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Umbrellos/pablobackyardigans
Umbrellos
2023-12-10T23:41:00Z
0
0
[ "license:openrail", "region:us" ]
null
2023-12-10T23:02:08Z
--- license: openrail ---
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open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15.2
open-llm-leaderboard
2023-12-10T23:06:21Z
0
0
[ "region:us" ]
null
2023-12-10T23:05:40Z
--- pretty_name: Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [OpenBuddy/openbuddy-deepseek-67b-v15.2](https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_OpenBuddy__openbuddy-deepseek-67b-v15.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-10T23:02:54.951377](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15.2/blob/main/results_2023-12-10T23-02-54.951377.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\": 0.714438762465758,\n\ \ \"acc_stderr\": 0.02999619071511929,\n \"acc_norm\": 0.7169995069840682,\n\ \ \"acc_norm_stderr\": 0.030591759435653683,\n \"mc1\": 0.3929008567931457,\n\ \ \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.5620230899021985,\n\ \ \"mc2_stderr\": 0.014942776097379195\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6424914675767918,\n \"acc_stderr\": 0.014005494275916573,\n\ \ \"acc_norm\": 0.6860068259385665,\n \"acc_norm_stderr\": 0.0135626912247263\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6717785301732723,\n\ \ \"acc_stderr\": 0.004686062421158153,\n \"acc_norm\": 0.8636725751842262,\n\ \ \"acc_norm_stderr\": 0.0034243464481037295\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6962962962962963,\n\ \ \"acc_stderr\": 0.039725528847851375,\n \"acc_norm\": 0.6962962962962963,\n\ \ \"acc_norm_stderr\": 0.039725528847851375\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7960526315789473,\n \"acc_stderr\": 0.032790004063100515,\n\ \ \"acc_norm\": 0.7960526315789473,\n \"acc_norm_stderr\": 0.032790004063100515\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.81,\n\ \ \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n \ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7660377358490567,\n \"acc_stderr\": 0.02605529690115292,\n\ \ \"acc_norm\": 0.7660377358490567,\n \"acc_norm_stderr\": 0.02605529690115292\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8541666666666666,\n\ \ \"acc_stderr\": 0.029514245964291762,\n \"acc_norm\": 0.8541666666666666,\n\ \ \"acc_norm_stderr\": 0.029514245964291762\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.58,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.7052023121387283,\n\ \ \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.7052023121387283,\n\ \ \"acc_norm_stderr\": 0.03476599607516478\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6808510638297872,\n \"acc_stderr\": 0.03047297336338005,\n\ \ \"acc_norm\": 0.6808510638297872,\n \"acc_norm_stderr\": 0.03047297336338005\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.543859649122807,\n\ \ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.543859649122807,\n\ \ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6827586206896552,\n \"acc_stderr\": 0.038783523721386215,\n\ \ \"acc_norm\": 0.6827586206896552,\n \"acc_norm_stderr\": 0.038783523721386215\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.5264550264550265,\n \"acc_stderr\": 0.02571523981134675,\n \"\ acc_norm\": 0.5264550264550265,\n \"acc_norm_stderr\": 0.02571523981134675\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5396825396825397,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8290322580645161,\n \"acc_stderr\": 0.021417242936321582,\n \"\ acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.021417242936321582\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5911330049261084,\n \"acc_stderr\": 0.03459058815883233,\n \"\ acc_norm\": 0.5911330049261084,\n \"acc_norm_stderr\": 0.03459058815883233\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\"\ : 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8242424242424242,\n \"acc_stderr\": 0.02972094300622445,\n\ \ \"acc_norm\": 0.8242424242424242,\n \"acc_norm_stderr\": 0.02972094300622445\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8787878787878788,\n \"acc_stderr\": 0.023253157951942088,\n \"\ acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.023253157951942088\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9637305699481865,\n \"acc_stderr\": 0.013492659751295141,\n\ \ \"acc_norm\": 0.9637305699481865,\n \"acc_norm_stderr\": 0.013492659751295141\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.022421273612923714,\n\ \ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.022421273612923714\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113114,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113114\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n\ \ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4966887417218543,\n \"acc_stderr\": 0.04082393379449654,\n \"\ acc_norm\": 0.4966887417218543,\n \"acc_norm_stderr\": 0.04082393379449654\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9045871559633027,\n \"acc_stderr\": 0.012595899282335801,\n \"\ acc_norm\": 0.9045871559633027,\n \"acc_norm_stderr\": 0.012595899282335801\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6111111111111112,\n \"acc_stderr\": 0.033247089118091176,\n \"\ acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.033247089118091176\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073322,\n \"\ acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073322\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9071729957805907,\n \"acc_stderr\": 0.018889750550956715,\n \ \ \"acc_norm\": 0.9071729957805907,\n \"acc_norm_stderr\": 0.018889750550956715\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n\ \ \"acc_stderr\": 0.02737309550054019,\n \"acc_norm\": 0.7892376681614349,\n\ \ \"acc_norm_stderr\": 0.02737309550054019\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8347107438016529,\n \"acc_stderr\": 0.03390780612972776,\n \"\ acc_norm\": 0.8347107438016529,\n \"acc_norm_stderr\": 0.03390780612972776\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.803680981595092,\n \"acc_stderr\": 0.031207970394709225,\n\ \ \"acc_norm\": 0.803680981595092,\n \"acc_norm_stderr\": 0.031207970394709225\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5982142857142857,\n\ \ \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.5982142857142857,\n\ \ \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761012,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761012\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9316239316239316,\n\ \ \"acc_stderr\": 0.016534627684311368,\n \"acc_norm\": 0.9316239316239316,\n\ \ \"acc_norm_stderr\": 0.016534627684311368\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9042145593869731,\n\ \ \"acc_stderr\": 0.010524031079055845,\n \"acc_norm\": 0.9042145593869731,\n\ \ \"acc_norm_stderr\": 0.010524031079055845\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7803468208092486,\n \"acc_stderr\": 0.02228963885261789,\n\ \ \"acc_norm\": 0.7803468208092486,\n \"acc_norm_stderr\": 0.02228963885261789\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4670391061452514,\n\ \ \"acc_stderr\": 0.016686126653013934,\n \"acc_norm\": 0.4670391061452514,\n\ \ \"acc_norm_stderr\": 0.016686126653013934\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7973856209150327,\n \"acc_stderr\": 0.02301544687798568,\n\ \ \"acc_norm\": 0.7973856209150327,\n \"acc_norm_stderr\": 0.02301544687798568\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7909967845659164,\n\ \ \"acc_stderr\": 0.02309314039837422,\n \"acc_norm\": 0.7909967845659164,\n\ \ \"acc_norm_stderr\": 0.02309314039837422\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.845679012345679,\n \"acc_stderr\": 0.020100830999851,\n\ \ \"acc_norm\": 0.845679012345679,\n \"acc_norm_stderr\": 0.020100830999851\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5354609929078015,\n \"acc_stderr\": 0.029752389657427054,\n \ \ \"acc_norm\": 0.5354609929078015,\n \"acc_norm_stderr\": 0.029752389657427054\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5573663624511083,\n\ \ \"acc_stderr\": 0.012685906538206239,\n \"acc_norm\": 0.5573663624511083,\n\ \ \"acc_norm_stderr\": 0.012685906538206239\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.026799562024887664,\n\ \ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.026799562024887664\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7777777777777778,\n \"acc_stderr\": 0.01681902837573638,\n \ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.01681902837573638\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7918367346938775,\n \"acc_stderr\": 0.025991117672813296,\n\ \ \"acc_norm\": 0.7918367346938775,\n \"acc_norm_stderr\": 0.025991117672813296\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n\ \ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\ \ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n\ \ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3929008567931457,\n\ \ \"mc1_stderr\": 0.017097248285233065,\n \"mc2\": 0.5620230899021985,\n\ \ \"mc2_stderr\": 0.014942776097379195\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8445146014206788,\n \"acc_stderr\": 0.010184308214775778\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.66868840030326,\n \ \ \"acc_stderr\": 0.012964999679688687\n }\n}\n```" repo_url: https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|arc:challenge|25_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-10T23-02-54.951377.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|gsm8k|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hellaswag|10_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-02-54.951377.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-management|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-10T23-02-54.951377.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|truthfulqa:mc|0_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-10T23-02-54.951377.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_10T23_02_54.951377 path: - '**/details_harness|winogrande|5_2023-12-10T23-02-54.951377.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-10T23-02-54.951377.parquet' - config_name: results data_files: - split: 2023_12_10T23_02_54.951377 path: - results_2023-12-10T23-02-54.951377.parquet - split: latest path: - results_2023-12-10T23-02-54.951377.parquet --- # Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseek-67b-v15.2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.2 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [OpenBuddy/openbuddy-deepseek-67b-v15.2](https://huggingface.co/OpenBuddy/openbuddy-deepseek-67b-v15.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_OpenBuddy__openbuddy-deepseek-67b-v15.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-10T23:02:54.951377](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-deepseek-67b-v15.2/blob/main/results_2023-12-10T23-02-54.951377.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": 0.714438762465758, "acc_stderr": 0.02999619071511929, "acc_norm": 0.7169995069840682, "acc_norm_stderr": 0.030591759435653683, "mc1": 0.3929008567931457, "mc1_stderr": 0.017097248285233065, "mc2": 0.5620230899021985, "mc2_stderr": 0.014942776097379195 }, "harness|arc:challenge|25": { "acc": 0.6424914675767918, "acc_stderr": 0.014005494275916573, "acc_norm": 0.6860068259385665, "acc_norm_stderr": 0.0135626912247263 }, "harness|hellaswag|10": { "acc": 0.6717785301732723, "acc_stderr": 0.004686062421158153, "acc_norm": 0.8636725751842262, "acc_norm_stderr": 0.0034243464481037295 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6962962962962963, "acc_stderr": 0.039725528847851375, "acc_norm": 0.6962962962962963, "acc_norm_stderr": 0.039725528847851375 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7960526315789473, "acc_stderr": 0.032790004063100515, "acc_norm": 0.7960526315789473, "acc_norm_stderr": 0.032790004063100515 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7660377358490567, "acc_stderr": 0.02605529690115292, "acc_norm": 0.7660377358490567, "acc_norm_stderr": 0.02605529690115292 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8541666666666666, "acc_stderr": 0.029514245964291762, "acc_norm": 0.8541666666666666, "acc_norm_stderr": 0.029514245964291762 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.58, "acc_stderr": 0.04960449637488584, "acc_norm": 0.58, 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"harness|truthfulqa:mc|0": { "mc1": 0.3929008567931457, "mc1_stderr": 0.017097248285233065, "mc2": 0.5620230899021985, "mc2_stderr": 0.014942776097379195 }, "harness|winogrande|5": { "acc": 0.8445146014206788, "acc_stderr": 0.010184308214775778 }, "harness|gsm8k|5": { "acc": 0.66868840030326, "acc_stderr": 0.012964999679688687 } } ``` ### 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]
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sdadasfgdfgfdg/xokit0
sdadasfgdfgfdg
2023-12-10T23:14:06Z
0
0
[ "license:openrail", "region:us" ]
null
2023-12-10T23:13:12Z
--- license: openrail ---
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arpachat/FashionTextImageSmall
arpachat
2023-12-10T23:16:38Z
0
0
[ "region:us" ]
null
2023-12-10T23:16:35Z
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1176081.0 num_examples: 250 download_size: 750208 dataset_size: 1176081.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
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sartajbhuvaji/Brain-Tumor-Classification
sartajbhuvaji
2023-12-10T23:36:07Z
0
1
[ "task_categories:image-classification", "size_categories:1K<n<10K", "license:mit", "biology", "brain tumor", "MRI", "tumor", "region:us" ]
[ "image-classification" ]
2023-12-10T23:27:32Z
--- license: mit task_categories: - image-classification tags: - biology - brain tumor - MRI - tumor pretty_name: 'Brain Tumor ' size_categories: - 1K<n<10K ---
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vahn9995/booksum-stable-diffusion-prompt
vahn9995
2023-12-11T00:22:27Z
0
0
[ "task_categories:summarization", "size_categories:1K<n<10K", "language:en", "license:bsd-3-clause-clear", "region:us" ]
[ "summarization" ]
2023-12-10T23:27:55Z
--- license: bsd-3-clause-clear task_categories: - summarization language: - en pretty_name: Stable Diffusion Book Sum Prompts size_categories: - 1K<n<10K --- This dataset was based off the https://huggingface.co/datasets/kmfoda/booksum dataset. It was made using GPT 3, inferencing it to create a multitude of stable-diffusion friendly prompts by passing in book chapters from the booksum dataset. This can be utilized in large-language model fine-tuning or training to create a model that outputs stable diffusion prompts.
