num_bytes: 7126313 num_examples: 10925 - name: all_docs_eval num_bytes: 7125701 num_examples: 10925 - name: train num_bytes: 11672849 num_examples: 16925 - name: validation num_bytes: 4912675 num_examples: 6489 download_size: 31822578 dataset_size: 51193640 --- # Dataset Card for "lmind_nq_train6000_eval6489_v1_recite_qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

akanametov/minions-dataset

--- license: mit ---

CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_T_D_PNP_GENERIC_OCR_rices_ns_1000

--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large__text num_bytes: 12334750 num_examples: 1000 download_size: 2117039 dataset_size: 12334750 --- # Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_T_D_PNP_GENERIC_OCR_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

talentlabs/training-data-blog-writer_v03-09-2023

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: title dtype: string - name: article dtype: string - name: text dtype: string splits: - name: train num_bytes: 48639092 num_examples: 9504 download_size: 30032406 dataset_size: 48639092 --- # Dataset Card for "training-data-blog-writer_v03-09-2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MMInstruction/ArxivQA

--- license: cc-by-sa-4.0 task_categories: - image-to-text language: - en tags: - 'vision-language ' - vqa pretty_name: ArxivQA size_categories: - 10K<n<100K --- # Dataset Card for Mutlimodal Arxiv QA ## Dataset Loading Instruction Each line of the `arxivqa.jsonl` file is an example: ``` {"id": "cond-mat-2862", "image": "images/0805.4509_1.jpg", "options": ["A) The ordering temperatures for all materials are above the normalized temperature \$ T/T_c \$ of 1.2.", "B) The magnetic ordering temperatures decrease for Dy, Tb, and Ho as the normalized temperature \$ T/T_c \$ approaches 1.", "C) The magnetic ordering temperatures for all materials are the same across the normalized temperature \$ T/T_c \$.", "D) The magnetic ordering temperature is highest for Yttrium (Y) and decreases for Dy, Tb, and Ho."], "question": "What can be inferred about the magnetic ordering temperatures of the materials tested as shown in the graph?", "label": "B", "rationale": "The graph shows a sharp decline in frequency as the normalized temperature \$ T/T_c \$ approaches 1 for Dy, Tb, and Ho, indicating that their magnetic ordering temperatures decrease. No such data is shown for Yttrium (Y), thus we can't infer it has the highest magnetic ordering temperature." } ``` - Download the `arxivqa.json` and `images.tgz` to your machine. - Decompress images: `tar -xzvf images.tgz`. - Loading the dataset and process the sample according to your need. ```python3 import json with open("arxivqa.jsonl", 'r') as fr: arxiv_qa = [ json.loads(line.strip()) for line in fr] sample = arxiv_qa[0] print(sample["image"]) # image file ``` ## Dataset details **Dataset type**: ArxivQA is a set of GPT4V-generated VQA samples based on figures from Arxiv Papers. **Papers or resources for more information**: https://mm-arxiv.github.io/ **License**: CC-BY-SA-4.0; and it should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use **Intended use**: Primary intended uses: The primary use of ArxivQA is research on large multimodal models. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo

--- pretty_name: Evaluation run of macadeliccc/laser-dolphin-mixtral-2x7b-dpo dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [macadeliccc/laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo)\ \ 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_macadeliccc__laser-dolphin-mixtral-2x7b-dpo\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-14T01:13:57.359475](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo/blob/main/results_2024-01-14T01-13-57.359475.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\": 0.6323249282667325,\n\ \ \"acc_stderr\": 0.03235123186693868,\n \"acc_norm\": 0.63602882598941,\n\ \ \"acc_norm_stderr\": 0.03299471578731984,\n \"mc1\": 0.4418604651162791,\n\ \ \"mc1_stderr\": 0.01738476747898622,\n \"mc2\": 0.6075861082832835,\n\ \ \"mc2_stderr\": 0.015099206529299735\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6245733788395904,\n \"acc_stderr\": 0.014150631435111728,\n\ \ \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892978\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6661023700458076,\n\ \ \"acc_stderr\": 0.004706398252382464,\n \"acc_norm\": 0.8579964150567616,\n\ \ \"acc_norm_stderr\": 0.0034834044902359936\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\ \ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n\ \ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n\ \ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.502127659574468,\n \"acc_stderr\": 0.03268572658667492,\n\ \ \"acc_norm\": 0.502127659574468,\n \"acc_norm_stderr\": 0.03268572658667492\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\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440679,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440679\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7645161290322581,\n\ \ \"acc_stderr\": 0.02413763242933771,\n \"acc_norm\": 0.7645161290322581,\n\ \ \"acc_norm_stderr\": 0.02413763242933771\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026704,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026704\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121437,\n\ \ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121437\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6307692307692307,\n \"acc_stderr\": 0.024468615241478923,\n\ \ \"acc_norm\": 0.6307692307692307,\n \"acc_norm_stderr\": 0.024468615241478923\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \ \ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8275229357798165,\n \"acc_stderr\": 0.016197807956848033,\n \"\ acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.016197807956848033\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.803921568627451,\n\ \ \"acc_stderr\": 0.027865942286639325,\n \"acc_norm\": 0.803921568627451,\n\ \ \"acc_norm_stderr\": 0.027865942286639325\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n\ \ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\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\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\ : 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\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\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\ \ \"acc_stderr\": 0.013964393769899129,\n \"acc_norm\": 0.8122605363984674,\n\ \ \"acc_norm_stderr\": 0.013964393769899129\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.024027745155265012,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.024027745155265012\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3664804469273743,\n\ \ \"acc_stderr\": 0.016115235504865467,\n \"acc_norm\": 0.3664804469273743,\n\ \ \"acc_norm_stderr\": 0.016115235504865467\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\ \ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\ \ \"acc_stderr\": 0.026236965881153266,\n \"acc_norm\": 0.6913183279742765,\n\ \ \"acc_norm_stderr\": 0.026236965881153266\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44654498044328556,\n\ \ \"acc_stderr\": 0.012697046024399682,\n \"acc_norm\": 0.44654498044328556,\n\ \ \"acc_norm_stderr\": 0.012697046024399682\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6507352941176471,\n \"acc_stderr\": 0.028959755196824876,\n\ \ \"acc_norm\": 0.6507352941176471,\n \"acc_norm_stderr\": 0.028959755196824876\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6568627450980392,\n \"acc_stderr\": 0.019206606848825362,\n \ \ \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.019206606848825362\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\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\": 0.8011695906432749,\n \"acc_stderr\": 0.03061111655743253,\n\ \ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.03061111655743253\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4418604651162791,\n\ \ \"mc1_stderr\": 0.01738476747898622,\n \"mc2\": 0.6075861082832835,\n\ \ \"mc2_stderr\": 0.015099206529299735\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7900552486187845,\n \"acc_stderr\": 0.01144628062926263\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4829416224412434,\n \ \ \"acc_stderr\": 0.013764467123761318\n }\n}\n```" repo_url: https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo 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_14T01_13_57.359475 path: - '**/details_harness|arc:challenge|25_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-14T01-13-57.359475.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|gsm8k|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hellaswag|10_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-14T01-13-57.359475.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-management|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-14T01-13-57.359475.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|truthfulqa:mc|0_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-14T01-13-57.359475.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_14T01_13_57.359475 path: - '**/details_harness|winogrande|5_2024-01-14T01-13-57.359475.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-14T01-13-57.359475.parquet' - config_name: results data_files: - split: 2024_01_14T01_13_57.359475 path: - results_2024-01-14T01-13-57.359475.parquet - split: latest path: - results_2024-01-14T01-13-57.359475.parquet --- # Dataset Card for Evaluation run of macadeliccc/laser-dolphin-mixtral-2x7b-dpo  Dataset automatically created during the evaluation run of model [macadeliccc/laser-dolphin-mixtral-2x7b-dpo](https://huggingface.co/macadeliccc/laser-dolphin-mixtral-2x7b-dpo) 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_macadeliccc__laser-dolphin-mixtral-2x7b-dpo", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-14T01:13:57.359475](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__laser-dolphin-mixtral-2x7b-dpo/blob/main/results_2024-01-14T01-13-57.359475.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": 0.6323249282667325, "acc_stderr": 0.03235123186693868, "acc_norm": 0.63602882598941, "acc_norm_stderr": 0.03299471578731984, "mc1": 0.4418604651162791, "mc1_stderr": 0.01738476747898622, "mc2": 0.6075861082832835, "mc2_stderr": 0.015099206529299735 }, "harness|arc:challenge|25": { "acc": 0.6245733788395904, "acc_stderr": 0.014150631435111728, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.013847460518892978 }, "harness|hellaswag|10": { "acc": 0.6661023700458076, "acc_stderr": 0.004706398252382464, "acc_norm": 0.8579964150567616, "acc_norm_stderr": 0.0034834044902359936 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595852, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595852 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.502127659574468, "acc_stderr": 0.03268572658667492, "acc_norm": 0.502127659574468, "acc_norm_stderr": 0.03268572658667492 }, "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": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440679, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440679 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145633, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7645161290322581, "acc_stderr": 0.02413763242933771, "acc_norm": 0.7645161290322581, "acc_norm_stderr": 0.02413763242933771 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.02886977846026704, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026704 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8808290155440415, "acc_stderr": 0.023381935348121437, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.023381935348121437 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6307692307692307, "acc_stderr": 0.024468615241478923, "acc_norm": 0.6307692307692307, "acc_norm_stderr": 0.024468615241478923 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.28888888888888886, "acc_stderr": 0.027634907264178544, "acc_norm": 0.28888888888888886, "acc_norm_stderr": 0.027634907264178544 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.03038835355188679, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.03038835355188679 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8275229357798165, "acc_stderr": 0.016197807956848033, "acc_norm": 0.8275229357798165, "acc_norm_stderr": 0.016197807956848033 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639325, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639325 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "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": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "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": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "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": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.020930193185179333, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.020930193185179333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8122605363984674, "acc_stderr": 0.013964393769899129, "acc_norm": 0.8122605363984674, "acc_norm_stderr": 0.013964393769899129 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7254335260115607, "acc_stderr": 0.024027745155265012, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.024027745155265012 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3664804469273743, "acc_stderr": 0.016115235504865467, "acc_norm": 0.3664804469273743, "acc_norm_stderr": 0.016115235504865467 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818733, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818733 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6913183279742765, "acc_stderr": 0.026236965881153266, "acc_norm": 0.6913183279742765, "acc_norm_stderr": 0.026236965881153266 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7407407407407407, "acc_stderr": 0.02438366553103545, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.02438366553103545 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.450354609929078, "acc_stderr": 0.029680105565029036, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.029680105565029036 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44654498044328556, "acc_stderr": 0.012697046024399682, "acc_norm": 0.44654498044328556, "acc_norm_stderr": 0.012697046024399682 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6507352941176471, "acc_stderr": 0.028959755196824876, "acc_norm": 0.6507352941176471, "acc_norm_stderr": 0.028959755196824876 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6568627450980392, "acc_stderr": 0.019206606848825362, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.019206606848825362 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.746938775510204, "acc_stderr": 0.027833023871399677, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399677 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "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": 0.8011695906432749, "acc_stderr": 0.03061111655743253, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.03061111655743253 }, "harness|truthfulqa:mc|0": { "mc1": 0.4418604651162791, "mc1_stderr": 0.01738476747898622, "mc2": 0.6075861082832835, "mc2_stderr": 0.015099206529299735 }, "harness|winogrande|5": { "acc": 0.7900552486187845, "acc_stderr": 0.01144628062926263 }, "harness|gsm8k|5": { "acc": 0.4829416224412434, "acc_stderr": 0.013764467123761318 } } ``` ## 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]

Yamei/TVCG_entity_classify

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: id dtype: int64 - name: entity_type dtype: string - name: entity_type_high dtype: string - name: label dtype: class_label: names: '0': method '1': evaluation '2': data '3': background '4': Author '5': DBPedia '6': Affiliation '7': Paper '8': Journal splits: - name: train num_bytes: 3565888.348566884 num_examples: 47281 - name: test num_bytes: 891528.6514331156 num_examples: 11821 download_size: 1857856 dataset_size: 4457417.0 --- # Dataset Card for "TVCG_entity_classify" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tellarin-ai/ntx_llm_inst_hindi

--- license: cc-by-sa-4.0 language: - hi task_categories: - token-classification --- # Dataset Card for NTX v1 in the Aya format - Hindi subset This dataset is a format conversion for the Hindi data from the original NTX into the Aya instruction format and it's released here under the CC-BY-SA 4.0 license. ## Dataset Details For the original NTX dataset, the conversion to the Aya instructions format, or more details, please refer to the full dataset in instruction form (https://huggingface.co/datasets/tellarin-ai/ntx_llm_instructions) or to the paper below. **NOTE: ** Unfortunately, due to a conversion issue with numerical expressions, this version here only includes the temporal expressions part of NTX. ## Citation If you utilize this dataset version, feel free to cite/footnote the complete version at https://huggingface.co/datasets/tellarin-ai/ntx_llm_instructions, but please also cite the *original dataset publication*. **BibTeX:** ``` @preprint{chen2023dataset, title={Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions}, author={Sanxing Chen and Yongqiang Chen and Börje F. Karlsson}, year={2023}, eprint={2303.18103}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

MBZUAI/LaMini-Hallucination

--- dataset_info: features: - name: question dtype: string - name: category dtype: string splits: - name: test num_bytes: 2785 num_examples: 40 download_size: 3220 dataset_size: 2785 --- # Dataset Card for "LaMini-Hallucination" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) # Citation ``` @article{lamini-lm, author = {Minghao Wu and Abdul Waheed and Chiyu Zhang and Muhammad Abdul-Mageed and Alham Fikri Aji }, title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions}, journal = {CoRR}, volume = {abs/2304.14402}, year = {2023}, url = {https://arxiv.org/abs/2304.14402}, eprinttype = {arXiv}, eprint = {2304.14402} } ```

yzhuang/metatree_vehicle_sensIT

--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float32 - name: y dtype: int64 splits: - name: train num_bytes: 28973280 num_examples: 68984 - name: validation num_bytes: 12408480 num_examples: 29544 download_size: 60104700 dataset_size: 41381760 --- # Dataset Card for "metatree_vehicle_sensIT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

richardr1126/spider-context-validation

--- language: - en license: - cc-by-4.0 source_datasets: - spider pretty_name: Spider Context Validation tags: - text-to-sql - SQL - spider - validation - eval - spider-eval dataset_info: features: - name: db_id dtype: string - name: question dtype: string - name: db_info dtype: string - name: ground_truth dtype: string --- # Dataset Card for Spider Context Validation ### Dataset Summary Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases. This dataset was created to validate spider-fine-tuned LLMs with database context. ### Yale Lily Spider Leaderboards The leaderboard can be seen at https://yale-lily.github.io/spider ### Languages The text in the dataset is in English. ### Licensing Information The spider dataset is licensed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) ### Citation ``` @article{yu2018spider, title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task}, author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others}, journal={arXiv preprint arXiv:1809.08887}, year={2018} } ```

AUTOMATIC/jaicards

--- license: mit task_categories: - conversational - text-generation size_categories: - 100K<n<1M --- # janitorai-cards This dataset contains 190k cards that I received from janitorai, from a source that wished to remain anonymous. My addition to this data is conversion of cards to [v2 character card](https://github.com/malfoyslastname/character-card-spec-v2/blob/main/README.md) format, and a local webpage that can be used to explore the dataset. ### Webpage ![](screenshot.png) Ther webpage lets you browse cards, search by text, fitler by tags and order by date/name/popularity. To use the webpage, put [index.html](index.html) into a directory, and download and extract archives into same directory: [0123.zip](0123.zip), [4567.zip](4567.zip), [89ab.zip](89ab.zip), [cdef.zip](cdef.zip), and [html.zip](html.zip). After that, just open [index.html](index.html) in the browser. The directory structure should look like this: ``` 📁 ┣━━ 📄 index.html ┣━━ 📁 cards ┃ ┣━━ 📁 0 ┃ ┣━━ 📁 1 ┃ ┃ ... ┃ ┗━━ 📁 f ┗━━ 📁 html ┣━━ 📄 allcards.js ┣━━ 📄 cards.js ┗━━ 📄 cardsmeta.js ``` For performance reasons, the webpage only loads 10000 most popular cards when you open it. To view all, click the "Load all" button in the top row. Caveat: instead of downloading the card, it opens it in a new page—you have to save it yourself. I can't figure out how to get the download to work. ### Files - [0123.zip](0123.zip), [4567.zip](4567.zip), [89ab.zip](89ab.zip), [cdef.zip](cdef.zip) - archives with v2 character cards, tested to work with SillyTavern. - [cards-js.7z](cards-js.7z) - all v2 character cards in json format, without images, tested to work with SillyTavern. - [index.html](index.html) - webpage for browsing cards. - [html.zip](html.zip) - files with information about cards - it's needed for the webpage to function. - [orig.7z](orig.7z) - original json files with cards from janitorai - not compatible with any software.

Maciel/e-commerce-sample-images

--- license: apache-2.0 ---

camilaslz/LEOFELIPE

--- license: openrail ---

DataLinguistic/MutiDataset

--- license: apache-2.0 ---

autoevaluate/autoeval-eval-project-jnlpba-c103d433-1295449602

--- type: predictions tags: - autotrain - evaluation datasets: - jnlpba eval_info: task: entity_extraction model: siddharthtumre/biobert-ner metrics: [] dataset_name: jnlpba dataset_config: jnlpba dataset_split: validation col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: siddharthtumre/biobert-ner * Dataset: jnlpba * Config: jnlpba * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@siddharthtumre](https://huggingface.co/siddharthtumre) for evaluating this model.

heliosprime/twitter_dataset_1713189721

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 15533 num_examples: 42 download_size: 16373 dataset_size: 15533 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713189721" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_mnli_existential_got

--- 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: 183289 num_examples: 843 - name: dev_mismatched num_bytes: 163475 num_examples: 689 - name: test_matched num_bytes: 182488 num_examples: 842 - name: test_mismatched num_bytes: 155896 num_examples: 712 - name: train num_bytes: 7382865 num_examples: 33251 download_size: 5028376 dataset_size: 8068013 --- # Dataset Card for "MULTI_VALUE_mnli_existential_got" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_chlee10__T3Q-Platypus-SOLAR