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marcelosantos/marcelo
marcelosantos
2023-12-10T23:39:43Z
0
0
[ "license:openrail", "region:us" ]
null
2023-12-10T23:36:11Z
--- license: openrail ---
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mitchcardoso/mitchcardoso
mitchcardoso
2023-12-10T23:49:11Z
0
0
[ "license:openrail", "region:us" ]
null
2023-12-10T23:47:51Z
--- license: openrail ---
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Vinnyyw/Belivoices
Vinnyyw
2023-12-10T23:49:49Z
0
0
[ "license:openrail", "region:us" ]
null
2023-12-10T23:49:00Z
--- license: openrail ---
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breno30/AlcyrCruz
breno30
2023-12-11T00:06:36Z
0
0
[ "license:openrail", "region:us" ]
null
2023-12-11T00:06:13Z
--- license: openrail ---
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open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ
open-llm-leaderboard
2023-12-11T00:15:56Z
0
0
[ "region:us" ]
null
2023-12-11T00:15:15Z
--- pretty_name: Evaluation run of TheBloke/neural-chat-7B-v3-2-GPTQ dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/neural-chat-7B-v3-2-GPTQ](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ)\ \ 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_TheBloke__neural-chat-7B-v3-2-GPTQ\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T00:12:21.907526](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ/blob/main/results_2023-12-11T00-12-21.907526.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\": 0.6058481456466821,\n\ \ \"acc_stderr\": 0.03323160720607251,\n \"acc_norm\": 0.6077924426433228,\n\ \ \"acc_norm_stderr\": 0.033909992378155715,\n \"mc1\": 0.4541003671970624,\n\ \ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.5979099902582387,\n\ \ \"mc2_stderr\": 0.01509977856693472\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6296928327645052,\n \"acc_stderr\": 0.01411129875167495,\n\ \ \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892978\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6360286795459071,\n\ \ \"acc_stderr\": 0.004801572028920794,\n \"acc_norm\": 0.8324039036048596,\n\ \ \"acc_norm_stderr\": 0.003727438786513393\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\ \ \"acc_stderr\": 0.04304979692464241,\n \"acc_norm\": 0.5407407407407407,\n\ \ \"acc_norm_stderr\": 0.04304979692464241\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.02914690474779833,\n\ \ \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.02914690474779833\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\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5722543352601156,\n\ \ \"acc_stderr\": 0.03772446857518027,\n \"acc_norm\": 0.5722543352601156,\n\ \ \"acc_norm_stderr\": 0.03772446857518027\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.49019607843137253,\n \"acc_stderr\": 0.04974229460422817,\n\ \ \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.04974229460422817\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.03267151848924777,\n\ \ \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.03267151848924777\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\ \ \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.4298245614035088,\n\ \ \"acc_norm_stderr\": 0.04657047260594964\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3544973544973545,\n \"acc_stderr\": 0.024636830602842,\n \"acc_norm\"\ : 0.3544973544973545,\n \"acc_norm_stderr\": 0.024636830602842\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7419354838709677,\n\ \ \"acc_stderr\": 0.02489246917246283,\n \"acc_norm\": 0.7419354838709677,\n\ \ \"acc_norm_stderr\": 0.02489246917246283\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\"\ : 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8341968911917098,\n \"acc_stderr\": 0.026839845022314415,\n\ \ \"acc_norm\": 0.8341968911917098,\n \"acc_norm_stderr\": 0.026839845022314415\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5948717948717949,\n \"acc_stderr\": 0.024890471769938145,\n\ \ \"acc_norm\": 0.5948717948717949,\n \"acc_norm_stderr\": 0.024890471769938145\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871923,\n \ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871923\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7963302752293578,\n \"acc_stderr\": 0.017266742087630783,\n \"\ acc_norm\": 0.7963302752293578,\n \"acc_norm_stderr\": 0.017266742087630783\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\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\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6564885496183206,\n \"acc_stderr\": 0.041649760719448786,\n\ \ \"acc_norm\": 0.6564885496183206,\n \"acc_norm_stderr\": 0.041649760719448786\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\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.04414343666854934,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.04414343666854934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6319018404907976,\n \"acc_stderr\": 0.03789213935838396,\n\ \ \"acc_norm\": 0.6319018404907976,\n \"acc_norm_stderr\": 0.03789213935838396\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n\ \ \"acc_stderr\": 0.02514093595033544,\n \"acc_norm\": 0.8205128205128205,\n\ \ \"acc_norm_stderr\": 0.02514093595033544\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7841634738186463,\n\ \ \"acc_stderr\": 0.014711684386139953,\n \"acc_norm\": 0.7841634738186463,\n\ \ \"acc_norm_stderr\": 0.014711684386139953\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.02590663263101613,\n\ \ \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.02590663263101613\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39106145251396646,\n\ \ \"acc_stderr\": 0.016320763763808383,\n \"acc_norm\": 0.39106145251396646,\n\ \ \"acc_norm_stderr\": 0.016320763763808383\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.026643278474508755,\n\ \ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.026643278474508755\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\ \ \"acc_stderr\": 0.026311858071854155,\n \"acc_norm\": 0.6881028938906752,\n\ \ \"acc_norm_stderr\": 0.026311858071854155\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6728395061728395,\n \"acc_stderr\": 0.02610567386140983,\n\ \ \"acc_norm\": 0.6728395061728395,\n \"acc_norm_stderr\": 0.02610567386140983\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.42907801418439717,\n \"acc_stderr\": 0.029525914302558555,\n \ \ \"acc_norm\": 0.42907801418439717,\n \"acc_norm_stderr\": 0.029525914302558555\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4165580182529335,\n\ \ \"acc_stderr\": 0.012591153245057392,\n \"acc_norm\": 0.4165580182529335,\n\ \ \"acc_norm_stderr\": 0.012591153245057392\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5625,\n \"acc_stderr\": 0.030134614954403924,\n \ \ \"acc_norm\": 0.5625,\n \"acc_norm_stderr\": 0.030134614954403924\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6045751633986928,\n \"acc_stderr\": 0.01978046595477751,\n \ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.01978046595477751\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\ \ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\ \ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.029162738410249772,\n\ \ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.029162738410249772\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7860696517412935,\n\ \ \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.7860696517412935,\n\ \ \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness|hendrycksTest-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\": 0.7719298245614035,\n \"acc_stderr\": 0.03218093795602357,\n\ \ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.03218093795602357\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4541003671970624,\n\ \ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.5979099902582387,\n\ \ \"mc2_stderr\": 0.01509977856693472\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7947908445146015,\n \"acc_stderr\": 0.01135031570746206\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5284306292645944,\n \ \ \"acc_stderr\": 0.013750202076584419\n }\n}\n```" repo_url: https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|arc:challenge|25_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-11T00-12-21.907526.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|gsm8k|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hellaswag|10_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T00-12-21.907526.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|winogrande|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-11T00-12-21.907526.parquet' - config_name: results data_files: - split: 2023_12_11T00_12_21.907526 path: - results_2023-12-11T00-12-21.907526.parquet - split: latest path: - results_2023-12-11T00-12-21.907526.parquet --- # Dataset Card for Evaluation run of TheBloke/neural-chat-7B-v3-2-GPTQ ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [TheBloke/neural-chat-7B-v3-2-GPTQ](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ) 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_TheBloke__neural-chat-7B-v3-2-GPTQ", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T00:12:21.907526](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ/blob/main/results_2023-12-11T00-12-21.907526.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": 0.6058481456466821, "acc_stderr": 0.03323160720607251, "acc_norm": 0.6077924426433228, "acc_norm_stderr": 0.033909992378155715, "mc1": 0.4541003671970624, "mc1_stderr": 0.017429593091323522, "mc2": 0.5979099902582387, "mc2_stderr": 0.01509977856693472 }, "harness|arc:challenge|25": { "acc": 0.6296928327645052, "acc_stderr": 0.01411129875167495, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.013847460518892978 }, "harness|hellaswag|10": { "acc": 0.6360286795459071, "acc_stderr": 0.004801572028920794, "acc_norm": 0.8324039036048596, "acc_norm_stderr": 0.003727438786513393 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5407407407407407, "acc_stderr": 0.04304979692464241, "acc_norm": 0.5407407407407407, "acc_norm_stderr": 0.04304979692464241 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6710526315789473, "acc_stderr": 0.03823428969926605, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.03823428969926605 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.660377358490566, "acc_stderr": 0.02914690474779833, "acc_norm": 0.660377358490566, "acc_norm_stderr": 0.02914690474779833 }, "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": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5722543352601156, "acc_stderr": 0.03772446857518027, "acc_norm": 0.5722543352601156, "acc_norm_stderr": 0.03772446857518027 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.49019607843137253, "acc_stderr": 0.04974229460422817, "acc_norm": 0.49019607843137253, "acc_norm_stderr": 0.04974229460422817 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5148936170212766, "acc_stderr": 0.03267151848924777, "acc_norm": 0.5148936170212766, "acc_norm_stderr": 0.03267151848924777 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4298245614035088, "acc_stderr": 0.04657047260594964, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.04657047260594964 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3544973544973545, "acc_stderr": 0.024636830602842, "acc_norm": 0.3544973544973545, "acc_norm_stderr": 0.024636830602842 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7419354838709677, "acc_stderr": 0.02489246917246283, "acc_norm": 0.7419354838709677, "acc_norm_stderr": 0.02489246917246283 }, 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[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]
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tanzirghumay/425
tanzirghumay
2023-12-11T00:21:11Z
0
0
[ "license:apache-2.0", "region:us" ]
null
2023-12-11T00:20:25Z
--- license: apache-2.0 ---
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agvidit1/Dataset-TG-HS-HX-Processed
agvidit1
2023-12-11T00:41:43Z
0
0
[ "region:us" ]
null
2023-12-11T00:41:41Z
--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 4582267 num_examples: 35982 download_size: 2878665 dataset_size: 4582267 configs: - config_name: default data_files: - split: train path: data/train-* ---
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arpachat/FashionTextImageSmall1
arpachat
2023-12-11T00:43:43Z
0
0
[ "region:us" ]
null
2023-12-11T00:43:22Z
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 225387304.77 num_examples: 44441 download_size: 269048072 dataset_size: 225387304.77 configs: - config_name: default data_files: - split: train path: data/train-* ---
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open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1
open-llm-leaderboard
2023-12-11T00:53:08Z
0
0
[ "region:us" ]
null
2023-12-11T00:52:28Z
--- pretty_name: Evaluation run of leejunhyeok/MoMo-70B-LoRA-V1.2_1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [leejunhyeok/MoMo-70B-LoRA-V1.2_1](https://huggingface.co/leejunhyeok/MoMo-70B-LoRA-V1.2_1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T00:49:27.690522](https://huggingface.co/datasets/open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1/blob/main/results_2023-12-11T00-49-27.690522.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\": 0.6982187092414253,\n\ \ \"acc_stderr\": 0.03042209430227255,\n \"acc_norm\": 0.7019460256253881,\n\ \ \"acc_norm_stderr\": 0.03101566845962337,\n \"mc1\": 0.4394124847001224,\n\ \ \"mc1_stderr\": 0.01737452048251371,\n \"mc2\": 0.6140893594618404,\n\ \ \"mc2_stderr\": 0.014827391515273696\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6715017064846417,\n \"acc_stderr\": 0.0137249784655373,\n\ \ \"acc_norm\": 0.7064846416382252,\n \"acc_norm_stderr\": 0.013307250444941115\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6772555267874926,\n\ \ \"acc_stderr\": 0.004665704208339046,\n \"acc_norm\": 0.8639713204540929,\n\ \ \"acc_norm_stderr\": 0.0034211839093201708\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.0315469804508223,\n\ \ \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.0315469804508223\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544064,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544064\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.03116489966694862,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.03116489966694862\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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-college_mathematics|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.0356760379963917\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\":\ \ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.030783736757745653,\n\ \ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.030783736757745653\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\ \ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\ \ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947558,\n\ \ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947558\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8225806451612904,\n \"acc_stderr\": 0.02173254068932929,\n \"\ acc_norm\": 0.8225806451612904,\n \"acc_norm_stderr\": 0.02173254068932929\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n \"\ acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\"\ : 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.029311188674983137,\n\ \ \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.029311188674983137\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8888888888888888,\n \"acc_stderr\": 0.022390787638216773,\n \"\ acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.022390787638216773\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678178,\n\ \ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678178\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6948717948717948,\n \"acc_stderr\": 0.023346335293325884,\n\ \ \"acc_norm\": 0.6948717948717948,\n \"acc_norm_stderr\": 0.023346335293325884\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295894,\n \ \ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295894\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.45695364238410596,\n \"acc_stderr\": 0.04067325174247443,\n \"\ acc_norm\": 0.45695364238410596,\n \"acc_norm_stderr\": 0.04067325174247443\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9009174311926605,\n \"acc_stderr\": 0.01280978008187893,\n \"\ acc_norm\": 0.9009174311926605,\n \"acc_norm_stderr\": 0.01280978008187893\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6064814814814815,\n \"acc_stderr\": 0.03331747876370312,\n \"\ acc_norm\": 0.6064814814814815,\n \"acc_norm_stderr\": 0.03331747876370312\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9068627450980392,\n \"acc_stderr\": 0.020397853969427,\n \"acc_norm\"\ : 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969427\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.8945147679324894,\n \"acc_stderr\": 0.01999556072375854,\n \"\ acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.01999556072375854\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n\ \ \"acc_stderr\": 0.027373095500540193,\n \"acc_norm\": 0.7892376681614349,\n\ \ \"acc_norm_stderr\": 0.027373095500540193\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515375,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515375\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035196,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035196\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8220858895705522,\n \"acc_stderr\": 0.03004735765580663,\n\ \ \"acc_norm\": 0.8220858895705522,\n \"acc_norm_stderr\": 0.03004735765580663\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8697318007662835,\n\ \ \"acc_stderr\": 0.012036729568216054,\n \"acc_norm\": 0.8697318007662835,\n\ \ \"acc_norm_stderr\": 0.012036729568216054\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7832369942196532,\n \"acc_stderr\": 0.022183477668412856,\n\ \ \"acc_norm\": 0.7832369942196532,\n \"acc_norm_stderr\": 0.022183477668412856\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6100558659217877,\n\ \ \"acc_stderr\": 0.016312376629213067,\n \"acc_norm\": 0.6100558659217877,\n\ \ \"acc_norm_stderr\": 0.016312376629213067\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7556270096463023,\n\ \ \"acc_stderr\": 0.024406162094668886,\n \"acc_norm\": 0.7556270096463023,\n\ \ \"acc_norm_stderr\": 0.024406162094668886\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.02118589361522518,\n\ \ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.02118589361522518\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5531914893617021,\n \"acc_stderr\": 0.02965823509766691,\n \ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.02965823509766691\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.559973924380704,\n\ \ \"acc_stderr\": 0.012678037478574513,\n \"acc_norm\": 0.559973924380704,\n\ \ \"acc_norm_stderr\": 0.012678037478574513\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103142,\n\ \ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103142\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7630718954248366,\n \"acc_stderr\": 0.017201662169789772,\n \ \ \"acc_norm\": 0.7630718954248366,\n \"acc_norm_stderr\": 0.017201662169789772\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\ \ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\ \ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8081632653061225,\n \"acc_stderr\": 0.025206963154225392,\n\ \ \"acc_norm\": 0.8081632653061225,\n \"acc_norm_stderr\": 0.025206963154225392\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\ \ \"acc_stderr\": 0.024112678240900798,\n \"acc_norm\": 0.8656716417910447,\n\ \ \"acc_norm_stderr\": 0.024112678240900798\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.02567934272327692,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.02567934272327692\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4394124847001224,\n\ \ \"mc1_stderr\": 0.01737452048251371,\n \"mc2\": 0.6140893594618404,\n\ \ \"mc2_stderr\": 0.014827391515273696\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8318863456985004,\n \"acc_stderr\": 0.010510336954166723\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5663381349507203,\n \ \ \"acc_stderr\": 0.013650728047064688\n }\n}\n```" repo_url: https://huggingface.co/leejunhyeok/MoMo-70B-LoRA-V1.2_1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|arc:challenge|25_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-11T00-49-27.690522.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|gsm8k|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hellaswag|10_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-49-27.690522.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-49-27.690522.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T00-49-27.690522.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T00_49_27.690522 path: - '**/details_harness|winogrande|5_2023-12-11T00-49-27.690522.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-11T00-49-27.690522.parquet' - config_name: results data_files: - split: 2023_12_11T00_49_27.690522 path: - results_2023-12-11T00-49-27.690522.parquet - split: latest path: - results_2023-12-11T00-49-27.690522.parquet --- # Dataset Card for Evaluation run of leejunhyeok/MoMo-70B-LoRA-V1.2_1 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/leejunhyeok/MoMo-70B-LoRA-V1.2_1 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [leejunhyeok/MoMo-70B-LoRA-V1.2_1](https://huggingface.co/leejunhyeok/MoMo-70B-LoRA-V1.2_1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T00:49:27.690522](https://huggingface.co/datasets/open-llm-leaderboard/details_leejunhyeok__MoMo-70B-LoRA-V1.2_1/blob/main/results_2023-12-11T00-49-27.690522.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": 0.6982187092414253, "acc_stderr": 0.03042209430227255, "acc_norm": 0.7019460256253881, "acc_norm_stderr": 0.03101566845962337, "mc1": 0.4394124847001224, "mc1_stderr": 0.01737452048251371, "mc2": 0.6140893594618404, "mc2_stderr": 0.014827391515273696 }, "harness|arc:challenge|25": { "acc": 0.6715017064846417, "acc_stderr": 0.0137249784655373, "acc_norm": 0.7064846416382252, "acc_norm_stderr": 0.013307250444941115 }, "harness|hellaswag|10": { "acc": 0.6772555267874926, "acc_stderr": 0.004665704208339046, "acc_norm": 0.8639713204540929, "acc_norm_stderr": 0.0034211839093201708 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.04171654161354543, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.04171654161354543 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8157894736842105, "acc_stderr": 0.0315469804508223, "acc_norm": 0.8157894736842105, "acc_norm_stderr": 0.0315469804508223 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544064, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544064 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8333333333333334, "acc_stderr": 0.03116489966694862, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.03116489966694862 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "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": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6680851063829787, "acc_stderr": 0.030783736757745653, "acc_norm": 0.6680851063829787, "acc_norm_stderr": 0.030783736757745653 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4298245614035088, "acc_stderr": 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"harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175008, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.04229525846816505, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8303030303030303, "acc_stderr": 0.029311188674983137, "acc_norm": 0.8303030303030303, "acc_norm_stderr": 0.029311188674983137 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8888888888888888, "acc_stderr": 0.022390787638216773, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.022390787638216773 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.927461139896373, "acc_stderr": 0.018718998520678178, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.018718998520678178 }, "harness|hendrycksTest-high_school_macroeconomics|5": { 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"harness|truthfulqa:mc|0": { "mc1": 0.4394124847001224, "mc1_stderr": 0.01737452048251371, "mc2": 0.6140893594618404, "mc2_stderr": 0.014827391515273696 }, "harness|winogrande|5": { "acc": 0.8318863456985004, "acc_stderr": 0.010510336954166723 }, "harness|gsm8k|5": { "acc": 0.5663381349507203, "acc_stderr": 0.013650728047064688 } } ``` ### 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]
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open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03
open-llm-leaderboard
2023-12-11T01:11:42Z
0
0
[ "region:us" ]
null
2023-12-11T01:11:01Z
--- pretty_name: Evaluation run of perlthoughts/Chupacabra-7B-v2.03 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [perlthoughts/Chupacabra-7B-v2.03](https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03)\ \ 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_perlthoughts__Chupacabra-7B-v2.03\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T01:08:10.868540](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03/blob/main/results_2023-12-11T01-08-10.868540.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\": 0.6311240267277043,\n\ \ \"acc_stderr\": 0.032483219688012585,\n \"acc_norm\": 0.6342658628547596,\n\ \ \"acc_norm_stderr\": 0.033138776879109225,\n \"mc1\": 0.33659730722154224,\n\ \ \"mc1_stderr\": 0.016542412809494887,\n \"mc2\": 0.48533561495565075,\n\ \ \"mc2_stderr\": 0.015259691745766833\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5981228668941979,\n \"acc_stderr\": 0.014327268614578274,\n\ \ \"acc_norm\": 0.6382252559726962,\n \"acc_norm_stderr\": 0.014041957945038076\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6450906193985262,\n\ \ \"acc_stderr\": 0.004775079636567097,\n \"acc_norm\": 0.8473411670981876,\n\ \ \"acc_norm_stderr\": 0.0035892328893065146\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.04244633238353227\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493857,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493857\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\ \ \"acc_stderr\": 0.037161774375660164,\n \"acc_norm\": 0.7291666666666666,\n\ \ \"acc_norm_stderr\": 0.037161774375660164\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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_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\": 0.6358381502890174,\n\ \ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\ \ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.8,\n \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5172413793103449,\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\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7709677419354839,\n \"acc_stderr\": 0.023904914311782658,\n \"\ acc_norm\": 0.7709677419354839,\n \"acc_norm_stderr\": 0.023904914311782658\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n \"\ acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386417,\n \"\ acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386417\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\ \ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.024537591572830503,\n\ \ \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.024537591572830503\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \ \ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\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\"\ : 0.41721854304635764,\n \"acc_stderr\": 0.040261414976346104,\n \"\ acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.040261414976346104\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374294,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374294\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\ acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"\ acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944863,\n \ \ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944863\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\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\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615624,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615624\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\ \ \"acc_stderr\": 0.023902325549560417,\n \"acc_norm\": 0.8418803418803419,\n\ \ \"acc_norm_stderr\": 0.023902325549560417\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n\ \ \"acc_stderr\": 0.013740797258579828,\n \"acc_norm\": 0.8199233716475096,\n\ \ \"acc_norm_stderr\": 0.013740797258579828\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\ \ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3452513966480447,\n\ \ \"acc_stderr\": 0.01590143260893036,\n \"acc_norm\": 0.3452513966480447,\n\ \ \"acc_norm_stderr\": 0.01590143260893036\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.02617390850671858,\n\ \ \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.02617390850671858\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.02492200116888633,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.02492200116888633\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \ \ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6433823529411765,\n \"acc_stderr\": 0.02909720956841195,\n\ \ \"acc_norm\": 0.6433823529411765,\n \"acc_norm_stderr\": 0.02909720956841195\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6388888888888888,\n \"acc_stderr\": 0.01943177567703731,\n \ \ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.01943177567703731\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\ \ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\ \ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.673469387755102,\n \"acc_stderr\": 0.030021056238440303,\n\ \ \"acc_norm\": 0.673469387755102,\n \"acc_norm_stderr\": 0.030021056238440303\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\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.33659730722154224,\n\ \ \"mc1_stderr\": 0.016542412809494887,\n \"mc2\": 0.48533561495565075,\n\ \ \"mc2_stderr\": 0.015259691745766833\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8089976322020521,\n \"acc_stderr\": 0.011047808761510432\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.510235026535254,\n \ \ \"acc_stderr\": 0.013769598923012388\n }\n}\n```" repo_url: https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|arc:challenge|25_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-11T01-08-10.868540.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|gsm8k|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hellaswag|10_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-08-10.868540.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-08-10.868540.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T01-08-10.868540.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T01_08_10.868540 path: - '**/details_harness|winogrande|5_2023-12-11T01-08-10.868540.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-11T01-08-10.868540.parquet' - config_name: results data_files: - split: 2023_12_11T01_08_10.868540 path: - results_2023-12-11T01-08-10.868540.parquet - split: latest path: - results_2023-12-11T01-08-10.868540.parquet --- # Dataset Card for Evaluation run of perlthoughts/Chupacabra-7B-v2.03 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [perlthoughts/Chupacabra-7B-v2.03](https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03) 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_perlthoughts__Chupacabra-7B-v2.03", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T01:08:10.868540](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03/blob/main/results_2023-12-11T01-08-10.868540.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": 0.6311240267277043, "acc_stderr": 0.032483219688012585, "acc_norm": 0.6342658628547596, "acc_norm_stderr": 0.033138776879109225, "mc1": 0.33659730722154224, "mc1_stderr": 0.016542412809494887, "mc2": 0.48533561495565075, "mc2_stderr": 0.015259691745766833 }, "harness|arc:challenge|25": { "acc": 0.5981228668941979, "acc_stderr": 0.014327268614578274, "acc_norm": 0.6382252559726962, "acc_norm_stderr": 0.014041957945038076 }, "harness|hellaswag|10": { "acc": 0.6450906193985262, "acc_stderr": 0.004775079636567097, "acc_norm": 0.8473411670981876, "acc_norm_stderr": 0.0035892328893065146 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.04244633238353227, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.04244633238353227 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6644736842105263, "acc_stderr": 0.038424985593952694, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.038424985593952694 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.048523658709391, "acc_norm": 0.63, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493857, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493857 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7291666666666666, "acc_stderr": 0.037161774375660164, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.037161774375660164 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "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": 0.6358381502890174, "acc_stderr": 0.03669072477416907, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416907 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.8, "acc_stderr": 0.040201512610368445, "acc_norm": 0.8, "acc_norm_stderr": 0.040201512610368445 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4473684210526316, "acc_stderr": 0.04677473004491199, "acc_norm": 0.4473684210526316, "acc_norm_stderr": 0.04677473004491199 }, "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": 0.42328042328042326, "acc_stderr": 0.02544636563440678, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440678 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677171, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677171 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7709677419354839, "acc_stderr": 0.023904914311782658, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.023904914311782658 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5320197044334976, "acc_stderr": 0.035107665979592154, "acc_norm": 0.5320197044334976, "acc_norm_stderr": 0.035107665979592154 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7727272727272727, "acc_stderr": 0.029857515673386417, "acc_norm": 0.7727272727272727, "acc_norm_stderr": 0.029857515673386417 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.02381447708659355, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.02381447708659355 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6256410256410256, "acc_stderr": 0.024537591572830503, "acc_norm": 0.6256410256410256, "acc_norm_stderr": 0.024537591572830503 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028593, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028593 }, "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": 0.41721854304635764, "acc_stderr": 0.040261414976346104, "acc_norm": 0.41721854304635764, "acc_norm_stderr": 0.040261414976346104 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374294, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374294 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639318, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639318 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8143459915611815, "acc_stderr": 0.025310495376944863, "acc_norm": 0.8143459915611815, "acc_norm_stderr": 0.025310495376944863 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7361963190184049, "acc_stderr": 0.03462419931615624, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.03462419931615624 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.41964285714285715, "acc_stderr": 0.04684099321077106, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.04684099321077106 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8418803418803419, "acc_stderr": 0.023902325549560417, "acc_norm": 0.8418803418803419, "acc_norm_stderr": 0.023902325549560417 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 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"harness|hendrycksTest-prehistory|5": { "acc": 0.7222222222222222, "acc_stderr": 0.02492200116888633, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.02492200116888633 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.44680851063829785, "acc_stderr": 0.029658235097666907, "acc_norm": 0.44680851063829785, "acc_norm_stderr": 0.029658235097666907 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303957, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303957 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6433823529411765, "acc_stderr": 0.02909720956841195, "acc_norm": 0.6433823529411765, "acc_norm_stderr": 0.02909720956841195 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6388888888888888, "acc_stderr": 0.01943177567703731, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.01943177567703731 }, "harness|hendrycksTest-public_relations|5": { "acc": 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"harness|truthfulqa:mc|0": { "mc1": 0.33659730722154224, "mc1_stderr": 0.016542412809494887, "mc2": 0.48533561495565075, "mc2_stderr": 0.015259691745766833 }, "harness|winogrande|5": { "acc": 0.8089976322020521, "acc_stderr": 0.011047808761510432 }, "harness|gsm8k|5": { "acc": 0.510235026535254, "acc_stderr": 0.013769598923012388 } } ``` ### 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]