--- pretty_name: Evaluation run of chlee10/T3Q-Platypus-SOLAR dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chlee10/T3Q-Platypus-SOLAR](https://huggingface.co/chlee10/T3Q-Platypus-SOLAR)\ \ 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_chlee10__T3Q-Platypus-SOLAR\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-12T05:48:31.143734](https://huggingface.co/datasets/open-llm-leaderboard/details_chlee10__T3Q-Platypus-SOLAR/blob/main/results_2024-03-12T05-48-31.143734.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\": 0.5426000137755855,\n\ \ \"acc_stderr\": 0.03377258305011606,\n \"acc_norm\": 0.543700672611483,\n\ \ \"acc_norm_stderr\": 0.034480897315487986,\n \"mc1\": 0.35495716034271724,\n\ \ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5066765475632212,\n\ \ \"mc2_stderr\": 0.01508715500679457\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520769,\n\ \ \"acc_norm\": 0.6186006825938567,\n \"acc_norm_stderr\": 0.014194389086685247\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6440948018323043,\n\ \ \"acc_stderr\": 0.004778081784542405,\n \"acc_norm\": 0.8417645887273452,\n\ \ \"acc_norm_stderr\": 0.0036421571661623495\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n\ \ \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5622641509433962,\n \"acc_stderr\": 0.030533338430467523,\n\ \ \"acc_norm\": 0.5622641509433962,\n \"acc_norm_stderr\": 0.030533338430467523\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n\ \ \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n\ \ \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5375722543352601,\n\ \ \"acc_stderr\": 0.0380168510452446,\n \"acc_norm\": 0.5375722543352601,\n\ \ \"acc_norm_stderr\": 0.0380168510452446\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808779,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808779\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4851063829787234,\n \"acc_stderr\": 0.032671518489247764,\n\ \ \"acc_norm\": 0.4851063829787234,\n \"acc_norm_stderr\": 0.032671518489247764\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\ \ \"acc_stderr\": 0.0433913832257986,\n \"acc_norm\": 0.30701754385964913,\n\ \ \"acc_norm_stderr\": 0.0433913832257986\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3915343915343915,\n \"acc_stderr\": 0.025138091388851105,\n \"\ acc_norm\": 0.3915343915343915,\n \"acc_norm_stderr\": 0.025138091388851105\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2619047619047619,\n\ \ \"acc_stderr\": 0.03932537680392869,\n \"acc_norm\": 0.2619047619047619,\n\ \ \"acc_norm_stderr\": 0.03932537680392869\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.6290322580645161,\n \"acc_stderr\": 0.02748054188795359,\n \"\ acc_norm\": 0.6290322580645161,\n \"acc_norm_stderr\": 0.02748054188795359\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969566,\n \"\ acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969566\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\ \ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7171717171717171,\n \"acc_stderr\": 0.03208779558786752,\n \"\ acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.03208779558786752\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7616580310880829,\n \"acc_stderr\": 0.030748905363909895,\n\ \ \"acc_norm\": 0.7616580310880829,\n \"acc_norm_stderr\": 0.030748905363909895\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.47692307692307695,\n \"acc_stderr\": 0.025323990861736118,\n\ \ \"acc_norm\": 0.47692307692307695,\n \"acc_norm_stderr\": 0.025323990861736118\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340492,\n \ \ \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340492\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.49159663865546216,\n \"acc_stderr\": 0.03247390276569669,\n\ \ \"acc_norm\": 0.49159663865546216,\n \"acc_norm_stderr\": 0.03247390276569669\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6972477064220184,\n \"acc_stderr\": 0.019698711434756343,\n \"\ acc_norm\": 0.6972477064220184,\n \"acc_norm_stderr\": 0.019698711434756343\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.3101851851851852,\n \"acc_stderr\": 0.03154696285656629,\n \"\ acc_norm\": 0.3101851851851852,\n \"acc_norm_stderr\": 0.03154696285656629\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7254901960784313,\n \"acc_stderr\": 0.03132179803083291,\n \"\ acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.03132179803083291\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159287,\n \ \ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159287\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6457399103139013,\n\ \ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n\ \ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.043285772152629715,\n\ \ \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.043285772152629715\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7272727272727273,\n \"acc_stderr\": 0.04065578140908705,\n \"\ acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04065578140908705\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04557239513497751,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04557239513497751\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5644171779141104,\n \"acc_stderr\": 0.038956324641389366,\n\ \ \"acc_norm\": 0.5644171779141104,\n \"acc_norm_stderr\": 0.038956324641389366\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\ \ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\ \ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7991452991452992,\n\ \ \"acc_stderr\": 0.026246772946890488,\n \"acc_norm\": 0.7991452991452992,\n\ \ \"acc_norm_stderr\": 0.026246772946890488\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7662835249042146,\n\ \ \"acc_stderr\": 0.015133383278988836,\n \"acc_norm\": 0.7662835249042146,\n\ \ \"acc_norm_stderr\": 0.015133383278988836\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6271676300578035,\n \"acc_stderr\": 0.02603389061357629,\n\ \ \"acc_norm\": 0.6271676300578035,\n \"acc_norm_stderr\": 0.02603389061357629\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2547486033519553,\n\ \ \"acc_stderr\": 0.014572650383409153,\n \"acc_norm\": 0.2547486033519553,\n\ \ \"acc_norm_stderr\": 0.014572650383409153\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.02849199358617156,\n\ \ \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.02849199358617156\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6109324758842444,\n\ \ \"acc_stderr\": 0.02769033753648537,\n \"acc_norm\": 0.6109324758842444,\n\ \ \"acc_norm_stderr\": 0.02769033753648537\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6820987654320988,\n \"acc_stderr\": 0.02591006352824088,\n\ \ \"acc_norm\": 0.6820987654320988,\n \"acc_norm_stderr\": 0.02591006352824088\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4148936170212766,\n \"acc_stderr\": 0.029392236584612503,\n \ \ \"acc_norm\": 0.4148936170212766,\n \"acc_norm_stderr\": 0.029392236584612503\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3956975228161669,\n\ \ \"acc_stderr\": 0.012489290735449018,\n \"acc_norm\": 0.3956975228161669,\n\ \ \"acc_norm_stderr\": 0.012489290735449018\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.43014705882352944,\n \"acc_stderr\": 0.030074971917302875,\n\ \ \"acc_norm\": 0.43014705882352944,\n \"acc_norm_stderr\": 0.030074971917302875\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5588235294117647,\n \"acc_stderr\": 0.020087362076702857,\n \ \ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.020087362076702857\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.04494290866252089,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.04494290866252089\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5102040816326531,\n \"acc_stderr\": 0.03200255347893783,\n\ \ \"acc_norm\": 0.5102040816326531,\n \"acc_norm_stderr\": 0.03200255347893783\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6616915422885572,\n\ \ \"acc_stderr\": 0.03345563070339193,\n \"acc_norm\": 0.6616915422885572,\n\ \ \"acc_norm_stderr\": 0.03345563070339193\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.04605661864718381,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4397590361445783,\n\ \ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\ \ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.032467217651178264,\n\ \ \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.032467217651178264\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35495716034271724,\n\ \ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5066765475632212,\n\ \ \"mc2_stderr\": 0.01508715500679457\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.823993685872139,\n \"acc_stderr\": 0.010703090882320705\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.43745261561789234,\n \ \ \"acc_stderr\": 0.013664299060751915\n }\n}\n```" repo_url: https://huggingface.co/chlee10/T3Q-Platypus-SOLAR leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|arc:challenge|25_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-12T05-48-31.143734.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|gsm8k|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hellaswag|10_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-12T05-48-31.143734.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-management|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-12T05-48-31.143734.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|truthfulqa:mc|0_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-12T05-48-31.143734.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_12T05_48_31.143734 path: - '**/details_harness|winogrande|5_2024-03-12T05-48-31.143734.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-12T05-48-31.143734.parquet' - config_name: results data_files: - split: 2024_03_12T05_48_31.143734 path: - results_2024-03-12T05-48-31.143734.parquet - split: latest path: - results_2024-03-12T05-48-31.143734.parquet --- # Dataset Card for Evaluation run of chlee10/T3Q-Platypus-SOLAR  Dataset automatically created during the evaluation run of model [chlee10/T3Q-Platypus-SOLAR](https://huggingface.co/chlee10/T3Q-Platypus-SOLAR) 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_chlee10__T3Q-Platypus-SOLAR", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-12T05:48:31.143734](https://huggingface.co/datasets/open-llm-leaderboard/details_chlee10__T3Q-Platypus-SOLAR/blob/main/results_2024-03-12T05-48-31.143734.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": 0.5426000137755855, "acc_stderr": 0.03377258305011606, "acc_norm": 0.543700672611483, "acc_norm_stderr": 0.034480897315487986, "mc1": 0.35495716034271724, "mc1_stderr": 0.0167508623813759, "mc2": 0.5066765475632212, "mc2_stderr": 0.01508715500679457 }, "harness|arc:challenge|25": { "acc": 0.5750853242320819, "acc_stderr": 0.014445698968520769, "acc_norm": 0.6186006825938567, "acc_norm_stderr": 0.014194389086685247 }, "harness|hellaswag|10": { "acc": 0.6440948018323043, "acc_stderr": 0.004778081784542405, "acc_norm": 0.8417645887273452, "acc_norm_stderr": 0.0036421571661623495 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5622641509433962, "acc_stderr": 0.030533338430467523, "acc_norm": 0.5622641509433962, "acc_norm_stderr": 0.030533338430467523 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04076663253918567, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04076663253918567 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5375722543352601, "acc_stderr": 0.0380168510452446, "acc_norm": 0.5375722543352601, "acc_norm_stderr": 0.0380168510452446 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808779, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808779 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4851063829787234, "acc_stderr": 0.032671518489247764, "acc_norm": 0.4851063829787234, "acc_norm_stderr": 0.032671518489247764 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.30701754385964913, "acc_stderr": 0.0433913832257986, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.0433913832257986 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3915343915343915, "acc_stderr": 0.025138091388851105, "acc_norm": 0.3915343915343915, "acc_norm_stderr": 0.025138091388851105 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2619047619047619, "acc_stderr": 0.03932537680392869, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.03932537680392869 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6290322580645161, "acc_stderr": 0.02748054188795359, "acc_norm": 0.6290322580645161, "acc_norm_stderr": 0.02748054188795359 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3497536945812808, "acc_stderr": 0.03355400904969566, "acc_norm": 0.3497536945812808, "acc_norm_stderr": 0.03355400904969566 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7212121212121212, "acc_stderr": 0.03501438706296781, "acc_norm": 0.7212121212121212, "acc_norm_stderr": 0.03501438706296781 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7171717171717171, "acc_stderr": 0.03208779558786752, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.03208779558786752 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7616580310880829, "acc_stderr": 0.030748905363909895, "acc_norm": 0.7616580310880829, "acc_norm_stderr": 0.030748905363909895 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.47692307692307695, "acc_stderr": 0.025323990861736118, "acc_norm": 0.47692307692307695, "acc_norm_stderr": 0.025323990861736118 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2851851851851852, "acc_stderr": 0.027528599210340492, "acc_norm": 0.2851851851851852, "acc_norm_stderr": 0.027528599210340492 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.49159663865546216, "acc_stderr": 0.03247390276569669, "acc_norm": 0.49159663865546216, "acc_norm_stderr": 0.03247390276569669 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6972477064220184, "acc_stderr": 0.019698711434756343, "acc_norm": 0.6972477064220184, "acc_norm_stderr": 0.019698711434756343 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.3101851851851852, "acc_stderr": 0.03154696285656629, "acc_norm": 0.3101851851851852, "acc_norm_stderr": 0.03154696285656629 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7254901960784313, "acc_stderr": 0.03132179803083291, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.03132179803083291 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7637130801687764, "acc_stderr": 0.027652153144159287, "acc_norm": 0.7637130801687764, "acc_norm_stderr": 0.027652153144159287 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6457399103139013, "acc_stderr": 0.032100621541349864, "acc_norm": 0.6457399103139013, "acc_norm_stderr": 0.032100621541349864 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5801526717557252, "acc_stderr": 0.043285772152629715, "acc_norm": 0.5801526717557252, "acc_norm_stderr": 0.043285772152629715 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7272727272727273, "acc_stderr": 0.04065578140908705, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.04065578140908705 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04557239513497751, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04557239513497751 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5644171779141104, "acc_stderr": 0.038956324641389366, "acc_norm": 0.5644171779141104, "acc_norm_stderr": 0.038956324641389366 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04547960999764376, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04547960999764376 }, "harness|hendrycksTest-management|5": { "acc": 0.6699029126213593, "acc_stderr": 0.0465614711001235, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.0465614711001235 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7991452991452992, "acc_stderr": 0.026246772946890488, "acc_norm": 0.7991452991452992, "acc_norm_stderr": 0.026246772946890488 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7662835249042146, "acc_stderr": 0.015133383278988836, "acc_norm": 0.7662835249042146, "acc_norm_stderr": 0.015133383278988836 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6271676300578035, "acc_stderr": 0.02603389061357629, "acc_norm": 0.6271676300578035, "acc_norm_stderr": 0.02603389061357629 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2547486033519553, "acc_stderr": 0.014572650383409153, "acc_norm": 0.2547486033519553, "acc_norm_stderr": 0.014572650383409153 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5490196078431373, "acc_stderr": 0.02849199358617156, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.02849199358617156 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6109324758842444, "acc_stderr": 0.02769033753648537, "acc_norm": 0.6109324758842444, "acc_norm_stderr": 0.02769033753648537 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6820987654320988, "acc_stderr": 0.02591006352824088, "acc_norm": 0.6820987654320988, "acc_norm_stderr": 0.02591006352824088 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4148936170212766, "acc_stderr": 0.029392236584612503, "acc_norm": 0.4148936170212766, "acc_norm_stderr": 0.029392236584612503 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3956975228161669, "acc_stderr": 0.012489290735449018, "acc_norm": 0.3956975228161669, "acc_norm_stderr": 0.012489290735449018 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.43014705882352944, "acc_stderr": 0.030074971917302875, "acc_norm": 0.43014705882352944, "acc_norm_stderr": 0.030074971917302875 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5588235294117647, "acc_stderr": 0.020087362076702857, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.020087362076702857 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.04494290866252089, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.04494290866252089 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5102040816326531, "acc_stderr": 0.03200255347893783, "acc_norm": 0.5102040816326531, "acc_norm_stderr": 0.03200255347893783 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6616915422885572, "acc_stderr": 0.03345563070339193, "acc_norm": 0.6616915422885572, "acc_norm_stderr": 0.03345563070339193 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.7, "acc_stderr": 0.04605661864718381, "acc_norm": 0.7, "acc_norm_stderr": 0.04605661864718381 }, "harness|hendrycksTest-virology|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7660818713450293, "acc_stderr": 0.032467217651178264, "acc_norm": 0.7660818713450293, "acc_norm_stderr": 0.032467217651178264 }, "harness|truthfulqa:mc|0": { "mc1": 0.35495716034271724, "mc1_stderr": 0.0167508623813759, "mc2": 0.5066765475632212, "mc2_stderr": 0.01508715500679457 }, "harness|winogrande|5": { "acc": 0.823993685872139, "acc_stderr": 0.010703090882320705 }, "harness|gsm8k|5": { "acc": 0.43745261561789234, "acc_stderr": 0.013664299060751915 } } ``` ## 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]

mattymchen/refinedweb-3m

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7834920949 num_examples: 3000000 download_size: 4904877808 dataset_size: 7834920949 --- # Dataset Card for "refinedweb-3m" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

QizhiPei/BioT5_finetune_dataset

--- license: mit language: - en --- ## References For more information, please refer to our paper and GitHub repository. Paper: [BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations](https://arxiv.org/abs/2310.07276) GitHub: [BioT5](https://github.com/QizhiPei/BioT5) Authors: *Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan*

davanstrien/testpapercomments-ds

--- dataset_info: features: - name: paper_url dtype: string - name: comment dtype: string splits: - name: train num_bytes: 502672 num_examples: 456 download_size: 0 dataset_size: 502672 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "testpapercomments-ds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

orgcatorg/stripes

--- configs: - config_name: Africa data_files: - split: train path: Africa/train-* - config_name: Asia-Pacific data_files: - split: train path: Asia-Pacific/train-* - config_name: Europe data_files: - split: train path: Europe/train-* - config_name: Middle East data_files: - split: train path: Middle East/train-* - config_name: US data_files: - split: train path: US/train-* dataset_info: - config_name: Africa features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 873549 num_examples: 175 download_size: 530795 dataset_size: 873549 - config_name: Asia-Pacific features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 2597100 num_examples: 596 download_size: 1526683 dataset_size: 2597100 - config_name: Europe features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 6333893 num_examples: 1241 download_size: 3748163 dataset_size: 6333893 - config_name: Middle East features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 6203258 num_examples: 958 download_size: 3539626 dataset_size: 6203258 - config_name: US features: - name: content dtype: string - name: title dtype: string - name: source_link dtype: string - name: description dtype: string - name: date dtype: string - name: image dtype: string - name: image_caption dtype: string - name: category dtype: string splits: - name: train num_bytes: 6964806 num_examples: 1220 download_size: 4135894 dataset_size: 6964806 --- # Dataset Card for "stripes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yzhuang/autotree_automl_10000_eye_movements_sgosdt_l256_dim10_d3_sd0

--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: input_y_clean sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 236440000 num_examples: 10000 - name: validation num_bytes: 236440000 num_examples: 10000 download_size: 155715478 dataset_size: 472880000 --- # Dataset Card for "autotree_automl_10000_eye_movements_sgosdt_l256_dim10_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

heliosprime/twitter_dataset_1713038771

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 11220 num_examples: 25 download_size: 8392 dataset_size: 11220 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713038771" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

communityai/Open-Orca___1million-gpt-4-200k

--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 371133122.45702064 num_examples: 200000 download_size: 196792432 dataset_size: 371133122.45702064 configs: - config_name: default data_files: - split: train path: data/train-* ---

tacoz/audCatImages

--- license: openrail ---

vekkt/french_CEFR

--- license: mit ---

mask-distilled-onesec-cv12-each-chunk-uniq/chunk_198

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1071458640.0 num_examples: 210420 download_size: 1093799592 dataset_size: 1071458640.0 --- # Dataset Card for "chunk_198" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

5mN/Sum-assistant-v1

--- task_categories: - question-answering language: - en - hr pretty_name: Univeristy of Mostar Assistant ---

one-sec-cv12/chunk_160

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 22251248064.5 num_examples: 231668 download_size: 20138689166 dataset_size: 22251248064.5 --- # Dataset Card for "chunk_160" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Luizagrod23/RyotaSakuraba

--- license: openrail ---

ddonuts/recurrent-events

--- license: other ---

SauravMaheshkar/NDC-substances-25

--- license: unknown task_categories: - graph-ml tags: - chemistry configs: - config_name: transductive data_files: - split: train path: "processed/transductive/train_df.csv" - split: valid path: "processed/transductive/val_df.csv" - split: test path: "processed/transductive/test_df.csv" - config_name: inductive data_files: - split: train path: "processed/inductive/train_df.csv" - split: valid path: "processed/inductive/val_df.csv" - split: test path: "processed/inductive/test_df.csv" - config_name: raw data_files: "raw/*.txt" --- Source Paper: https://arxiv.org/abs/1802.06916 ### Usage ``` from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset dataset = CornellTemporalHyperGraphDataset(root = "./", name="NDC-substances-25", split="train") ``` ### Citation ```misc @article{Benson-2018-simplicial, author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon}, title = {Simplicial closure and higher-order link prediction}, year = {2018}, doi = {10.1073/pnas.1800683115}, publisher = {National Academy of Sciences}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences} } ```