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open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03-128k
open-llm-leaderboard
2023-12-11T01:15:11Z
0
0
[ "region:us" ]
null
2023-12-11T01:14:28Z
--- pretty_name: Evaluation run of perlthoughts/Chupacabra-7B-v2.03-128k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [perlthoughts/Chupacabra-7B-v2.03-128k](https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03-128k)\ \ 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_perlthoughts__Chupacabra-7B-v2.03-128k\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T01:11:36.377069](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03-128k/blob/main/results_2023-12-11T01-11-36.377069.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\": 0.6308159681070862,\n\ \ \"acc_stderr\": 0.03241075436743821,\n \"acc_norm\": 0.6340939515561961,\n\ \ \"acc_norm_stderr\": 0.03306369804375197,\n \"mc1\": 0.35495716034271724,\n\ \ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5116101369020933,\n\ \ \"mc2_stderr\": 0.015317262236176729\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6015358361774744,\n \"acc_stderr\": 0.014306946052735565,\n\ \ \"acc_norm\": 0.6467576791808873,\n \"acc_norm_stderr\": 0.013967822714840056\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6435968930491934,\n\ \ \"acc_stderr\": 0.004779574402771385,\n \"acc_norm\": 0.8456482772356104,\n\ \ \"acc_norm_stderr\": 0.0036054721167622923\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\ \ \"acc_stderr\": 0.042446332383532265,\n \"acc_norm\": 0.5925925925925926,\n\ \ \"acc_norm_stderr\": 0.042446332383532265\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.038781398887976104,\n\ \ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.038781398887976104\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.049020713000019756,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.049020713000019756\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\ \ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\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\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6184971098265896,\n\ \ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n\ \ \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\ \ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.04615186962583703,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.04615186962583703\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\ \ \"acc_stderr\": 0.02390491431178265,\n \"acc_norm\": 0.7709677419354839,\n\ \ \"acc_norm_stderr\": 0.02390491431178265\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047712,\n\ \ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047712\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593556,\n\ \ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593556\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6435897435897436,\n \"acc_stderr\": 0.024283140529467305,\n\ \ \"acc_norm\": 0.6435897435897436,\n \"acc_norm_stderr\": 0.024283140529467305\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114996,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114996\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \ \ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650154,\n \"\ acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650154\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\"\ : 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474086,\n \"\ acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474086\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233504,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233504\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\ \ \"acc_stderr\": 0.030636591348699803,\n \"acc_norm\": 0.7040358744394619,\n\ \ \"acc_norm_stderr\": 0.030636591348699803\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516304,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516304\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8461538461538461,\n\ \ \"acc_stderr\": 0.02363687331748928,\n \"acc_norm\": 0.8461538461538461,\n\ \ \"acc_norm_stderr\": 0.02363687331748928\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\": 0.8212005108556832,\n\ \ \"acc_stderr\": 0.013702643715368985,\n \"acc_norm\": 0.8212005108556832,\n\ \ \"acc_norm_stderr\": 0.013702643715368985\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\ \ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31843575418994413,\n\ \ \"acc_stderr\": 0.015581008080360276,\n \"acc_norm\": 0.31843575418994413,\n\ \ \"acc_norm_stderr\": 0.015581008080360276\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7026143790849673,\n \"acc_stderr\": 0.02617390850671858,\n\ \ \"acc_norm\": 0.7026143790849673,\n \"acc_norm_stderr\": 0.02617390850671858\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.02465968518596728,\n\ \ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.02465968518596728\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873862,\n \ \ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873862\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4661016949152542,\n\ \ \"acc_stderr\": 0.012740853872949832,\n \"acc_norm\": 0.4661016949152542,\n\ \ \"acc_norm_stderr\": 0.012740853872949832\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681397,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681397\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6552287581699346,\n \"acc_stderr\": 0.019228322018696644,\n \ \ \"acc_norm\": 0.6552287581699346,\n \"acc_norm_stderr\": 0.019228322018696644\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484375,\n\ \ \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484375\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\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\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35495716034271724,\n\ \ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.5116101369020933,\n\ \ \"mc2_stderr\": 0.015317262236176729\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8105761641673244,\n \"acc_stderr\": 0.011012790432989248\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5049279757391963,\n \ \ \"acc_stderr\": 0.01377181577547058\n }\n}\n```" repo_url: https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03-128k leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|arc:challenge|25_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-11T01-11-36.377069.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|gsm8k|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hellaswag|10_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-11-36.377069.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-11-36.377069.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T01-11-36.377069.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T01_11_36.377069 path: - '**/details_harness|winogrande|5_2023-12-11T01-11-36.377069.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-11T01-11-36.377069.parquet' - config_name: results data_files: - split: 2023_12_11T01_11_36.377069 path: - results_2023-12-11T01-11-36.377069.parquet - split: latest path: - results_2023-12-11T01-11-36.377069.parquet --- # Dataset Card for Evaluation run of perlthoughts/Chupacabra-7B-v2.03-128k ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03-128k - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [perlthoughts/Chupacabra-7B-v2.03-128k](https://huggingface.co/perlthoughts/Chupacabra-7B-v2.03-128k) 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_perlthoughts__Chupacabra-7B-v2.03-128k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T01:11:36.377069](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__Chupacabra-7B-v2.03-128k/blob/main/results_2023-12-11T01-11-36.377069.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": 0.6308159681070862, "acc_stderr": 0.03241075436743821, "acc_norm": 0.6340939515561961, "acc_norm_stderr": 0.03306369804375197, "mc1": 0.35495716034271724, "mc1_stderr": 0.0167508623813759, "mc2": 0.5116101369020933, "mc2_stderr": 0.015317262236176729 }, "harness|arc:challenge|25": { "acc": 0.6015358361774744, "acc_stderr": 0.014306946052735565, "acc_norm": 0.6467576791808873, "acc_norm_stderr": 0.013967822714840056 }, "harness|hellaswag|10": { "acc": 0.6435968930491934, "acc_stderr": 0.004779574402771385, "acc_norm": 0.8456482772356104, "acc_norm_stderr": 0.0036054721167622923 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.042446332383532265, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.042446332383532265 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6513157894736842, "acc_stderr": 0.038781398887976104, "acc_norm": 0.6513157894736842, "acc_norm_stderr": 0.038781398887976104 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.049020713000019756, "acc_norm": 0.61, "acc_norm_stderr": 0.049020713000019756 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6943396226415094, "acc_stderr": 0.028353298073322666, "acc_norm": 0.6943396226415094, "acc_norm_stderr": 0.028353298073322666 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7430555555555556, "acc_stderr": 0.03653946969442099, "acc_norm": 0.7430555555555556, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "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": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6184971098265896, "acc_stderr": 0.03703851193099521, "acc_norm": 0.6184971098265896, "acc_norm_stderr": 0.03703851193099521 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.04878608714466996, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.04878608714466996 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.40350877192982454, "acc_stderr": 0.04615186962583703, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.04615186962583703 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4444444444444444, "acc_stderr": 0.025591857761382182, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.025591857761382182 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7709677419354839, "acc_stderr": 0.02390491431178265, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.02390491431178265 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.03517945038691063, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.793939393939394, "acc_stderr": 0.03158415324047712, "acc_norm": 0.793939393939394, "acc_norm_stderr": 0.03158415324047712 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.028869778460267042, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.028869778460267042 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8756476683937824, "acc_stderr": 0.023814477086593556, "acc_norm": 0.8756476683937824, "acc_norm_stderr": 0.023814477086593556 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 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0.03941897526516304 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.047184714852195886, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.047184714852195886 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8461538461538461, "acc_stderr": 0.02363687331748928, "acc_norm": 0.8461538461538461, "acc_norm_stderr": 0.02363687331748928 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 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"harness|hendrycksTest-prehistory|5": { "acc": 0.7314814814814815, "acc_stderr": 0.02465968518596728, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.02465968518596728 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873862, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873862 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4661016949152542, "acc_stderr": 0.012740853872949832, "acc_norm": 0.4661016949152542, "acc_norm_stderr": 0.012740853872949832 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681397, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681397 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6552287581699346, "acc_stderr": 0.019228322018696644, "acc_norm": 0.6552287581699346, "acc_norm_stderr": 0.019228322018696644 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.689795918367347, "acc_stderr": 0.029613459872484375, "acc_norm": 0.689795918367347, "acc_norm_stderr": 0.029613459872484375 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.89, "acc_stderr": 0.03144660377352203, "acc_norm": 0.89, "acc_norm_stderr": 0.03144660377352203 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness|truthfulqa:mc|0": { "mc1": 0.35495716034271724, "mc1_stderr": 0.0167508623813759, "mc2": 0.5116101369020933, "mc2_stderr": 0.015317262236176729 }, "harness|winogrande|5": { "acc": 0.8105761641673244, "acc_stderr": 0.011012790432989248 }, "harness|gsm8k|5": { "acc": 0.5049279757391963, "acc_stderr": 0.01377181577547058 } } ``` ### 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]
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gsm8k
null
2023-12-12T13:28:03Z
222,956
135
[ "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:mit", "math-word-problems", "arxiv:2110.14168", "region:us" ]
[ "text2text-generation" ]
2022-04-12T10:22:10Z
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: gsm8k pretty_name: Grade School Math 8K tags: - math-word-problems dataset_info: - config_name: main features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 3963202 num_examples: 7473 - name: test num_bytes: 713732 num_examples: 1319 download_size: 4915944 dataset_size: 4676934 - config_name: socratic features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 5198108 num_examples: 7473 - name: test num_bytes: 936859 num_examples: 1319 download_size: 6374717 dataset_size: 6134967 --- # Dataset Card for GSM8K ## 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://openai.com/blog/grade-school-math/ - **Repository:** https://github.com/openai/grade-school-math - **Paper:** https://arxiv.org/abs/2110.14168 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. - These problems take between 2 and 8 steps to solve. - Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. - A bright middle school student should be able to solve every problem: from the paper, "Problems require no concepts beyond the level of early Algebra, and the vast majority of problems can be solved without explicitly defining a variable." - Solutions are provided in natural language, as opposed to pure math expressions. From the paper: "We believe this is the most generally useful data format, and we expect it to shed light on the properties of large language models’ internal monologues"" ### Supported Tasks and Leaderboards This dataset is generally used to test logic and math in language modelling. It has been used for many benchmarks, including the [LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). ### Languages The text in the dataset is in English. The associated BCP-47 code is `en`. ## Dataset Structure ### Data Instances For the `main` configuration, each instance contains a string for the grade-school level math question and a string for the corresponding answer with multiple steps of reasoning and calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)). ```python { 'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?', 'answer': 'Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72', } ``` For the `socratic` configuration, each instance contains a string for a grade-school level math question, a string for the corresponding answer with multiple steps of reasoning, calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)), and *Socratic sub-questions*. ```python { 'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?', 'answer': 'How many clips did Natalia sell in May? ** Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nHow many clips did Natalia sell altogether in April and May? ** Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72', } ``` ### Data Fields The data fields are the same among `main` and `socratic` configurations and their individual splits. - question: The question string to a grade school math problem. - answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution. ### Data Splits | name |train|validation| |--------|----:|---------:| |main | 7473| 1319| |socratic| 7473| 1319| ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization From the paper, appendix A: > We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors. #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? Surge AI (surgehq.ai) ### 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 The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information ```bibtex @article{cobbe2021gsm8k, title={Training Verifiers to Solve Math Word Problems}, author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John}, journal={arXiv preprint arXiv:2110.14168}, year={2021} } ``` ### Contributions Thanks to [@jon-tow](https://github.com/jon-tow) for adding this dataset.