Ve11ichor/Song_SA_np_input

--- license: apache-2.0 task_categories: - text-classification language: - zh size_categories: - 1K<n<10K ---

aisc-team-d1/PMC_Data

--- configs: - config_name: default data_files: - split: train path: train-* - split: test path: test-* dataset_info: features: - name: PMC_id dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: inline dtype: string - name: img_ref dtype: string splits: - name: train num_bytes: 634222272 num_examples: 316838 - name: test num_bytes: 253538916 num_examples: 120836 download_size: 139781550 dataset_size: 887761188 --- # PMC-CaseReport Dataset - [PMC-CaseReport_original Dataset](#pmc-casereport-dataset) - [Daraset Structure](#dataset-structure) - [Sample](#sample) This is the text parts and the figure parts can be dowloaded from https://pan.baidu.com/s/1Src_rhXsaOFp8zJ_3zMFsQ?pwd=p3ne. ## Dataset Structure **PMC-CaseReport** (Filtered version: 317K VQA pairs for taining and of 121K for testing images). The dataset can be loading following huggingface datasets rule: ``` from datasets import load_dataset dataset = load_dataset("chaoyi-wu/PMC-CaseReport_original") ``` - ## Sample A case in dataset is shown bellow, | PMC_id | PMC9052276 | | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | context | We report the case of a 73-year-old female who presented to the ER with left-sided body weakness of unclear duration.She had an ischemic stroke four years prior with no residual neurologic deficits, a myocardial infarction requiring coronary artery bypass grafting (CABG) two years prior, hypertension, and dementia. Her vital signs were blood pressure (BP) 117/78 mmHg, pulse 121 beats per minute, temperature 98.9 F, respiratory rate (RR) 18 cycles/minute, and oxygen saturation (SpO2) of 97% on ambient air.She was disoriented to place and time with a Glasgow Coma Score (GCS) of 14 (E4V4M6).Her speech was slurred, cranial nerves (CN) 2-12 were grossly intact, motor strength on the left upper and lower extremities was 0/5 and on the right upper and lower extremities was 4/5, and the sensation was preserved in all extremities.The patient had a National Institutes of Health Stroke Scale (NIHSS) score of 16 and a Modified Rankin Score (mRS) of 5 points.A non-contrast head CT scan revealed evidence of old lacuna infarcts in the basal ganglia and thalamus.No intracranial hemorrhage or acute infarct was found.CT perfusion was not done as our center lacks the resources needed to perform that. | | inline | A brain MRI scan showed an acute pontine stroke (Figures and old infarcts | | question | What did the brain MRI scan reveal? | | answer | The brain MRI scan showed an acute pontine stroke and old infarcts. | | img_ref | "['FIG1', 'FIG3', 'FIG4']" | | | Explanation to each key - PMC_id: corresponding PMC paper id. - context: the context in case report before discussing about the image. - inline: the inline sentence in original paper for referring and should not be input into network - question: the genrated question. - answer: the correct answer. - img_ref: the list for related img id. You can get the image form our PMC figure parts, and fig is named unified as ```PMCxxxxxxx_figid.jpg``` like ```PMC9052276_FIG1.jpg``` Note that, we have not filter the context strictly. Thus, in few cases the answer may be leaked in context. Besides, our PMC figures are collected before this datasets, and during the time window, some papers have been updated. Thus some figures may be missed in our figure base.

AayushShah/SQL_Merged_IDs_and_Text

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: NATURAL_LANG dtype: string - name: SCHEMA dtype: string - name: SQL dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 1089459820.9581463 num_examples: 270986 - name: test num_bytes: 121052878.04185376 num_examples: 30110 download_size: 101851785 dataset_size: 1210512699.0 --- # Dataset Card for "SQL_Merged_IDs_and_Text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

voidful/wikihow_chat

--- dataset_info: features: - name: article_id dtype: int64 - name: question dtype: string - name: answer dtype: string - name: related_document_urls_wayback_snapshots sequence: string - name: split dtype: int64 - name: cluster dtype: int64 - name: dialog dtype: string splits: - name: train num_bytes: 19620137 num_examples: 8235 - name: test num_bytes: 5507274 num_examples: 2333 - name: validation num_bytes: 2810866 num_examples: 1178 download_size: 14161836 dataset_size: 27938277 --- # Dataset Card for "wikihow_chat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Azie88/COVID_Vaccine_Tweet_sentiment_analysis_roberta

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels dtype: int64 splits: - name: train num_bytes: 1827789 num_examples: 7999 - name: eval num_bytes: 527000 num_examples: 2000 download_size: 569069 dataset_size: 2354789 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---

nanyy1025/pubmed_rct_20k

--- license: openrail ---

multimodalart/latent-majesty-diffusion-settings

--- license: mit --- A collection of default settings for the text-to-image model [Latent Majesty Diffusion](https://colab.research.google.com/github/multimodalart/majesty-diffusion/blob/main/latent.ipynb). If you love your settings, please add yours by going to the `Files and versions` tab and hitting upload. ![How to upload](https://i.imgur.com/5Exa76X.png) Also please add a description on what your settings excel (it's okay if they are general purpose too) ![How to describe](https://i.imgur.com/zPY2xfm.png)

GBaker/MedQA-USMLE-4-options-hf-cosine-similarity

--- license: cc-by-sa-4.0 --- Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large) This version is augmented with context retrieved from the textbooks provided with the original dataset using cosine similarity. <h4>Citation information:</h4> @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} }

Prajapat/banking_conversation_falcon

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 221593 num_examples: 800 download_size: 106357 dataset_size: 221593 configs: - config_name: default data_files: - split: train path: data/train-* ---

jlmarrugom/gin-img-datasets

--- license: apache-2.0 ---

RyokoAI/Sensei

--- license: cc0-1.0 ---

LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_4_500

--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 1792 num_examples: 63 download_size: 0 dataset_size: 1792 --- # Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_4_500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Svenni551/toxic-full-uncensored-v3.1

--- dataset_info: features: - name: prompt dtype: string - name: output dtype: string splits: - name: train num_bytes: 42273 num_examples: 43 download_size: 28694 dataset_size: 42273 configs: - config_name: default data_files: - split: train path: data/train-* ---

mHossain/final_train_v4_test_1040000

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: input_text dtype: string - name: target_text dtype: string - name: prefix dtype: string splits: - name: train num_bytes: 7345201.5 num_examples: 18000 - name: test num_bytes: 816133.5 num_examples: 2000 download_size: 3516028 dataset_size: 8161335.0 --- # Dataset Card for "final_train_v4_test_1040000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

wardenga/lsoie

--- annotations_creators: - machine-generated language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - unknown source_datasets: - extended|qa_srl task_categories: - text-retrieval task_ids: [] pretty_name: LSOIE tags: - Open Information Extraction --- # Dataset Card for LSOIE ## 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://github.com/Jacobsolawetz/large-scale-oie - **Repository:** https://github.com/Jacobsolawetz/large-scale-oie - **Paper:** https://arxiv.org/abs/2101.11177 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary The Large Scale Open Information Extraction Dataset (LSOIE), is a dataset 20 times larger than the next largest human-annotated Open Information Extraction (OIE) dataset. LSOIE is a built upon the QA-SRL 2.0 dataset by transforming the list of Questions and answers for each predicate to a tuple representing a fact. ### Supported Tasks and Leaderboards Open Information Extraction ### Languages The text in this dataset is english. ## Dataset Structure ### Data Instances A datapoint comprises one fact together with the sentence it was extracted from. There can be multiple facts for each Sentence. Each fact is represented by a tuple $(a_0, p, a_1,\dots a_n)$ where $a_0$ is the head entity $p$ is the predicate and $a_1, \dots,a_n$ represent the tail. ### Data Fields - word_ids : sequence of indices (int) representing tokens in a sentence, - words : a sequence of strings, the tokens in the sentence, - pred : the predicate of the fact, - pred_ids : ids of the tokens in the predicate, - head_pred_id : id of the head token in the predicate, - sent_id : sentence id, - run_id : , - label : Sequence of tags (BIO) representing the fact, e.g. if the fact is given by $(a_0, p, a_1, \dots, a_n) $ ### Data Splits [Needs More Information] ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information [Needs More Information]

hamedhf/nlp_twitter_analysis

--- license: mit task_categories: - text-classification language: - fa - en ---

zyy111/aihuihua

--- license: openrail ---

vishal-burman/moe_misspellings

--- dataset_info: features: - name: correct_word dtype: string - name: incorrect_words sequence: string splits: - name: train num_bytes: 278157870 num_examples: 2720843 download_size: 163693499 dataset_size: 278157870 --- # Dataset Card for "moe_misspellings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

harouzie/vi_question_generation

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: context dtype: string - name: question dtype: string - name: answers dtype: string - name: id dtype: string splits: - name: train num_bytes: 211814961.2307449 num_examples: 174499 - name: test num_bytes: 26477628.80776531 num_examples: 21813 - name: valid num_bytes: 26476414.961489797 num_examples: 21812 download_size: 142790671 dataset_size: 264769005 task_categories: - question-answering - text2text-generation language: - vi pretty_name: Vietnamese Dataset for Extractive Question Answering and Question Generation size_categories: - 100K<n<1M ---

Denviny/LORA

--- license: other ---

recastai/databricks-dolly-15k-chatml

--- language: - en dataset_info: features: - name: instruction dtype: string - name: context dtype: string - name: response dtype: string - name: category dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 34692013 num_examples: 15011 download_size: 15166632 dataset_size: 34692013 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering - text2text-generation --- # Dataset Card for "databricks-dolly-15k-chatml" ## Dataset Summary This dataset has been created by **Re:cast AI** to transform the existing dataset [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) into a [chatml](https://huggingface.co/docs/transformers/main/en/chat_templating) friendly format for use in SFT tasks with pretrained models. ## Dataset Structure ```python messages = [ { "content": "You are an expert Q&A system that is trusted around the world. You always... etc.", "role": "system" }, { "content": "(Optional) Context information is below.\n----------------\nVirgin Australia, the... etc.", "role": "user" }, { "content": "Virgin Australia commenced services on 31 August 2000... etc.", "role": "assistant" } ] ] ``` ## Usage ```python from datasets import load_dataset dataset = load_dataset("recastai/databricks-dolly-15k-chatml", split="train") ``` ## Processing applied to original dataset ```python INSTRUCTIONS = """You are an expert Q&A system that is trusted around the world. You always answer the user's query in a helpful and friendly way. Some rules you always follow: 1. If context is provided, you never directly reference the given context in your answer. 2. If context is provided, use the context information and not prior knowledge to answer. 3. Avoid statements like 'Based on the context, ...' or 'The context information ...' or 'The answer to the user's query...' or anything along those lines. 4. If no context is provided use your internal knowledge to answer.""" # databricks-dolly-15k features: # - instruction: The user query/question # - context: (optional) context to use to help the assistant # - response: The assistant's response to the query/question # key_mapping = dict( query = "instruction", context = "context", response = "response" ) def process_chatml_fn(example, validation=False): """ Processing specific to databricks-dolly-15k into a chat format. """ user_content = ( "(Optional) Context information is below.\n" "----------------\n" "{context}\n" "----------------\n" "Answer the following query.\n" "{query}\n" ) assistant_content = "{response}" message = [ {"role": "system", "content": INSTRUCTIONS}, {"role": "user", "content": user_content.format(context=example[key_mapping['context']], query=example[key_mapping['query']])}, {"role": "assistant", "content": assistant_content.format(response=example[key_mapping['response']])} ] return message ```

MLNTeam-Unical/NFT-70M_transactions

--- dataset_info: features: - name: num_sales dtype: int64 - name: fees_seller dtype: float64 - name: fees_opensea dtype: float64 - name: fees_seller_usd dtype: float64 - name: fees_opensea_usd dtype: float64 - name: tx_timestamp dtype: string - name: price dtype: float64 - name: gain dtype: float64 - name: usd_price dtype: float64 - name: usd_gain dtype: float64 - name: token dtype: string - name: to_eth dtype: float64 - name: to_usd dtype: float64 - name: created_date dtype: string - name: chain dtype: string - name: token_type dtype: string - name: asset_contract_type dtype: string - name: asset_type dtype: string - name: payout_collection_address dtype: int64 - name: from_account dtype: int64 - name: to_account dtype: int64 - name: seller_account dtype: int64 - name: winner_account dtype: int64 - name: contract_address dtype: int64 - name: nft_image dtype: int64 - name: collection_image dtype: int64 - name: token_id dtype: int64 - name: nft_name dtype: int64 - name: nft_description dtype: int64 - name: collection_name dtype: int64 - name: collection_description dtype: int64 splits: - name: train num_bytes: 21291348001 num_examples: 70972143 download_size: 6633664673 dataset_size: 21291348001 size_categories: - 10M<n<100M license: cc-by-nc-4.0 task_categories: - time-series-forecasting - text-classification - feature-extraction - text-generation - zero-shot-classification - text2text-generation - sentence-similarity - image-classification - image-to-text - text-to-image - text-retrieval language: - en tags: - Non-fungible Tokens - Crypto - Web3 - Art - Multimodal Learning pretty_name: NFT-70M_transactions --- # Dataset Card for "NFT-70M_transactions" ## Dataset summary The *NFT-70M_transactions* dataset is the largest and most up-to-date collection of Non-Fungible Tokens (NFT) transactions between 2021 and 2023 sourced from [OpenSea](https://opensea.io), the leading trading platform in the Web3 ecosystem. With more than 70M transactions enriched with metadata, this dataset is conceived to support a wide range of tasks, ranging from sequential and transactional data processing/analysis to graph-based modeling of the complex relationships between traders. Besides, the availability of textual and image contents further amplifies the modeling capabilities and usage opportunities of this dataset, making it a unique and comprehensive multimodal source of information for delving into the NFT landscape. This dataset can serve as a benchmark for various innovative and impactful tasks within the crypto landscape, such as projecting NFT prices or detecting fraudolent and wash trading activities. Furthermore, the multimodal nature of the dataset fosters the development of classification models, as well as textual and visual generative models. ## Data anonymization We point out that the collected NFT transactions and metadata from OpenSea are publicly distributed on blockchain. For our purposes of re-distribution, we are also committed to ensure non-disclosure of information that might lead to identifying the NFT creators, in order to be compliant with privacy-preserving requirements and to avoid violation of data protection regulations and of property rights. In this respect, we carried out three actions: - Values of all variables describing non-sensitive information were kept in their original form; - Values of all variables describing sensitive information were anonymized, in a one-way, non-revertible mode; - URLs of image data and textual contents (i.e., NFT images and their descriptions) were replaced by identifiers to numerical vectors that represent an encrypted representation (i.e., embeddings) of the image/text contents obtained via neural network models. Such embeddings are eventually provided in place of their original image and text data, and can be found in the [**NFT-70M_image**](https://huggingface.co/datasets/MLNTeam-Unical/NFT-70M_image) and [**NFT-70M_text**](https://huggingface.co/datasets/MLNTeam-Unical/NFT-70M_text) supplementary datasets, respectively. ## Data Fields | Variable | Type | Description | Processing | Notes | |--------------------------|-------------|-----------------------------------------------------------------------------------------------------------|------------------|-----------------------------------| | token_id | String | The id of the NFT — this value is unique within the same collection | Anonymized | Original values were replaced by hash-codes | | num_sales | Integer | A progressive integer indicating the number of successful transactions involving the NFT up to the current timestamp (cf. *tx_timestamp*) | Original | Not sensitive variable | | nft_name | Vector ID | The name of the NFT | Anonymized | Original values were encrypted via neural textual embedding | | nft_description | Vector ID | The description of the NFT as provided by the creator | Anonymized | Original values were encrypted via neural textual embedding | | nft_image | Vector ID | The ID for accessing the NFT image vector | Anonymized | Original values were encrypted via neural visual embedding | | collection_name | Vector ID | The ID for accessing the Collection name vector | Anonymized | Original values were encrypted via neural textual embedding | | collection_description | Vector ID | The ID for accessing the Collection description vector | Anonymized | Original values were encrypted via neural textual embedding | | collection_image | Vector ID | The ID for accessing the Collection image vector | Anonymized | Original values were encrypted via neural visual embedding | | fees_seller | Float | The absolute amount of fees the seller has gained from this transaction expressed in *token* | Original | Not sensitive variable | | fees_opensea | Float | The absolute amount of fees OpenSea has gained from this transaction expressed in *token* | Original | Not sensitive variable | | fees_seller_usd | Float | The absolute amount of fees the seller has gained from this transaction expressed in US dollars (USD) | Original | Not sensitive variable | | fees_opensea_usd | Float | The absolute amount of fees OpenSea has gained from this transaction expressed in US dollars (USD) | Original | Not sensitive variable | | payout_collection_address| String | The wallet address where seller fees are deposited | Anonymized | Original values were replaced by hash-codes | | tx_timestamp | String | Timestamp of the transaction expressed in yyyy-mm-ddTHH:MM:SS | Original | Not sensitive variable | | price | Float | The price of the transaction expressed in token | Original | Not sensitive variable | | gain | Float | The gain after fees (i.e., gain = price - fees_opensea * price - fees_seller * price) | Original | Not sensitive variable | | usd_price | Float | The price of the transaction expressed in US dollars (USD) | Original | Not sensitive variable | | usd_gain | Float | The difference between the price and the fees expressed in US dollars (USD) | Original | Not sensitive variable | | token | Categorical | The token type used to pay the transaction | Original | Not sensitive variable | | to_eth | Float | The conversion rate to convert tokens into Ethereum at the current timestamp, such that eth = price * to_eth | Original | Not sensitive variable | | to_usd | Float | The conversion rate to convert tokens into US dollars (USD) at the current timestamp, such that usd = price * to_usd | Original | Not sensitive variable | | from_account | String | The address that sends the payment (i.e., winner/buyer) | Anonymized | Original values were replaced by hash-codes | | to_account | String | The address that receives the payment (it often corresponds to the contract linked to the asset) | Anonymized | Original values were replaced by hash-codes | | seller_account | String | The address of the NFT seller | Anonymized | Original values were replaced by hash-codes | | winner_account | String | The address of the NFT buyer | Anonymized | Original values were replaced by hash-codes | | contract_address | String | The contract address on the blockchain | Anonymized | Original values were replaced by hash-codes | | created_date | Timestamp | The date of creation of the contract | Original | Not sensitive variable | | chain | Categorical | The blockchain where the transaction occurs | Original | Not sensitive variable | | token_type | Categorical | The schema of the token, i.e., ERC721 or ERC1155 | Original | Not sensitive variable | | asset_contract_type | Categorical | The asset typology, i.e., non-fungible or semi-fungible | Original | Not sensitive variable | | asset_type | Categorical | Whether the asset was involved in a simple or bundle transaction | Original | Not sensitive variable | ## How to use Data provided within this repository can be straightforwardly loaded via the *datasets* library as follows: ```python from datasets import load_dataset dataset = load_dataset("MLNTeam-Unical/NFT-70M_transactions") ``` Complementary data involving textual and visual embeddings can be integrated as follows: ```python from datasets import load_dataset import numpy as np transactions_dataset=load_dataset("MLNTeam-Unical/NFT-70M_transactions") image_dataset=load_dataset("MLNTeam-Unical/NFT-70M_image") text_dataset=load_dataset("MLNTeam-Unical/NFT-70M_text") # Mapping from image_id to the row_index within the image dataset image_id2row_index={int(id):k for k,id in enumerate(image_dataset["train"]["id"])} # Mapping from text_id to row_index within the text dataset text_id2row_index={int(id):k for k,id in enumerate(text_dataset["train"]["id"])} def get_image_embedding(image_id,image_id2row_index,image_dataset): # If the mapping contains the image, the embedding exists idx_emb=image_id2row_index.get(int(image_id),None) if idx_emb: # If the embedding exists, return it return np.array(image_dataset["train"].select([idx_emb])["emb"][0]) else: return None def get_text_embedding(text_id,text_id2row_index,text_dataset): # If the mapping contains the text, the embedding exists idx_emb=text_id2row_index.get(int(text_id),None) if idx_emb: # If the embedding exists, return it return np.array(text_dataset["train"].select([idx_emb])["emb"][0]) else: return None ### USAGE EXAMPLE ### # Select transaction_id transaction_id=120 # Get the image_id (e.g., collection_image or nft_image) id_image=transactions_dataset["train"].select([transaction_id])["collection_image"][0] # Get the image image_embedding=get_image_embedding(id_image,image_id2row_index,image_dataset) # Get the text_id id_text=transactions_dataset["train"].select([transaction_id])["collection_description"][0] # Get the text text_embedding=get_text_embedding(id_text,text_id2row_index,text_dataset) ``` ## Ethical use of data and informed consent This data repository is made available for research and informational purposes only. Any finding that might be drawn from the data provided within this repository should be intended to support decision-making regarding actions made on NFTs, and not to replace the human specialists. *The authors are not responsible for any issues related to trading failures based on the data provided within this repository.* ## Terms of Usage Please cite the following papers in any research product whose findings are based on the data provided within this repository: - L. La Cava, D. Costa, A. Tagarelli: SONAR: Web-based Tool for Multimodal Exploration of Non-Fungible Token Inspiration Networks. In: Proc. ACM SIGIR 2023. Taipei, Taiwan, July 23-27 2023. DOI: https://doi.org/10.1145/3539618.3591821 - L. La Cava, D. Costa, A. Tagarelli: Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens. CoRR abs/2303.17031 (2023). DOI: https://doi.org/10.48550/arXiv.2303.17031 - D. Costa, L. La Cava, A. Tagarelli: Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction. In: Proc. ACM WebConf 2023, pp. 1875-1885. Austin, TX, USA, 30 April 2023 – 4 May 2023. DOI: https://doi.org/10.1145/3543507.3583520 Data within this repository were fetched using the REST APIs provided by OpenSea. You should also acknowledge [OpenSea API]("https://docs.opensea.io/reference/api-overview). ## Liability statement The authors hereby declare that they are not responsible for any harmful or objectionable content that may be contained within the data provided within this repository. Users of the dataset are expected to exercise due diligence and responsibility when using the data, including but not limited to: (i) Content Review: Users should review the dataset's contents carefully and assess its suitability for their intended purposes; (ii) Compliance: Users are responsible for ensuring that their use of the dataset complies with all applicable laws, regulations, and ethical standards; (iii) Data Processing: Users may need to apply data preprocessing, filtering, or other techniques to remove or address any objectionable or harmful content as needed. The authors of this dataset disclaim any liability for the accuracy, completeness, or suitability of the data and shall not be held responsible for any consequences resulting from the use or misuse of the dataset. *By accessing and using this dataset, users acknowledge and accept this disclaimer.*