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derek-thomas/dataset-creator-reddit-bestofredditorupdates
derek-thomas
2023-12-17T05:00:56Z
28,480
0
[ "region:us" ]
null
2023-11-17T07:44:26Z
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: content dtype: string - name: score dtype: int64 - name: date_utc dtype: timestamp[ns] - name: title dtype: string - name: flair dtype: string - name: poster dtype: string - name: permalink dtype: string - name: updated dtype: bool - name: new dtype: bool splits: - name: train num_bytes: 63816139 num_examples: 10266 download_size: 37194431 dataset_size: 63816139 --- # Dataset Card for "dataset-creator-reddit-bestofredditorupdates" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) --- Generated Part of README Below --- ## Dataset Overview The goal is to have an open dataset of [r/bestofredditorupdates](https://www.reddit.com/r/bestofredditorupdates/) submissions. I'm leveraging PRAW and the Reddit API to get downloads. There is a limit of 1000 in an API call and limited search functionality, so this is run daily to get new submissions. ## Creation Details This dataset was created by [derek-thomas/dataset-creator-reddit-bestofredditorupdates](https://huggingface.co/spaces/derek-thomas/dataset-creator-reddit-bestofredditorupdates) ## Update Frequency The dataset is updated daily with the most recent update being `2023-12-17 05:00:00 UTC+0000` where we added **12 new rows**. ## Licensing [Reddit Licensing terms](https://www.redditinc.com/policies/data-api-terms) as accessed on October 25: [License information] ## Opt-out To opt-out of this dataset please make a request in the community tab
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MMMU/MMMU
MMMU
2023-12-16T02:33:30Z
7,800
74
[ "task_categories:question-answering", "task_categories:visual-question-answering", "task_categories:multiple-choice", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "biology", "medical", "finance", "chemistry", "music", "art", "art_theory", "design", "business", "accounting", "economics", "manage", "marketing", "health", "medicine", "basic_medical_science", "clinical", "pharmacy", "public_health", "humanities", "social_science", "history", "literature", "sociology", "psychology", "science", "geography", "math", "physics", "engineering", "agriculture", "architecture", "computer_science", "electronics", "energy_and_power", "materials", "mechanical_engineering", "arxiv:2311.16502", "region:us" ]
[ "question-answering", "visual-question-answering", "multiple-choice" ]
2023-11-27T17:52:01Z
--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - question-answering - visual-question-answering - multiple-choice pretty_name: mmmu dataset_info: - config_name: Accounting features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262599.0 num_examples: 5 - name: validation num_bytes: 1598285.0 num_examples: 30 - name: test num_bytes: 22135625.0 num_examples: 380 download_size: 37363379 dataset_size: 23996509.0 - config_name: Agriculture features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 22082656.0 num_examples: 5 - name: validation num_bytes: 119217558.0 num_examples: 30 - name: test num_bytes: 993664077.0 num_examples: 287 download_size: 1158036990 dataset_size: 1134964291.0 - config_name: Architecture_and_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 137750.0 num_examples: 5 - name: validation num_bytes: 721378.0 num_examples: 30 - name: test num_bytes: 16054607.0 num_examples: 551 download_size: 48763955 dataset_size: 16913735.0 - config_name: Art features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 6241184.0 num_examples: 5 - name: validation num_bytes: 29934534.0 num_examples: 30 - name: test num_bytes: 237801390.0 num_examples: 231 download_size: 585798641 dataset_size: 273977108.0 - config_name: Art_Theory features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 7435106.0 num_examples: 5 - name: validation num_bytes: 33481558.0 num_examples: 30 - name: test num_bytes: 553174647.0 num_examples: 429 download_size: 930525695 dataset_size: 594091311.0 - config_name: Basic_Medical_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 814310.0 num_examples: 5 - name: validation num_bytes: 4125930.0 num_examples: 30 - name: test num_bytes: 48125891.0 num_examples: 326 download_size: 84666454 dataset_size: 53066131.0 - config_name: Biology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 574342.0 num_examples: 5 - name: validation num_bytes: 8491863.0 num_examples: 30 - name: test num_bytes: 132966151.0 num_examples: 345 download_size: 410242502 dataset_size: 142032356.0 - config_name: Chemistry features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 262397.0 num_examples: 5 - name: validation num_bytes: 1518573.0 num_examples: 30 - name: test num_bytes: 37219529.0 num_examples: 603 download_size: 108345562 dataset_size: 39000499.0 - config_name: Clinical_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1467945.0 num_examples: 5 - name: validation num_bytes: 10882484.0 num_examples: 30 - name: test num_bytes: 98201863.0 num_examples: 325 download_size: 160611488 dataset_size: 110552292.0 - config_name: Computer_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 440523.0 num_examples: 5 - name: validation num_bytes: 2072018.0 num_examples: 30 - name: test num_bytes: 32047381.0 num_examples: 371 download_size: 55640991 dataset_size: 34559922.0 - config_name: Design features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2259873.0 num_examples: 5 - name: validation num_bytes: 17923120.0 num_examples: 30 - name: test num_bytes: 77676331.0 num_examples: 169 download_size: 142866617 dataset_size: 97859324.0 - config_name: Diagnostics_and_Laboratory_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 2056117.0 num_examples: 5 - name: validation num_bytes: 37106233.0 num_examples: 30 - name: test num_bytes: 157003069.0 num_examples: 162 download_size: 603957093 dataset_size: 196165419.0 - config_name: Economics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 171434.0 num_examples: 5 - name: validation num_bytes: 1487048.0 num_examples: 30 - name: test num_bytes: 11852300.0 num_examples: 267 download_size: 20777635 dataset_size: 13510782.0 - config_name: Electronics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 123632.0 num_examples: 5 - name: validation num_bytes: 641377.0 num_examples: 30 - name: test num_bytes: 5717686.0 num_examples: 256 download_size: 11602832 dataset_size: 6482695.0 - config_name: Energy_and_Power features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 105006.0 num_examples: 5 - name: validation num_bytes: 1641935.0 num_examples: 30 - name: test num_bytes: 14748428.0 num_examples: 432 download_size: 35246567 dataset_size: 16495369.0 - config_name: Finance features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 296124.0 num_examples: 5 - name: validation num_bytes: 1071060.0 num_examples: 30 - name: test num_bytes: 12065803.0 num_examples: 355 download_size: 29551521 dataset_size: 13432987.0 - config_name: Geography features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - 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config_name: Manage features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 449514.0 num_examples: 5 - name: validation num_bytes: 3277436.0 num_examples: 30 - name: test num_bytes: 29963963.0 num_examples: 245 download_size: 51186888 dataset_size: 33690913.0 - config_name: Marketing features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 116960.0 num_examples: 5 - name: validation num_bytes: 1472981.0 num_examples: 30 - name: test num_bytes: 7732976.0 num_examples: 181 download_size: 13146078 dataset_size: 9322917.0 - config_name: Materials features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 239632.0 num_examples: 5 - name: validation num_bytes: 2305211.0 num_examples: 30 - name: test num_bytes: 25256854.0 num_examples: 458 download_size: 78365794 dataset_size: 27801697.0 - config_name: Math features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 175861.0 num_examples: 5 - name: validation num_bytes: 1444458.0 num_examples: 30 - name: test num_bytes: 27701878.0 num_examples: 505 download_size: 89368153 dataset_size: 29322197.0 - config_name: Mechanical_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 152542.0 num_examples: 5 - name: validation num_bytes: 874988.0 num_examples: 30 - name: test num_bytes: 15093746.0 num_examples: 429 download_size: 30450114 dataset_size: 16121276.0 - config_name: Music features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 1417615.0 num_examples: 5 - name: validation num_bytes: 9359372.0 num_examples: 30 - name: test num_bytes: 134096770.0 num_examples: 334 download_size: 174725052 dataset_size: 144873757.0 - config_name: Pharmacy features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 207924.0 num_examples: 5 - name: validation num_bytes: 1656342.0 num_examples: 30 - name: test num_bytes: 31866248.0 num_examples: 430 download_size: 62721263 dataset_size: 33730514.0 - config_name: Physics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 233734.0 num_examples: 5 - name: validation num_bytes: 1114130.0 num_examples: 30 - name: test num_bytes: 15905705.0 num_examples: 408 download_size: 35238571 dataset_size: 17253569.0 - config_name: Psychology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 600864.0 num_examples: 5 - name: validation num_bytes: 4403886.0 num_examples: 30 - name: test num_bytes: 53813915.0 num_examples: 305 download_size: 102466671 dataset_size: 58818665.0 - config_name: Public_Health features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 234781.0 num_examples: 5 - name: validation num_bytes: 1508761.0 num_examples: 30 - name: test num_bytes: 32150088.0 num_examples: 509 download_size: 48231609 dataset_size: 33893630.0 - config_name: Sociology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: dev num_bytes: 3769220.0 num_examples: 5 - name: validation num_bytes: 18455336.0 num_examples: 30 - name: test num_bytes: 144301123.0 num_examples: 252 download_size: 310313826 dataset_size: 166525679.0 configs: - config_name: Accounting data_files: - split: dev path: Accounting/dev-* - split: validation path: Accounting/validation-* - split: test path: Accounting/test-* - config_name: Agriculture data_files: - split: dev path: Agriculture/dev-* - split: validation path: Agriculture/validation-* - split: test path: Agriculture/test-* - config_name: Architecture_and_Engineering data_files: - split: dev path: Architecture_and_Engineering/dev-* - split: validation path: Architecture_and_Engineering/validation-* - split: test path: Architecture_and_Engineering/test-* - config_name: Art data_files: - split: dev path: Art/dev-* - split: validation path: Art/validation-* - split: test path: Art/test-* - config_name: Art_Theory data_files: - split: dev path: Art_Theory/dev-* - split: validation path: Art_Theory/validation-* - split: test path: Art_Theory/test-* - config_name: Basic_Medical_Science data_files: - split: dev path: Basic_Medical_Science/dev-* - split: validation path: Basic_Medical_Science/validation-* - split: test path: Basic_Medical_Science/test-* - config_name: Biology data_files: - split: dev path: Biology/dev-* - split: validation path: Biology/validation-* - split: test path: Biology/test-* - config_name: Chemistry data_files: - split: dev path: Chemistry/dev-* - split: validation path: Chemistry/validation-* - split: test path: Chemistry/test-* - config_name: Clinical_Medicine data_files: - split: dev path: Clinical_Medicine/dev-* - split: validation path: Clinical_Medicine/validation-* - split: test path: Clinical_Medicine/test-* - config_name: Computer_Science data_files: - split: dev path: Computer_Science/dev-* - split: validation path: Computer_Science/validation-* - split: test path: Computer_Science/test-* - config_name: Design data_files: - split: dev path: Design/dev-* - split: validation path: Design/validation-* - split: test path: Design/test-* - config_name: Diagnostics_and_Laboratory_Medicine data_files: - split: dev path: Diagnostics_and_Laboratory_Medicine/dev-* - split: validation path: Diagnostics_and_Laboratory_Medicine/validation-* - split: test path: Diagnostics_and_Laboratory_Medicine/test-* - config_name: Economics data_files: - split: dev path: Economics/dev-* - split: validation path: Economics/validation-* - split: test path: Economics/test-* - config_name: Electronics data_files: - split: dev path: Electronics/dev-* - split: validation path: Electronics/validation-* - split: test path: Electronics/test-* - config_name: Energy_and_Power data_files: - split: dev path: Energy_and_Power/dev-* - split: validation path: Energy_and_Power/validation-* - split: test path: Energy_and_Power/test-* - config_name: Finance data_files: - split: dev path: Finance/dev-* - split: validation path: Finance/validation-* - split: test path: Finance/test-* - config_name: Geography data_files: - split: dev path: Geography/dev-* - split: validation path: Geography/validation-* - split: test path: Geography/test-* - config_name: History data_files: - split: dev path: History/dev-* - split: validation path: History/validation-* - split: test path: History/test-* - config_name: Literature data_files: - split: dev path: Literature/dev-* - split: validation path: Literature/validation-* - split: test path: Literature/test-* - config_name: Manage data_files: - split: dev path: Manage/dev-* - split: validation path: Manage/validation-* - split: test path: Manage/test-* - config_name: Marketing data_files: - split: dev path: Marketing/dev-* - split: validation path: Marketing/validation-* - split: test path: Marketing/test-* - config_name: Materials data_files: - split: dev path: Materials/dev-* - split: validation path: Materials/validation-* - split: test path: Materials/test-* - config_name: Math data_files: - split: dev path: Math/dev-* - split: validation path: Math/validation-* - split: test path: Math/test-* - config_name: Mechanical_Engineering data_files: - split: dev path: Mechanical_Engineering/dev-* - split: validation path: Mechanical_Engineering/validation-* - split: test path: Mechanical_Engineering/test-* - config_name: Music data_files: - split: dev path: Music/dev-* - split: validation path: Music/validation-* - split: test path: Music/test-* - config_name: Pharmacy data_files: - split: dev path: Pharmacy/dev-* - split: validation path: Pharmacy/validation-* - split: test path: Pharmacy/test-* - config_name: Physics data_files: - split: dev path: Physics/dev-* - split: validation path: Physics/validation-* - split: test path: Physics/test-* - config_name: Psychology data_files: - split: dev path: Psychology/dev-* - split: validation path: Psychology/validation-* - split: test path: Psychology/test-* - config_name: Public_Health data_files: - split: dev path: Public_Health/dev-* - split: validation path: Public_Health/validation-* - split: test path: Public_Health/test-* - config_name: Sociology data_files: - split: dev path: Sociology/dev-* - split: validation path: Sociology/validation-* - split: test path: Sociology/test-* tags: - biology - medical - finance - chemistry - music - art - art_theory - design - music - business - accounting - economics - finance - manage - marketing - health - medicine - basic_medical_science - clinical - pharmacy - public_health - humanities - social_science - history - literature - sociology - psychology - science - biology - chemistry - geography - math - physics - engineering - agriculture - architecture - computer_science - electronics - energy_and_power - materials - mechanical_engineering --- # MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI) [**🌐 Homepage**](https://mmmu-benchmark.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/MMMU/MMMU/) | [**🤗 Paper**](https://huggingface.co/papers/2311.16502) | [**📖 arXiv**](https://arxiv.org/abs/2311.16502) | [**GitHub**](https://github.com/MMMU-Benchmark/MMMU) ## 🔔News - **🔥[2023-12-04]: Our evaluation server for test set is now availble on [EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview). We welcome all submissions and look forward to your participation! 😆** ## Dataset Details ### Dataset Description We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes **11.5K meticulously collected multimodal questions** from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering. These questions span **30 subjects** and **183 subfields**, comprising **30 highly heterogeneous image types**, such as charts, diagrams, maps, tables, music sheets, and chemical structures. We believe MMMU will stimulate the community to build next-generation multimodal foundation models towards expert artificial general intelligence (AGI). 🎯 **We have released a full set comprising 150 development samples and 900 validation samples. We have released 10,500 test questions without their answers.** The development set is used for few-shot/in-context learning, and the validation set is used for debugging models, selecting hyperparameters, or quick evaluations. The answers and explanations for the test set questions are withheld. You can submit your model's predictions for the **test set** on **[EvalAI](https://eval.ai/web/challenges/challenge-page/2179/overview)**. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6230d750d93e84e233882dbc/2Ulh9yznm1dvISV4xJ_Ok.png) ### Dataset Creation MMMU was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning. The data for the MMMU dataset was manually collected by a team of college students from various disciplines, using online sources, textbooks, and lecture materials. - **Content:** The dataset contains 11.5K college-level problems across six broad disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) and 30 college subjects. - **Image Types:** The dataset includes 30 highly heterogeneous image types, such as charts, diagrams, maps, tables, music sheets, and chemical structures, interleaved with text. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6230d750d93e84e233882dbc/Mbf8O5lEH8I8czprch0AG.png) ## 🏆 Mini-Leaderboard We show a mini-leaderboard here and please find more information in our paper or [**homepage**](https://mmmu-benchmark.github.io/). | Model | Val (900) | Test (10.5K) | |----------------------------|:---------:|:------------:| | Gemini Ultra* | **59.4** | - | | GPT-4V(ision) (Playground) | 56.8 | **55.7** | | Gemini Pro* | 47.9 | - | | Qwen-VL-PLUS* | 45.2 | 40.8 | | BLIP-2 FLAN-T5-XXL | 35.4 | 34.0 | | InstructBLIP-T5-XXL | 35.7 | 33.8 | | LLaVA-1.5-13B | 36.4 | 33.6 | | Qwen-VL-7B | 35.9 | 32.9 | | mPLUG-OWL2* | 32.7 | 32.1 | | BLIP-2 FLAN-T5-XL | 34.4 | 31.0 | | InstructBLIP-T5-XL | 32.9 | 30.6 | | SPHINX* | 32.9 | - | | Gemini Nano2* | 32.6 | - | | CogVLM | 32.1 | 30.1 | | Otter | 32.2 | 29.1 | | LLaMA-Adapter2-7B | 29.8 | 27.7 | | MiniGPT4-Vicuna-13B | 26.8 | 27.6 | | Fuyu-8B | 27.9 | 27.4 | | Kosmos2 | 24.4 | 26.6 | | OpenFlamingo2-9B | 28.7 | 26.3 | | Frequent Choice | 22.1 | 23.9 | | Random Choice | 26.8 | 25.8 | *: results provided by the authors. ## Limitations Despite its comprehensive nature, MMMU, like any benchmark, is not without limitations. The manual curation process, albeit thorough, may carry biases. And the focus on college-level subjects might not fully be a sufficient test for Expert AGI. However, we believe it should be necessary for an Expert AGI to achieve strong performance on MMMU to demonstrate their broad and deep subject knowledge as well as expert-level understanding and reasoning capabilities. In future work, we plan to incorporate human evaluations into MMMU. This will provide a more grounded comparison between model capabilities and expert performance, shedding light on the proximity of current AI systems to achieving Expert AGI. ## Disclaimers The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution. Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to notify us. Upon verification, such samples will be promptly removed. ## Contact - Xiang Yue: [email protected] - Yu Su: [email protected] - Wenhu Chen: [email protected] ## Citation **BibTeX:** ```bibtex @article{yue2023mmmu, title={MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI}, author={Xiang Yue and Yuansheng Ni and Kai Zhang and Tianyu Zheng and Ruoqi Liu and Ge Zhang and Samuel Stevens and Dongfu Jiang and Weiming Ren and Yuxuan Sun and Cong Wei and Botao Yu and Ruibin Yuan and Renliang Sun and Ming Yin and Boyuan Zheng and Zhenzhu Yang and Yibo Liu and Wenhao Huang and Huan Sun and Yu Su and Wenhu Chen}, journal={arXiv preprint arXiv:2311.16502}, year={2023}, } ```
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sboughorbel/mmlu_arabic
sboughorbel
2023-12-17T17:38:06Z
4,654
0
[ "region:us" ]
null
2023-11-23T09:48:43Z
--- dataset_info: features: - name: question dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: correct_answer dtype: string splits: - name: validation num_bytes: 213556 num_examples: 456 - name: test num_bytes: 19534326 num_examples: 28084 download_size: 27157379 dataset_size: 19747882 ---
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DFKI-SLT/brat
DFKI-SLT
2023-12-11T09:54:08Z
3,197
2
[ "task_categories:token-classification", "task_ids:parsing", "annotations_creators:expert-generated", "language_creators:found", "region:us" ]
[ "token-classification" ]
2022-05-10T06:13:33Z
--- annotations_creators: - expert-generated language_creators: - found license: [] task_categories: - token-classification task_ids: - parsing --- # Information Card for Brat ## Table of Contents - [Description](#description) - [Summary](#summary) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Usage](#usage) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Description - **Homepage:** https://brat.nlplab.org - **Paper:** https://aclanthology.org/E12-2021/ - **Leaderboard:** \[Needs More Information\] - **Point of Contact:** \[Needs More Information\] ### Summary Brat is an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annota- tion for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. brat is designed in particular for structured annotation, where the notes are not free form text but have a fixed form that can be automatically processed and interpreted by a computer. ## Dataset Structure Dataset annotated with brat format is processed using this script. Annotations created in brat are stored on disk in a standoff format: annotations are stored separately from the annotated document text, which is never modified by the tool. For each text document in the system, there is a corresponding annotation file. The two are associated by the file naming convention that their base name (file name without suffix) is the same: for example, the file DOC-1000.ann contains annotations for the file DOC-1000.txt. More information can be found [here](https://brat.nlplab.org/standoff.html). ### Data Instances ``` { "context": ''<?xml version="1.0" encoding="UTF-8" standalone="no"?>\n<Document xmlns:gate="http://www.gat...' "file_name": "A01" "spans": { 'id': ['T1', 'T2', 'T4', 'T5', 'T6', 'T3', 'T7', 'T8', 'T9', 'T10', 'T11', 'T12',...] 'type': ['background_claim', 'background_claim', 'background_claim', 'own_claim',...] 'locations': [{'start': [2417], 'end': [2522]}, {'start': [2524], 'end': [2640]},...] 'text': ['complicated 3D character models...', 'The range of breathtaking realistic...', ...] } "relations": { 'id': ['R1', 'R2', 'R3', 'R4', 'R5', 'R6', 'R7', 'R8', 'R9', 'R10', 'R11', 'R12',...] 'type': ['supports', 'supports', 'supports', 'supports', 'contradicts', 'contradicts',...] 'arguments': [{'type': ['Arg1', 'Arg2'], 'target': ['T4', 'T5']},...] } "equivalence_relations": {'type': [], 'targets': []}, "events": {'id': [], 'type': [], 'trigger': [], 'arguments': []}, "attributions": {'id': [], 'type': [], 'target': [], 'value': []}, "normalizations": {'id': [], 'type': [], 'target': [], 'resource_id': [], 'entity_id': []}, "notes": {'id': [], 'type': [], 'target': [], 'note': []}, } ``` ### Data Fields - `context` (`str`): the textual content of the data file - `file_name` (`str`): the name of the data / annotation file without extension - `spans` (`dict`): span annotations of the `context` string - `id` (`str`): the id of the span, starts with `T` - `type` (`str`): the label of the span - `locations` (`list`): the indices indicating the span's locations (multiple because of fragments), consisting of `dict`s with - `start` (`list` of `int`): the indices indicating the inclusive character start positions of the span fragments - `end` (`list` of `int`): the indices indicating the exclusive character end positions of the span fragments - `text` (`list` of `str`): the texts of the span fragments - `relations`: a sequence of relations between elements of `spans` - `id` (`str`): the id of the relation, starts with `R` - `type` (`str`): the label of the relation - `arguments` (`list` of `dict`): the spans related to the relation, consisting of `dict`s with - `type` (`list` of `str`): the argument roles of the spans in the relation, either `Arg1` or `Arg2` - `target` (`list` of `str`): the spans which are the arguments of the relation - `equivalence_relations`: contains `type` and `target` (more information needed) - `events`: contains `id`, `type`, `trigger`, and `arguments` (more information needed) - `attributions` (`dict`): attribute annotations of any other annotation - `id` (`str`): the instance id of the attribution - `type` (`str`): the type of the attribution - `target` (`str`): the id of the annotation to which the attribution is for - `value` (`str`): the attribution's value or mark - `normalizations` (`dict`): the unique identification of the real-world entities referred to by specific text expressions - `id` (`str`): the instance id of the normalized entity - `type`(`str`): the type of the normalized entity - `target` (`str`): the id of the annotation to which the normalized entity is for - `resource_id` (`str`): the associated resource to the normalized entity - `entity_id` (`str`): the instance id of normalized entity - `notes` (`dict`): a freeform text, added to the annotation - `id` (`str`): the instance id of the note - `type` (`str`): the type of note - `target` (`str`): the id of the related annotation - `note` (`str`): the text body of the note ### Usage The `brat` dataset script can be used by calling `load_dataset()` method and passing any arguments that are accepted by the `BratConfig` (which is a special [BuilderConfig](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.BuilderConfig)). It requires at least the `url` argument. The full list of arguments is as follows: - `url` (`str`): the url of the dataset which should point to either a zip file or a directory containing the Brat data (`*.txt`) and annotation (`*.ann`) files - `description` (`str`, optional): the description of the dataset - `citation` (`str`, optional): the citation of the dataset - `homepage` (`str`, optional): the homepage of the dataset - `split_paths` (`dict`, optional): a mapping of (arbitrary) split names to subdirectories or lists of files (without extension), e.g. `{"train": "path/to/train_directory", "test": "path/to/test_director"}` or `{"train": ["path/to/train_file1", "path/to/train_file2"]}`. In both cases (subdirectory paths or file paths), the paths are relative to the url. If `split_paths` is not provided, the dataset will be loaded from the root directory and all direct subfolders will be considered as splits. - `file_name_blacklist` (`list`, optional): a list of file names (without extension) that should be ignored, e.g. `["A28"]`. This is useful if the dataset contains files that are not valid brat files. Important: Using the `data_dir` parameter of the `load_dataset()` method overrides the `url` parameter of the `BratConfig`. We provide an example of [SciArg](https://aclanthology.org/W18-5206.pdf) dataset below: ```python from datasets import load_dataset kwargs = { "description" : """This dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of scientific writing.""", "citation" : """@inproceedings{lauscher2018b, title = {An argument-annotated corpus of scientific publications}, booktitle = {Proceedings of the 5th Workshop on Mining Argumentation}, publisher = {Association for Computational Linguistics}, author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo}, address = {Brussels, Belgium}, year = {2018}, pages = {40–46} }""", "homepage": "https://github.com/anlausch/ArguminSci", "url": "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip", "split_paths": { "train": "compiled_corpus", }, "file_name_blacklist": ['A28'], } dataset = load_dataset('dfki-nlp/brat', **kwargs) ``` ## Additional Information ### Licensing Information \[Needs More Information\] ### Citation Information ``` @inproceedings{stenetorp-etal-2012-brat, title = "brat: a Web-based Tool for {NLP}-Assisted Text Annotation", author = "Stenetorp, Pontus and Pyysalo, Sampo and Topi{\'c}, Goran and Ohta, Tomoko and Ananiadou, Sophia and Tsujii, Jun{'}ichi", booktitle = "Proceedings of the Demonstrations at the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics", month = apr, year = "2012", address = "Avignon, France", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/E12-2021", pages = "102--107", } ```
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Tianduo/gsm8k-split
Tianduo
2023-12-12T07:43:33Z
2,654
1
[ "region:us" ]
null
2023-12-12T07:01:51Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 3559960 num_examples: 6705 - name: dev num_bytes: 403242 num_examples: 768 - name: test num_bytes: 713732 num_examples: 1319 download_size: 0 dataset_size: 4676934 --- # Dataset Card for "gsm8k-processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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mteb/results
mteb
2023-12-15T20:14:19Z
2,236
5
[ "benchmark:mteb", "region:us" ]
null
2022-09-01T14:15:23Z
--- benchmark: mteb type: evaluation submission_name: MTEB ---
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geraldOslo/prompt_answer_dataset_for_norwegian_radiation_protection_in_dentistry
geraldOslo
2023-12-17T09:39:22Z
1,922
0
[ "size_categories:1K<n<10K", "language:no", "license:cc-by-2.0", "dentistry", "physics", "radiation protection", "doi:10.57967/hf/1291", "region:us" ]
null
2023-10-30T08:56:56Z
--- license: cc-by-2.0 language: - 'no' tags: - dentistry - physics - radiation protection pretty_name: Question/answer connected to radiation protection in dentistry size_categories: - 1K<n<10K configs: - config_name: tab data_files: "data.csv" sep: ";" --- # This dataset is discontinoued because the name is to long There are problerms loading this dataset in some systems because the path of the caching system is far to long. I cannot delete it because it has a DOI. The dataset will be developed further here: [geraldOslo/RadProtDataSet](https://huggingface.co/datasets/geraldOslo/RadProtDataSet) # Contents ## Overview This dataset comprises prompt/answer pairs related to the curriculum for Norwegian dentistry and dental hygiene students, specifically focusing on the subjects of radiation physics, radiation protection, and radiological technology. ## Data source The prompt/answer pairs in this dataset were generated using commercially available Large Language Models (LLMs), including OpenAI GPT-4 and Anthropic Claude 2. These pairs were generated based on the analysis of documents provided as input to these LLMs. Following this, some irrelevant pairs were deleted, some were edited for accuracy and clarity, and all pairs were proofread for errors. ### Source documents The dataset was created using a variety of source documents, primarily encompassing: - [Lov om strålevern og bruk av stråling (strålevernloven) (2000)](https://lovdata.no/dokument/SF/forskrift/2016-12-16-1659) - [Forskrift om strålevern og bruk av stråling (strålevernforskriften) (2016)](https://lovdata.no/dokument/SF/forskrift/2016-12-16-1659) - [DSA: Medisinsk strålebruk (web page) (2023)](https://dsa.no/medisinsk-stralebruk) - [DSA: Veileder 14](https://dsa.no/publikasjoner/veileder-14-veileder-om-stralebruk-innen-odontologi/Veileder_14_odontologi.pdf) - [DSA: StrålevernRapport • 2014:2 Strålebruk i Norge](https://dsa.no/publikasjoner/stralevernrapport-2-2014-stralebruk-i-norge/StralevernRapport_02-2014_Str%C3%A5lebruk%20i%20Norge.pdf) - [DSA: StrålevernRapport 2015:12 Stråledoser til befolkningen](https://dsa.no/publikasjoner/stralevernrapport-12-2015-straledoser-til-befolkningen/StralevernRapport_12-15_Str%C3%A5ledoser_til_befolkningen-.pdf) - [DSA: Veileder til forskrift om strålevern og bruk av stråling Veileder Nummer 5 Revidert mai 2023](https://dsa.no/publikasjoner/_/attachment/inline/70e8470f-6c36-46fc-9e97-c27298859d66:22ab78bd659798c58cc3ce55c07dbb9aad9b44a0/Veileder%205_rev-mai2023.pdf) - [Gerald Torgersen: Strålingsfysikk, strålevern og radiologisk teknologi for tannpleie- og tannlegestudenter (online course) (2023)](https://uio.instructure.com/courses/19845) - Own teaching material and notes DSA is the The Norwegian Radiation and Nuclear Safety Authority # Purpose The dataset is generated for fine-tuning of open source LLMs. # Format The dataset is a UTF-8 formatted ";"-separated csv-file. There are two columns: prompt, prediction # Warning The dataset is provided for use on own responsibility. Please give feedback if you find a serious error. # Todo - add more relevant prompt/response pairs - further proofreading and adjustments
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qgyd2021/few_shot_intent_sft
qgyd2021
2023-12-13T02:21:00Z
1,798
9
[ "task_categories:text-classification", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:100M<n<1B", "language:zh", "language:en", "license:apache-2.0", "few-shot", "intent", "arxiv:2003.04807", "arxiv:1709.10217", "arxiv:1509.01626", "arxiv:2307.09705", "region:us" ]