dbaezaj/lince_ner_dataset

--- dataset_info: features: - name: id dtype: int64 - name: words sequence: string - name: lid sequence: string - name: labels sequence: string ---

open-llm-leaderboard/details_fionazhang__fine-tune-mistral-environment-merge

--- pretty_name: Evaluation run of fionazhang/fine-tune-mistral-environment-merge dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [fionazhang/fine-tune-mistral-environment-merge](https://huggingface.co/fionazhang/fine-tune-mistral-environment-merge)\ \ 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_fionazhang__fine-tune-mistral-environment-merge\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-29T01:47:21.122290](https://huggingface.co/datasets/open-llm-leaderboard/details_fionazhang__fine-tune-mistral-environment-merge/blob/main/results_2024-01-29T01-47-21.122290.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\": 0.6354317163514616,\n\ \ \"acc_stderr\": 0.032307072675482454,\n \"acc_norm\": 0.6419311935208026,\n\ \ \"acc_norm_stderr\": 0.03296064982960984,\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.4397408572877062,\n\ \ \"mc2_stderr\": 0.014194431681893268\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5725255972696246,\n \"acc_stderr\": 0.014456862944650649,\n\ \ \"acc_norm\": 0.6262798634812287,\n \"acc_norm_stderr\": 0.014137708601759086\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.635929097789285,\n\ \ \"acc_stderr\": 0.00480185288132974,\n \"acc_norm\": 0.8365863373829915,\n\ \ \"acc_norm_stderr\": 0.0036898701424130753\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\ \ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\ \ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.038947344870133176,\n\ \ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.038947344870133176\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\": 0.6830188679245283,\n \"acc_stderr\": 0.028637235639800893,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.028637235639800893\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n\ \ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\ \ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\ \ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155254,\n \"\ acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155254\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.043902592653775614,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.043902592653775614\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7548387096774194,\n\ \ \"acc_stderr\": 0.024472243840895525,\n \"acc_norm\": 0.7548387096774194,\n\ \ \"acc_norm_stderr\": 0.024472243840895525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.029376616484945633,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.029376616484945633\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812142,\n\ \ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812142\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.02432173848460235,\n \ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.02432173848460235\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \ \ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.030956636328566548,\n\ \ \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.030956636328566548\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8201834862385321,\n \"acc_stderr\": 0.01646534546739154,\n \"\ acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.01646534546739154\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.033981108902946366,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.033981108902946366\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7843137254901961,\n \"acc_stderr\": 0.02886743144984932,\n \"\ acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.02886743144984932\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.03114679648297246,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.03114679648297246\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.034465133507525975,\n\ \ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.034465133507525975\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.036401182719909456,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.036401182719909456\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822585,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822585\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8071519795657727,\n\ \ \"acc_stderr\": 0.014108533515757431,\n \"acc_norm\": 0.8071519795657727,\n\ \ \"acc_norm_stderr\": 0.014108533515757431\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577615,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577615\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.36089385474860336,\n\ \ \"acc_stderr\": 0.016062290671110462,\n \"acc_norm\": 0.36089385474860336,\n\ \ \"acc_norm_stderr\": 0.016062290671110462\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.0248480182638752,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.0248480182638752\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632938,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632938\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.02500646975579921,\n\ \ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.02500646975579921\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4471968709256845,\n\ \ \"acc_stderr\": 0.012698825252435108,\n \"acc_norm\": 0.4471968709256845,\n\ \ \"acc_norm_stderr\": 0.012698825252435108\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000314,\n \ \ \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000314\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\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291296,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291296\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.4397408572877062,\n\ \ \"mc2_stderr\": 0.014194431681893268\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7892659826361483,\n \"acc_stderr\": 0.011462046419710681\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3525398028809704,\n \ \ \"acc_stderr\": 0.013159909755930323\n }\n}\n```" repo_url: https://huggingface.co/fionazhang/fine-tune-mistral-environment-merge 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_29T01_47_21.122290 path: - '**/details_harness|arc:challenge|25_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-29T01-47-21.122290.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|gsm8k|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hellaswag|10_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-29T01-47-21.122290.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-management|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-29T01-47-21.122290.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|truthfulqa:mc|0_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-29T01-47-21.122290.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_29T01_47_21.122290 path: - '**/details_harness|winogrande|5_2024-01-29T01-47-21.122290.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-29T01-47-21.122290.parquet' - config_name: results data_files: - split: 2024_01_29T01_47_21.122290 path: - results_2024-01-29T01-47-21.122290.parquet - split: latest path: - results_2024-01-29T01-47-21.122290.parquet --- # Dataset Card for Evaluation run of fionazhang/fine-tune-mistral-environment-merge  Dataset automatically created during the evaluation run of model [fionazhang/fine-tune-mistral-environment-merge](https://huggingface.co/fionazhang/fine-tune-mistral-environment-merge) 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_fionazhang__fine-tune-mistral-environment-merge", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-29T01:47:21.122290](https://huggingface.co/datasets/open-llm-leaderboard/details_fionazhang__fine-tune-mistral-environment-merge/blob/main/results_2024-01-29T01-47-21.122290.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": 0.6354317163514616, "acc_stderr": 0.032307072675482454, "acc_norm": 0.6419311935208026, "acc_norm_stderr": 0.03296064982960984, "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.4397408572877062, "mc2_stderr": 0.014194431681893268 }, "harness|arc:challenge|25": { "acc": 0.5725255972696246, "acc_stderr": 0.014456862944650649, "acc_norm": 0.6262798634812287, "acc_norm_stderr": 0.014137708601759086 }, "harness|hellaswag|10": { "acc": 0.635929097789285, "acc_stderr": 0.00480185288132974, "acc_norm": 0.8365863373829915, "acc_norm_stderr": 0.0036898701424130753 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6074074074074074, "acc_stderr": 0.0421850621536888, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.0421850621536888 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6447368421052632, "acc_stderr": 0.038947344870133176, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.038947344870133176 }, "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": 0.6830188679245283, "acc_stderr": 0.028637235639800893, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.028637235639800893 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.653179190751445, "acc_stderr": 0.036291466701596636, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.036291466701596636 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155254, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155254 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.40476190476190477, "acc_stderr": 0.043902592653775614, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.043902592653775614 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7548387096774194, "acc_stderr": 0.024472243840895525, "acc_norm": 0.7548387096774194, "acc_norm_stderr": 0.024472243840895525 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7515151515151515, "acc_stderr": 0.03374402644139403, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.03374402644139403 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.029376616484945633, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.029376616484945633 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8808290155440415, "acc_stderr": 0.02338193534812142, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.02338193534812142 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6410256410256411, "acc_stderr": 0.02432173848460235, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.02432173848460235 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251976, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251976 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6512605042016807, "acc_stderr": 0.030956636328566548, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.030956636328566548 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8201834862385321, "acc_stderr": 0.01646534546739154, "acc_norm": 0.8201834862385321, "acc_norm_stderr": 0.01646534546739154 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5416666666666666, "acc_stderr": 0.033981108902946366, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.033981108902946366 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7843137254901961, "acc_stderr": 0.02886743144984932, "acc_norm": 0.7843137254901961, "acc_norm_stderr": 0.02886743144984932 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.03114679648297246, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.03114679648297246 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8091603053435115, "acc_stderr": 0.034465133507525975, "acc_norm": 0.8091603053435115, "acc_norm_stderr": 0.034465133507525975 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.036401182719909456, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.036401182719909456 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252627, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.0335195387952127, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.0335195387952127 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822585, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822585 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165616, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165616 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8071519795657727, "acc_stderr": 0.014108533515757431, "acc_norm": 0.8071519795657727, "acc_norm_stderr": 0.014108533515757431 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7196531791907514, "acc_stderr": 0.024182427496577615, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.024182427496577615 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.36089385474860336, "acc_stderr": 0.016062290671110462, "acc_norm": 0.36089385474860336, "acc_norm_stderr": 0.016062290671110462 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7483660130718954, "acc_stderr": 0.0248480182638752, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.0248480182638752 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632938, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632938 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7191358024691358, "acc_stderr": 0.02500646975579921, "acc_norm": 0.7191358024691358, "acc_norm_stderr": 0.02500646975579921 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4471968709256845, "acc_stderr": 0.012698825252435108, "acc_norm": 0.4471968709256845, "acc_norm_stderr": 0.012698825252435108 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462923, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462923 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6650326797385621, "acc_stderr": 0.019094228167000314, "acc_norm": 0.6650326797385621, "acc_norm_stderr": 0.019094228167000314 }, "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": 0.7183673469387755, "acc_stderr": 0.028795185574291296, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.028795185574291296 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8109452736318408, "acc_stderr": 0.02768691358801302, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.02768691358801302 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.4397408572877062, "mc2_stderr": 0.014194431681893268 }, "harness|winogrande|5": { "acc": 0.7892659826361483, "acc_stderr": 0.011462046419710681 }, "harness|gsm8k|5": { "acc": 0.3525398028809704, "acc_stderr": 0.013159909755930323 } } ``` ## 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]