[ "text-classification", "text-generation", "text2text-generation" ]
2023-09-22T11:26:09Z
--- license: apache-2.0 task_categories: - text-classification - text-generation - text2text-generation language: - zh - en tags: - few-shot - intent size_categories: - 100M<n<1B dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: not_applicable dtype: bool - name: intent dtype: string - name: intent_version dtype: string - name: n_way dtype: int32 - name: n_shot dtype: int32 - name: description dtype: string splits: - name: train num_bytes: 22484898 num_examples: 22080 - name: test num_bytes: 1853817 num_examples: 2477 download_size: 7816475 dataset_size: 24338715 --- ## 小样本意图识别指令数据集 收集了意图识别的数据集, 将其制作成 prompt, 用于 few-shot 的意图识别 LLM 研究. 编写 prompt 模板需要想像力, 你可以在 community 中交流你的想法. `{dataset_name}_prompt` 子集是从其对应的 `{dataset_name}` 数据集和 `{dataset_name}_template` 子集动态生成的, 因此每一次的结果都会不一样. 提示: 由于训练时 prompt 的长度可能超出最大限制而被 truncate, 因此尽量把 prompt 设计成即使被 truncate 也仍然可以用于 GPT 训练. [提示工程指南](https://www.promptingguide.ai/zh/techniques/cot) ### 样本示例 <details> <summary>train subset prompt 示例: (intent: Is it safe to go to the gym indoors if I'm vaccinated?)</summary> <pre><code>intent recognition.<br> Examples: ------------ text: will i be okay on the gym intent: Is it safe to go to the gym indoors if I'm vaccinated? ------------ text: I want to go and exercise at the gym, indoors, but I don't know if it's safe? intent: Is it safe to go to the gym indoors if I'm vaccinated? ------------ text: I worry I will catch Covid from the Gym even though I have been vaccinated? intent: Is it safe to go to the gym indoors if I'm vaccinated? ------------ text: What does the fda think about the covid 19 vaccine? intent: Is the vaccine FDA approved? ------------ text: it's never safe in a gym there are always bacteria everywhere intent: Is it safe to go to the gym indoors if I'm vaccinated? ------------ text: who is the difference between FDA authorization and approval? intent: Is the vaccine FDA approved? ------------ text: would the vaccine FDA be approved intent: Is the vaccine FDA approved? ------------ text: If I had my vaccine, is it safe to go to the indoor gym? intent: </code></pre> </details> <details> <summary>train subset prompt 示例: (intent: 考虑一下)</summary> <pre><code>电销场景意图识别。如果不能确定,请输出 “未知意图”。<br> Examples: ------------ text: 没关系啦 知道的 intent: 肯定答复 ------------ text: 怎么能联系你 intent: 查联系方式 ------------ text: 恩。让我想想吧。 intent: 考虑一下 ------------ text: 说点有用的 intent: 请讲重点 ------------ text: 唉唉 intent: 语气词 ------------ text: 说快一点 intent: 请讲重点 ------------ text: 再介绍一下 intent: 要求复述 ------------ text: 从哪弄到我信息 intent: 质疑隐私安全 ------------ text: 哎。。不是的 intent: 不是 ------------ text: 给我电话号码 intent: 查联系方式 ------------ text: 先看看吧 intent: 考虑一下 ------------ text: 怎么知道道我的信息 intent: 质疑隐私安全 ------------ text: 哎,再说吧,我再想想 intent: 考虑一下 ------------ text: 不,我清醒。 intent: 不是 ------------ text: 重说一次 intent: 要求复述 ------------ text: 行了,晚安 intent: 肯定答复 ------------ text: 额额额额 intent: 语气词 ------------ text: 恩。哎再说吧我考虑一下hiahia intent: </code></pre> </details> <details> <summary>train subset prompt 示例: (intent: 污言秽语)</summary> <pre><code>电销场景意图识别。<br> Examples: text: 那留言 intent: 语音信箱<br> text: 好啊,哈哈,没事,我再找其他的人 intent: 好的<br> text: 在! intent: 我在<br> text: 要打副本,没时间 intent: 没时间<br> text: 必须去学习!赶快去! intent: 加快速度<br> text: 好的。满汉全席送上 intent: 好的<br> text: 你看到我给你的留言了么 intent: 语音信箱<br> text: 我在呢。 intent: 我在<br> text: 傻逼? intent: 污言秽语<br> text: 胸大无脑 intent: 污言秽语<br> text: 不着急。 intent: 请等一等<br> text: 恩 我是团子 intent: 做自我介绍<br> text: 我是收电费的 intent: 做自我介绍<br> text: 我现在没时间接电话呢,待会儿打给你。 intent: 没时间<br> text: 好的。哈哈。初六见。我去睡觉啦 intent: 好的<br> text: 在啊 intent: 我在<br> text: 包皮猩 intent: 污言秽语<br> text: 离开一下 intent: 请等一等<br> text: 有病 intent: 污言秽语<br> text: 给我留个言 intent: 语音信箱<br> text: 你等一下 intent: 请等一等<br> text: 立刻马上!!!快快快快 intent: 加快速度<br> text: 我是郭钊源 intent: 做自我介绍<br> text: 快点儿 intent: 加快速度<br> text: 没时间睡觉怎么办吖 intent: 没时间<br> text: 吃!你来 intent: </code></pre> </details> <details> <summary>test subset prompt 示例: (intent: 未能理解)</summary> <pre><code>电销场景意图识别。如果不能确定,请输出 “未知意图”。<br> Examples: ------------ text: 讲什么 intent: 未能理解 ------------ text: 等着吧! intent: 请等一等 ------------ text: 搞不懂你 intent: 未能理解 ------------ text: 我实在是不想弄了,我那时事多没时间啊! intent: 没时间 ------------ text: 这你自己不清楚自己啊,还不晓得 intent: 不清楚 ------------ text: 没问题放心吧 intent: 肯定(没问题) ------------ text: 公司名字是什么 intent: 查公司介绍 ------------ text: 不放弃 intent: 肯定(需要) ------------ text: 老师也不懂 intent: </code></pre> </details> <details> <summary>test subset prompt 示例: (intent: 肯定(嗯嗯))</summary> <pre><code>电销场景意图识别。 不确定时请输出 “未知领域”。<br> Examples: ------------ text: 截止期过了多少天 intent: 疑问(时长) ------------ text: 不了 intent: 不需要 ------------ text: 不行,不够不够 intent: 否定(不可以) ------------ text: 4个1 intent: 答数值 ------------ text: 辽宁 intent: 地址 ------------ text: 不清楚 intent: 不清楚 ------------ text: 店里 intent: 地址 ------------ text: 嗯啊嗯嗯来吧 intent: 肯定(嗯嗯) ------------ text: 利息比别的贷款高 intent: 价格太高 ------------ text: 算23点,[9,4,8,2 intent: 答数值 ------------ text: 可以还得上 intent: 会按时处理 ------------ text: 对啊 就是不行 intent: 否定(不可以) ------------ text: 真的不便宜 intent: 价格太高 ------------ text: 嗯,thanks intent: 肯定(嗯嗯) ------------ text: 这你自己不清楚自己啊,还不晓得 intent: 不清楚 ------------ text: 我找找吧 intent: 会按时处理 ------------ text: 这是拖欠几天了 intent: 疑问(时长) ------------ text: 不需要证据 intent: 不需要 ------------ text: 噢,谢谢 intent: 肯定(嗯嗯) ------------ text: 恩恩,想我 intent: </code></pre> </details> <details> <summary>test subset prompt 示例: (intent: 不信任)</summary> <pre><code>意图识别。<br> Examples: text: 你不要答非所问 intent: 答非所问<br> text: 费用搞错了 intent: 否定(错误)<br> text: 我给你留言了,你木有回 intent: 语音信箱<br> text: 小骗子 intent: 不信任<br> text: 昆明 intent: 实体(地址)<br> text: 哦,行,好了你发信息给我 intent: 肯定(可以)<br> text: 哦,这样啊,没时间就算了 intent: 没时间<br> text: 我错了,别欺负我了 intent: 请求谅解<br> text: 万一你们是骗子怎么办 intent: 不信任<br> text: 我太乃刀了 intent: 无关领域<br> text: 讲清楚重要的 intent: 请讲重点<br> text: 骗子,好好说话 intent: </code></pre> </details> ### 数据来源 数据集从网上收集整理如下: #### 意图识别 意图识别(英语) | 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 | | :--- | :---: | :---: | :---: | :---: | :---: | | ATIS | 英语 | [ATIS](https://paperswithcode.com/dataset/atis); [ATIS_dataset](https://github.com/howl-anderson/ATIS_dataset) | 4978(Training set)+893(Testing set) | 微软提供的公开数据集 (Airline Travel Information System),实现意图识别任务。 | [atis_intents](https://huggingface.co/datasets/fathyshalab/atis_intents) | | conv_intent | 英语 | [conv_intent](https://huggingface.co/datasets/generalization/conv_intent_Full-p_1) | 13.8K | | [intent-recogniton](https://www.kaggle.com/code/upsunny/intent-recogniton-based-on-bert) | | banking77 | 英语 | [banking77](https://arxiv.org/abs/2003.04807); [task-specific-datasets](https://github.com/PolyAI-LDN/task-specific-datasets) | 13,083 | 在线银行查询数据集 | [banking77](https://huggingface.co/datasets/banking77) | | mobile_assistant | 英语 | [Intent-Classification-large](https://huggingface.co/datasets/dipesh/Intent-Classification-large) | 17K (但是我去除了意图为 others 的样本.) | | | | amazon_massive_intent_en_us | 英语 | [amazon_massive_intent_en_us](https://huggingface.co/datasets/SetFit/amazon_massive_intent_en-US) | 16.5K | Alexa virtual assistant | [nlu_evaluation_data](https://huggingface.co/datasets/nlu_evaluation_data) | | snips_built_in_intents | 英语 | [nlu-benchmark](https://github.com/sonos/nlu-benchmark); [benchmarking](https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d) | 328 | | [snips_built_in_intents](https://huggingface.co/datasets/snips_built_in_intents) | | vira_intents | 英语 | [vira-intent-classification](https://github.com/IBM/vira-intent-classification) | 10.9K | COVID-19 疫苗意图 | [vira_intents_live](https://huggingface.co/datasets/codesj/vira-intents-live); [vira_intents_live](https://huggingface.co/datasets/vira-chatbot/vira-intents-live) | | intent_classification | 英语 | [intent_classification](https://huggingface.co/datasets/Bhuvaneshwari/intent_classification) | 13.8K | | | | Out-of-Scope | 英语 | [范围外意图分类数据集](https://tianchi.aliyun.com/dataset/94112); [clinc150](https://archive.ics.uci.edu/dataset/570/clinc150) | | 该数据集提供了一种评估“Out-of-Scope”输入的意图分类模型的方法。 | [Out-of-Scope Intent Classification Dataset](https://www.kaggle.com/datasets/stefanlarson/outofscope-intent-classification-dataset); [clinc_oos](https://huggingface.co/datasets/clinc_oos) | | finance21 | 英语 | [finance21](https://github.com/Dark-Sied/Intent_Classification/) | | | | | book6 | 英语 | [book6](https://github.com/ajinkyaT/CNN_Intent_Classification) | 12000 | Six categories namely: AddToPlaylist, BookRestaurant, GetWeather , RateBook , SearchCreativeWork, SearchScreeningEvent each having nearly 2000 sentences. | [Intent Recognition Dataset](https://www.kaggle.com/datasets/himanshunayal/intent-recognition-dataset) | | bi_text | 英语 | [bi_text](https://www.kaggle.com/datasets/bitext/training-dataset-for-chatbotsvirtual-assistants); [customer-support-intent-dataset](https://www.kaggle.com/datasets/scodepy/customer-support-intent-dataset) | 8175 | 该数据集涵盖“客户支持”领域,包括分为 11 个类别的 27 个意图。 这些意图是从 Bitext 的 20 个特定领域数据集(银行、零售、公用事业……)中选择的,保留了跨领域的通用意图。 | | | small talk | 英语 | [Small Talk](https://www.kaggle.com/datasets/salmanfaroz/small-talk-intent-classification-data) | 3000 | 闲聊用于为用户提供与聊天机器人的随意对话流程 | | | chatbots | 英语 | [Chatbots: Intent Recognition Dataset](https://www.kaggle.com/datasets/elvinagammed/chatbots-intent-recognition-dataset) | | 用于分类、识别和聊天机器人开发的数据 | | | ide_intent | 英语 | [intent-classification-for-ide-functionalities](https://www.kaggle.com/datasets/abdullahusmani86/intent-classification-for-ide-functionalities) | 27019 | IDE 意图分类数据集。 | | | star_wars | 英语 | [star-wars](https://www.kaggle.com/datasets/aslanahmedov/star-wars-chat-bot) | 100 | 包含有关星球大战宇宙的各种数据。 | | | jarvis_intent | 英语 | [jarvisintent](https://www.kaggle.com/datasets/joelyu/jarvisintent) | 4556 | | | 意图识别(汉语) | 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 | | :--- | :---: | :---: | :---: | :---: | :---: | | amazon_massive_intent_zh_cn | 汉语 | [amazon_massive_intent_zh_cn](https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-CN) | 16.5K | Alexa virtual assistant | | | THU Intent Corpus | 汉语 | | 共计约6,000个句子 | 清华大学发布的中文意图识别和词槽填充数据集,包含15个领域和27个意图类别 | | | CrossWOZ | 汉语 | [CrossWOZ](https://github.com/thu-coai/CrossWOZ) | | CrossWOZ是第一个大规模中文跨域Wizard-of-Oz任务导向数据集。 它包含 5 个领域的 6K 对话会话和 102K 话语,包括酒店、餐厅、景点、地铁和出租车。 此外,该语料库还包含用户侧和系统侧丰富的对话状态和对话行为注释。 | | | CMID | 汉语 | [CMID](https://github.com/ishine/CMID) | | 该数据集用于中文医学 QA 意图理解任务。 | | | dmslots | 汉语 | [dmslots](https://raw.githubusercontent.com/kids/bert_nlu/main/data/dmslots.txt) | | 弱标注数据 | | | SMP2017 | 汉语 | [SMP2017-ECDT](http://ir.hit.edu.cn/SMP2017-ECDT); [1709.10217](https://arxiv.org/abs/1709.10217); [SMP2017ECDT-DATA](https://github.com/HITlilingzhi/SMP2017ECDT-DATA) | | 第六届全国社会媒体处理大会之中文人机对话技术评测(SMP2017-ECDT) | [ChineseNLPCorpus](https://github.com/InsaneLife/ChineseNLPCorpus) | | SMP2019 | 汉语 | [SMP2019](https://conference.cipsc.org.cn/smp2019/evaluation.html); [smp2019ecdt_task1](https://adamszq.github.io/smp2019ecdt_task1/) | | SMP2019 ECDT 中文人机对话技术测评 | [SMP2017-2019-ECDT-data](https://github.com/hml-ubt/SMP2017-2019-ECDT-data); [ChineseNLPCorpus](https://github.com/InsaneLife/ChineseNLPCorpus) | | a_intent | 汉语 | [意图识别](https://blog.csdn.net/weixin_42551154/article/details/129480825); [a_intent](https://pan.baidu.com/s/19_oqY4bC_lJa_7Mc6lxU7w?pwd=v4bi) | 12000 | 该意图识别数据集是一个多分类任务,目标是根据用户的输入文本判断用户的意图 | | | RiSAWOZ | 汉语 | [RiSAWOZ](https://gem-benchmark.com/data_cards/RiSAWOZ) | | RiSAWOZ 是一个中文对话数据集。 它可用于研究各种对话任务,例如对话状态跟踪、对话上下文到文本生成、共指消解以及统一生成省略号和共指消解。 | [GEM/RiSAWOZ](https://huggingface.co/datasets/GEM/RiSAWOZ) | | IMCS-IR | 汉语 | [中文医疗信息处理评测基准CBLUE](https://tianchi.aliyun.com/dataset/95414); [CBLUE 智能对话诊疗意图识别 IMCS-IR](https://github.com/winninghealth/imcs-ir) | | 中文医疗信息处理挑战榜CBLUE | | #### 文本分类 | 数据 | 语言 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 | | :--- | :---: | :---: | :---: | :---: | :---: | | ag_news | 英语 | [AG_corpus_of_news_articles](http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html); [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626); [ag_news](https://huggingface.co/datasets/ag_news) | 120K | AG的新闻主题分类数据集 | | | daily_dialog | 英语 | [DailyDialog](http://yanran.li/dailydialog) | 11.1K | 标签分类为:dummy (0), inform (1), question (2), directive (3), commissive (4). 情感分类为:no emotion (0), anger (1), disgust (2), fear (3), happiness (4), sadness (5), surprise (6). | [daily_dialog](https://huggingface.co/datasets/daily_dialog) | | chinese_news_title | 汉语 | [中文新闻文本标题分类](https://aistudio.baidu.com/datasetdetail/103654) | | 中文新闻标题数据集包含可供训练的32类(即新闻主题)标题47,952个,可供测试的新闻标题15,986个。在删除这些包含不能处理的特殊字符的标题后,我们保留了47,850个训练标题和15,950个测试标题(即#DataSet1)。 | [百度网盘](https://pan.baidu.com/s/1mgBTFOO) | #### 其它任务类型 | 数据 | 语言 | 任务类型 | 原始数据/项目地址 | 样本个数 | 原始数据描述 | 替代数据下载地址 | | :--- | :---: | :-----: | :---: | :---: | :---: | :---: | | suicide_intent | 英语 | 情感分类 | [suicide-intent](https://www.kaggle.com/datasets/hetarthraval/suicide-intent-detection-dataset) | 3731 | 该数据集有四个类别:快乐、正常、悲伤和自杀意图。 | | | CARER | 英语 | 情感分类 | [emotion](https://paperswithcode.com/dataset/emotion) | 20K | 情感是英语 Twitter 消息的数据集,包含六种基本情感:愤怒、恐惧、快乐、爱、悲伤和惊讶。 | [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) | | COIG-CQIA | 汉语 | 指令微调 | [CValues](https://arxiv.org/abs/2307.09705); [paralym/COIG-CQIA](https://github.com/paralym/COIG-CQIA) | | 高质量指令微调数据集,旨在为中文NLP社区提供高质量且符合人类交互行为的指令微调数据。 | [m-a-p/COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA) | | emo2019 | 英语 | 情感分类 | [SemEval-2019 Task 3](https://www.aclweb.org/anthology/S19-2005) | TRAIN: 30160, TEST: 5509 | 情绪检测。四个标签:others (0), happy (1), sad (2), angry (3). | [emo](https://huggingface.co/datasets/emo) | ### 数据加载 ```python #!/usr/bin/python3 # -*- coding: utf-8 -*- from datasets import load_dataset, concatenate_datasets name_list = [ "amazon_massive_intent_en_us_prompt", "amazon_massive_intent_zh_cn_prompt", "atis_intent_prompt", "banking77_prompt", "bi_text11_prompt", "bi_text27_prompt", "book6_prompt", # "chinese_news_title_prompt", "cmid_4class_prompt", "cmid_36class_prompt", "conv_intent_prompt", "crosswoz_prompt", "dmslots_prompt", "finance21_prompt", "intent_classification_prompt", "mobile_assistant_prompt", "mtop_intent_prompt", "out_of_scope_prompt", "small_talk_prompt", "smp2017_task1_prompt", "smp2019_task1_domain_prompt", "smp2019_task1_intent_prompt", "snips_built_in_intents_prompt", "telemarketing_intent_en_prompt", "telemarketing_intent_cn_prompt", "vira_intents_prompt", ] train_dataset = list() for name in name_list: dataset = load_dataset( path="qgyd2021/few_shot_intent_sft", name=name, split="train", ) train_dataset.append(dataset) train_dataset = concatenate_datasets(train_dataset) valid_dataset = list() for name in name_list: dataset = load_dataset( path="qgyd2021/few_shot_intent_sft", name=name, split="test", ) valid_dataset.append(dataset) valid_dataset = concatenate_datasets(valid_dataset) ``` ### 参考来源 <details> <summary>参考的数据来源,展开查看</summary> <pre><code> https://huggingface.co/datasets/qanastek/MASSIVE https://huggingface.co/datasets/fathyshalab/atis_intents https://huggingface.co/datasets/generalization/conv_intent_Full-p_1 https://huggingface.co/datasets/banking77 https://huggingface.co/datasets/dipesh/Intent-Classification-large https://huggingface.co/datasets/SetFit/amazon_massive_intent_en-US https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-CN https://huggingface.co/datasets/SetFit/amazon_massive_intent_zh-TW https://huggingface.co/datasets/snips_built_in_intents https://huggingface.co/datasets/zapsdcn/citation_intent https://huggingface.co/datasets/ibm/vira-intents https://huggingface.co/datasets/mteb/mtop_intent https://huggingface.co/datasets/Bhuvaneshwari/intent_classification https://huggingface.co/datasets/ibm/vira-intents-live https://huggingface.co/datasets/ebrigham/nl_banking_intents https://pan.baidu.com/s/19_oqY4bC_lJa_7Mc6lxU7w?pwd=v4bi https://gitee.com/a2798063/SMP2019/tree/master https://cold-eye.github.io/post/nlp-corpus/ https://www.cluebenchmarks.com/introduce.html https://github.com/search?q=chinese%20intent&type=repositories </code></pre> </details>
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laion/dalle-3-dataset
laion
2023-12-18T00:44:58Z
1,610
218
[ "language:en", "license:cc0-1.0", "image-text-dataset", "synthetic-dataset", "region:us" ]
null
2023-10-06T18:11:38Z
--- language: - en license: - cc0-1.0 tags: - image-text-dataset - synthetic-dataset dataset_info: features: - name: caption dtype: string - name: image dtype: image - name: link dtype: string - name: message_id dtype: string - name: timestamp dtype: string configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for LAION DALL·E 3 Discord Dataset **Description**: This dataset consists of prompt and image URL pairs scraped from the LAION [share-dalle-3 discord channel](https://discord.com/channels/823813159592001537/1158354590463447092). The purpose is to collect image-text pairs for research and exploration. **Source Code**: The code used to generate this data can be found [here](https://github.com/LAION-AI/Discord-Scrapers.git). ## Contributors - [Zach Nagengast](https://github.com/ZachNagengast) - [Eduardo Pach](https://github.com/EduardoPach) - [Seva Maltsev](https://github.com/TwoAbove) - The [LAION community](https://discord.com/invite/eq3cAMZtCC) ## Data Attributes - **caption**: The text description or prompt associated with the image. Data type: string. - **image**: The embedded image data from the discord message attachment. Data type: image. - **link**: The URL to the associated image. Data type: string. - **message_id**: The discord message id where the image was posted. Data type: string. - **timestamp**: Time the original message was posted. Datatype: string.