Megnis/python_code_instructions_27k_Saiga

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 83038004 num_examples: 27224 download_size: 30871544 dataset_size: 83038004 configs: - config_name: default data_files: - split: train path: data/train-* ---

EmmaGthn/Moji_balanced

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: gender dtype: int64 splits: - name: train num_bytes: 6618798 num_examples: 53016 - name: test num_bytes: 256593 num_examples: 2000 download_size: 2582045 dataset_size: 6875391 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

CyberHarem/galleon_granbluefantasy

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of galleon/ガレヲン (Granblue Fantasy) This is the dataset of galleon/ガレヲン (Granblue Fantasy), containing 325 images and their tags. The core tags of this character are `brown_hair, long_hair, animal_ears, horns, breasts, pointy_ears, extra_ears, bangs, multicolored_hair, large_breasts, streaked_hair, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 325 | 557.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 325 | 293.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 815 | 666.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 325 | 483.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 815 | 988.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/galleon_granbluefantasy/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/galleon_granbluefantasy', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, black_dress, closed_eyes, detached_sleeves, frilled_sleeves, solo, white_gloves, bare_shoulders, blush | | 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, closed_eyes, detached_sleeves, frilled_sleeves, solo, white_gloves, asymmetrical_hair, upper_body | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, asymmetrical_hair, asymmetrical_legwear, closed_eyes, detached_sleeves, frilled_sleeves, solo, thigh_strap, white_gloves, pelvic_curtain, black_dress, full_body, hair_between_eyes, single_thighhigh | | 3 | 16 | ![](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) | 1boy, 1girl, closed_eyes, hetero, solo_focus, blush, detached_sleeves, white_gloves, nipples, paizuri, huge_breasts, mosaic_censoring, kissing_penis, nude | | 4 | 17 | ![](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, blindfold, cleavage, solo, blue_hair, smile, thigh_strap, long_sleeves, mask, nail_polish, closed_mouth, facing_viewer, parted_lips | | 5 | 9 | ![](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, bare_shoulders, cleavage, closed_eyes, navel, solo, bikini, hair_between_eyes, thighs, blush, blue_hair, collarbone, thigh_strap, wet | | 6 | 11 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, closed_eyes, solo, cleavage, collared_shirt, long_sleeves, white_shirt, blue_hair, blush, smile, collarbone, hair_between_eyes, naked_shirt, navel, sitting | | 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, closed_eyes, completely_nude, hair_between_eyes, solo, smile, collarbone, nipples, artist_name, barefoot, blue_hair, blush, closed_mouth, lips, navel | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_dress | closed_eyes | detached_sleeves | frilled_sleeves | solo | white_gloves | bare_shoulders | blush | asymmetrical_hair | upper_body | asymmetrical_legwear | thigh_strap | pelvic_curtain | full_body | hair_between_eyes | single_thighhigh | 1boy | hetero | solo_focus | nipples | paizuri | huge_breasts | mosaic_censoring | kissing_penis | nude | blindfold | cleavage | blue_hair | smile | long_sleeves | mask | nail_polish | closed_mouth | facing_viewer | parted_lips | navel | bikini | thighs | collarbone | wet | collared_shirt | white_shirt | naked_shirt | sitting | completely_nude | artist_name | barefoot | lips | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:--------------|:-------------------|:------------------|:-------|:---------------|:-----------------|:--------|:--------------------|:-------------|:-----------------------|:--------------|:-----------------|:------------|:--------------------|:-------------------|:-------|:---------|:-------------|:----------|:----------|:---------------|:-------------------|:----------------|:-------|:------------|:-----------|:------------|:--------|:---------------|:-------|:--------------|:---------------|:----------------|:--------------|:--------|:---------|:---------|:-------------|:------|:-----------------|:--------------|:--------------|:----------|:------------------|:--------------|:-----------|:-------| | 0 | 8 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 16 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 17 | ![](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 | | | | | | | | | | | | | | | 5 | 9 | ![](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 | X | X | | | | | | | | | | 6 | 11 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | X | | | X | | | X | | | | | | | X | | | | | | | | | | | | X | X | X | X | | | | | | X | | | X | | X | X | X | X | | | | | | 7 | 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 |

joseluhf11/oct-object-detection-v2-merge

--- dataset_info: features: - name: image dtype: image - name: objects struct: - name: bbox sequence: sequence: int64 - name: categories sequence: string splits: - name: train num_bytes: 153967507.25 num_examples: 1246 download_size: 71637288 dataset_size: 153967507.25 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "oct-object-detection-v2-merge" Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease. Changes from from v1 are images are grouped into a single row for the same class detection object, and also join with merge method overlapping boxes. merge means, get the whole area covered by both boxes. [Source datataset](https://doi.org/10.1101/2023.03.29.534704)

chirunder/MixSnips_for_DecoderOnly_90-10_split-HALF

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: completion dtype: string - name: text dtype: string splits: - name: train num_bytes: 17739996.800127994 num_examples: 22500 - name: test num_bytes: 1971899.199872005 num_examples: 2501 download_size: 7061034 dataset_size: 19711896.0 --- # Dataset Card for "MixSnips_for_DecoderOnly_90-10_split-HALF" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nlpUc3mStudents/mental-risk-d

--- dataset_info: features: - name: subject_id dtype: string - name: id_message dtype: int64 - name: date dtype: string - name: message dtype: string - name: suffer_in_favour dtype: float64 - name: suffer_against dtype: float64 - name: suffer_other dtype: float64 - name: control dtype: float64 splits: - name: train num_bytes: 949991 num_examples: 6248 - name: test num_bytes: 91047 num_examples: 624 download_size: 486498 dataset_size: 1041038 --- # Dataset Card for "mental-risk-d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)