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203427as321/articles
203427as321
2023-12-18T01:00:07Z
1,479
0
[ "region:us" ]
null
2023-05-25T19:13:43Z
--- dataset_info: features: - name: label dtype: string - name: text dtype: string - name: __index_level_0__ dtype: float64 splits: - name: train num_bytes: 23996247 num_examples: 1534 download_size: 0 dataset_size: 23996247 --- # Dataset Card for "articles" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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HAERAE-HUB/K-MMLU-Preview
HAERAE-HUB
2023-12-16T10:38:26Z
1,329
4
[ "task_categories:multiple-choice", "size_categories:10K<n<100K", "language:ko", "license:cc-by-nc-nd-4.0", "mmlu", "haerae", "region:us" ]
[ "multiple-choice" ]
2023-11-27T09:06:18Z
--- configs: - config_name: easy-Agricultural-Sciences data_files: - split: train path: data/[easy]-Agricultural-Sciences-train.csv - split: dev path: data/[easy]-Agricultural-Sciences-dev.csv - split: test path: data/[easy]-Agricultural-Sciences-test.csv - config_name: easy-Aviation-Engineering-and-Maintenance data_files: - split: train path: data/[easy]-Aviation-Engineering-and-Maintenance-train.csv - split: dev path: data/[easy]-Aviation-Engineering-and-Maintenance-dev.csv - split: test path: data/[easy]-Aviation-Engineering-and-Maintenance-test.csv - config_name: easy-Biology data_files: - split: train path: data/[easy]-Biology-train.csv - split: dev path: data/[easy]-Biology-dev.csv - split: test path: data/[easy]-Biology-test.csv - config_name: easy-Chemical-Engineering data_files: - split: train path: data/[easy]-Chemical-Engineering-train.csv - split: dev path: data/[easy]-Chemical-Engineering-dev.csv - split: test path: data/[easy]-Chemical-Engineering-test.csv - config_name: easy-Chemistry data_files: - split: train path: data/[easy]-Chemistry-train.csv - split: dev path: data/[easy]-Chemistry-dev.csv - split: test path: data/[easy]-Chemistry-test.csv - config_name: easy-Civil-Engineering data_files: - split: train path: data/[easy]-Civil-Engineering-train.csv - split: dev path: data/[easy]-Civil-Engineering-dev.csv - split: test path: data/[easy]-Civil-Engineering-test.csv - config_name: easy-Computer-Science data_files: - split: train path: data/[easy]-Computer-Science-train.csv - split: dev path: data/[easy]-Computer-Science-dev.csv - split: test path: data/[easy]-Computer-Science-test.csv - config_name: easy-Construction data_files: - split: train path: data/[easy]-Construction-train.csv - split: dev path: data/[easy]-Construction-dev.csv - split: test path: data/[easy]-Construction-test.csv - config_name: easy-Ecology data_files: - split: train path: data/[easy]-Ecology-train.csv - split: dev path: data/[easy]-Ecology-dev.csv - split: test path: data/[easy]-Ecology-test.csv - config_name: easy-Electrical-Engineering data_files: - split: train path: data/[easy]-Electrical-Engineering-train.csv - split: dev path: data/[easy]-Electrical-Engineering-dev.csv - split: test path: data/[easy]-Electrical-Engineering-test.csv - config_name: easy-Electronics-Engineering data_files: - split: train path: data/[easy]-Electronics-Engineering-train.csv - split: dev path: data/[easy]-Electronics-Engineering-dev.csv - split: test path: data/[easy]-Electronics-Engineering-test.csv - config_name: easy-Energy-Management data_files: - split: train path: data/[easy]-Energy-Management-train.csv - split: dev path: data/[easy]-Energy-Management-dev.csv - split: test path: data/[easy]-Energy-Management-test.csv - config_name: easy-Environmental-Science data_files: - split: train path: data/[easy]-Environmental-Science-train.csv - split: dev path: data/[easy]-Environmental-Science-dev.csv - split: test path: data/[easy]-Environmental-Science-test.csv - config_name: easy-Fashion data_files: - split: train path: data/[easy]-Fashion-train.csv - split: dev path: data/[easy]-Fashion-dev.csv - split: test path: data/[easy]-Fashion-test.csv - config_name: easy-Food-Processing data_files: - split: train path: data/[easy]-Food-Processing-train.csv - split: dev path: data/[easy]-Food-Processing-dev.csv - split: test path: data/[easy]-Food-Processing-test.csv - config_name: easy-Gas-Technology-and-Engineering data_files: - split: train path: data/[easy]-Gas-Technology-and-Engineering-train.csv - split: dev path: data/[easy]-Gas-Technology-and-Engineering-dev.csv - split: test path: data/[easy]-Gas-Technology-and-Engineering-test.csv - config_name: easy-Geomatics data_files: - split: train path: data/[easy]-Geomatics-train.csv - split: dev path: data/[easy]-Geomatics-dev.csv - split: test path: data/[easy]-Geomatics-test.csv - config_name: easy-Industrial-Engineer data_files: - split: train path: data/[easy]-Industrial-Engineer-train.csv - split: dev path: data/[easy]-Industrial-Engineer-dev.csv - split: test path: data/[easy]-Industrial-Engineer-test.csv - config_name: easy-Information-Technology data_files: - split: train path: data/[easy]-Information-Technology-train.csv - split: dev path: data/[easy]-Information-Technology-dev.csv - split: test path: data/[easy]-Information-Technology-test.csv - config_name: easy-Interior-Architecture-and-Design data_files: - split: train path: data/[easy]-Interior-Architecture-and-Design-train.csv - split: dev path: data/[easy]-Interior-Architecture-and-Design-dev.csv - split: test path: data/[easy]-Interior-Architecture-and-Design-test.csv - config_name: easy-Law data_files: - split: train path: data/[easy]-Law-train.csv - split: dev path: data/[easy]-Law-dev.csv - split: test path: data/[easy]-Law-test.csv - config_name: easy-Machine-Design-and-Manufacturing data_files: - split: train path: data/[easy]-Machine-Design-and-Manufacturing-train.csv - split: dev path: data/[easy]-Machine-Design-and-Manufacturing-dev.csv - split: test path: data/[easy]-Machine-Design-and-Manufacturing-test.csv - config_name: easy-Management data_files: - split: train path: data/[easy]-Management-train.csv - split: dev path: data/[easy]-Management-dev.csv - split: test path: data/[easy]-Management-test.csv - config_name: easy-Maritime-Engineering data_files: - split: train path: data/[easy]-Maritime-Engineering-train.csv - split: dev path: data/[easy]-Maritime-Engineering-dev.csv - split: test path: data/[easy]-Maritime-Engineering-test.csv - config_name: easy-Marketing data_files: - split: train path: data/[easy]-Marketing-train.csv - split: dev path: data/[easy]-Marketing-dev.csv - split: test path: data/[easy]-Marketing-test.csv - config_name: easy-Materials-Engineering data_files: - split: train path: data/[easy]-Materials-Engineering-train.csv - split: dev path: data/[easy]-Materials-Engineering-dev.csv - split: test path: data/[easy]-Materials-Engineering-test.csv - config_name: easy-Mechanical-Engineering data_files: - split: train path: data/[easy]-Mechanical-Engineering-train.csv - split: dev path: data/[easy]-Mechanical-Engineering-dev.csv - split: test path: data/[easy]-Mechanical-Engineering-test.csv - config_name: easy-Nondestructive-Testing data_files: - split: train path: data/[easy]-Nondestructive-Testing-train.csv - split: dev path: data/[easy]-Nondestructive-Testing-dev.csv - split: test path: data/[easy]-Nondestructive-Testing-test.csv - config_name: easy-Patent data_files: - split: train path: data/[easy]-Patent-train.csv - split: dev path: data/[easy]-Patent-dev.csv - split: test path: data/[easy]-Patent-test.csv - config_name: easy-Psychology data_files: - split: train path: data/[easy]-Psychology-train.csv - split: dev path: data/[easy]-Psychology-dev.csv - split: test path: data/[easy]-Psychology-test.csv - config_name: easy-Public-Safety data_files: - split: train path: data/[easy]-Public-Safety-train.csv - split: dev path: data/[easy]-Public-Safety-dev.csv - split: test path: data/[easy]-Public-Safety-test.csv - config_name: easy-Railway-and-Automotive-Engineering data_files: - split: train path: data/[easy]-Railway-and-Automotive-Engineering-train.csv - split: dev path: data/[easy]-Railway-and-Automotive-Engineering-dev.csv - split: test path: data/[easy]-Railway-and-Automotive-Engineering-test.csv - config_name: easy-Refrigerating-Machinery data_files: - split: train path: data/[easy]-Refrigerating-Machinery-train.csv - split: dev path: data/[easy]-Refrigerating-Machinery-dev.csv - split: test path: data/[easy]-Refrigerating-Machinery-test.csv - config_name: easy-Social-Welfare data_files: - split: train path: data/[easy]-Social-Welfare-train.csv - split: dev path: data/[easy]-Social-Welfare-dev.csv - split: test path: data/[easy]-Social-Welfare-test.csv - config_name: easy-Telecommunications-and-Wireless-Technology data_files: - split: train path: data/[easy]-Telecommunications-and-Wireless-Technology-train.csv - split: dev path: data/[easy]-Telecommunications-and-Wireless-Technology-dev.csv - split: test path: data/[easy]-Telecommunications-and-Wireless-Technology-test.csv - config_name: hard-Accounting data_files: - split: train path: data/[hard]-Accounting-train.csv - split: dev path: data/[hard]-Accounting-dev.csv - split: test path: data/[hard]-Accounting-test.csv - config_name: hard-Agricultural-Sciences data_files: - split: train path: data/[hard]-Agricultural-Sciences-train.csv - split: dev path: data/[hard]-Agricultural-Sciences-dev.csv - split: test path: data/[hard]-Agricultural-Sciences-test.csv - config_name: hard-Biology data_files: - split: train path: data/[hard]-Biology-train.csv - split: dev path: data/[hard]-Biology-dev.csv - split: test path: data/[hard]-Biology-test.csv - config_name: hard-Chemical-Engineering data_files: - split: train path: data/[hard]-Chemical-Engineering-train.csv - split: dev path: data/[hard]-Chemical-Engineering-dev.csv - split: test path: data/[hard]-Chemical-Engineering-test.csv - config_name: hard-Chemistry data_files: - split: train path: data/[hard]-Chemistry-train.csv - split: dev path: data/[hard]-Chemistry-dev.csv - split: test path: data/[hard]-Chemistry-test.csv - config_name: hard-Civil-Engineering data_files: - split: train path: data/[hard]-Civil-Engineering-train.csv - split: dev path: data/[hard]-Civil-Engineering-dev.csv - split: test path: data/[hard]-Civil-Engineering-test.csv - config_name: hard-Computer-Science data_files: - split: train path: data/[hard]-Computer-Science-train.csv - split: dev path: data/[hard]-Computer-Science-dev.csv - split: test path: data/[hard]-Computer-Science-test.csv - config_name: hard-Construction data_files: - split: train path: data/[hard]-Construction-train.csv - split: dev path: data/[hard]-Construction-dev.csv - split: test path: data/[hard]-Construction-test.csv - config_name: hard-Criminal-Law data_files: - split: train path: data/[hard]-Criminal-Law-train.csv - split: dev path: data/[hard]-Criminal-Law-dev.csv - split: test path: data/[hard]-Criminal-Law-test.csv - config_name: hard-Economics data_files: - split: train path: data/[hard]-Economics-train.csv - split: dev path: data/[hard]-Economics-dev.csv - split: test path: data/[hard]-Economics-test.csv - config_name: hard-Education data_files: - split: train path: data/[hard]-Education-train.csv - split: dev path: data/[hard]-Education-dev.csv - split: test path: data/[hard]-Education-test.csv - config_name: hard-Electrical-Engineering data_files: - split: train path: data/[hard]-Electrical-Engineering-train.csv - split: dev path: data/[hard]-Electrical-Engineering-dev.csv - split: test path: data/[hard]-Electrical-Engineering-test.csv - config_name: hard-Electronics-Engineering data_files: - split: train path: data/[hard]-Electronics-Engineering-train.csv - split: dev path: data/[hard]-Electronics-Engineering-dev.csv - split: test path: data/[hard]-Electronics-Engineering-test.csv - config_name: hard-Energy-Management data_files: - split: train path: data/[hard]-Energy-Management-train.csv - split: dev path: data/[hard]-Energy-Management-dev.csv - split: test path: data/[hard]-Energy-Management-test.csv - config_name: hard-Food-Processing data_files: - split: train path: data/[hard]-Food-Processing-train.csv - split: dev path: data/[hard]-Food-Processing-dev.csv - split: test path: data/[hard]-Food-Processing-test.csv - config_name: hard-Gas-Technology-and-Engineering data_files: - split: train path: data/[hard]-Gas-Technology-and-Engineering-train.csv - split: dev path: data/[hard]-Gas-Technology-and-Engineering-dev.csv - split: test path: data/[hard]-Gas-Technology-and-Engineering-test.csv - config_name: hard-Geomatics data_files: - split: train path: data/[hard]-Geomatics-train.csv - split: dev path: data/[hard]-Geomatics-dev.csv - split: test path: data/[hard]-Geomatics-test.csv - config_name: hard-Health data_files: - split: train path: data/[hard]-Health-train.csv - split: dev path: data/[hard]-Health-dev.csv - split: test path: data/[hard]-Health-test.csv - config_name: hard-Industrial-Engineer data_files: - split: train path: data/[hard]-Industrial-Engineer-train.csv - split: dev path: data/[hard]-Industrial-Engineer-dev.csv - split: test path: data/[hard]-Industrial-Engineer-test.csv - config_name: hard-Information-Technology data_files: - split: train path: data/[hard]-Information-Technology-train.csv - split: dev path: data/[hard]-Information-Technology-dev.csv - split: test path: data/[hard]-Information-Technology-test.csv - config_name: hard-Law data_files: - split: train path: data/[hard]-Law-train.csv - split: dev path: data/[hard]-Law-dev.csv - split: test path: data/[hard]-Law-test.csv - config_name: hard-Machine-Design-and-Manufacturing data_files: - split: train path: data/[hard]-Machine-Design-and-Manufacturing-train.csv - split: dev path: data/[hard]-Machine-Design-and-Manufacturing-dev.csv - split: test path: data/[hard]-Machine-Design-and-Manufacturing-test.csv - config_name: hard-Management data_files: - split: train path: data/[hard]-Management-train.csv - split: dev path: data/[hard]-Management-dev.csv - split: test path: data/[hard]-Management-test.csv - config_name: hard-Materials-Engineering data_files: - split: train path: data/[hard]-Materials-Engineering-train.csv - split: dev path: data/[hard]-Materials-Engineering-dev.csv - split: test path: data/[hard]-Materials-Engineering-test.csv - config_name: hard-Political-Science-and-Sociology data_files: - split: train path: data/[hard]-Political-Science-and-Sociology-train.csv - split: dev path: data/[hard]-Political-Science-and-Sociology-dev.csv - split: test path: data/[hard]-Political-Science-and-Sociology-test.csv - config_name: hard-Psychology data_files: - split: train path: data/[hard]-Psychology-train.csv - split: dev path: data/[hard]-Psychology-dev.csv - split: test path: data/[hard]-Psychology-test.csv - config_name: hard-Public-Safety data_files: - split: train path: data/[hard]-Public-Safety-train.csv - split: dev path: data/[hard]-Public-Safety-dev.csv - split: test path: data/[hard]-Public-Safety-test.csv - config_name: hard-Railway-and-Automotive-Engineering data_files: - split: train path: data/[hard]-Railway-and-Automotive-Engineering-train.csv - split: dev path: data/[hard]-Railway-and-Automotive-Engineering-dev.csv - split: test path: data/[hard]-Railway-and-Automotive-Engineering-test.csv - config_name: hard-Real-Estate data_files: - split: train path: data/[hard]-Real-Estate-train.csv - split: dev path: data/[hard]-Real-Estate-dev.csv - split: test path: data/[hard]-Real-Estate-test.csv - config_name: hard-Social-Welfare data_files: - split: train path: data/[hard]-Social-Welfare-train.csv - split: dev path: data/[hard]-Social-Welfare-dev.csv - split: test path: data/[hard]-Social-Welfare-test.csv - config_name: hard-Taxation data_files: - split: train path: data/[hard]-Taxation-train.csv - split: dev path: data/[hard]-Taxation-dev.csv - split: test path: data/[hard]-Taxation-test.csv - config_name: hard-Telecommunications-and-Wireless-Technology data_files: - split: train path: data/[hard]-Telecommunications-and-Wireless-Technology-train.csv - split: dev path: data/[hard]-Telecommunications-and-Wireless-Technology-dev.csv - split: test path: data/[hard]-Telecommunications-and-Wireless-Technology-test.csv license: cc-by-nc-nd-4.0 task_categories: - multiple-choice language: - ko tags: - mmlu - haerae size_categories: - 10K<n<100K --- # K-MMLU (Korean-MMLU) <font color='red'>🚧 We're updating the dataset.🚧</font> *Paper Coming Soon!* The K-MMLU (Korean-MMLU) is a comprehensive suite designed to evaluate the advanced knowledge and reasoning abilities of large language models (LLMs) within the Korean language and cultural context. This suite encompasses 45 topics, primarily focusing on expert-level subjects. It includes general subjects like Physics and Ecology, and law and political science, alongside specialized fields such as Non-Destructive Training and Maritime Engineering. The datasets are derived from Korean licensing exams, with about 90% of the questions including human accuracy based on the performance of human test-takers in these exams. K-MMLU is segmented into training, testing, and development subsets, with the test subset ranging from a minimum of 100 to a maximum of 1000 questions, totaling 35,000 questions. Additionally, a set of 10 questions is provided as a development set for few-shot exemplar development. At total, K-MMLU consists of 254,334 instances. ### Usage via LM-Eval-Harness Official implementation for the evaluation is now available! You may run the evaluations yourself by: ```python lm_eval --model hf \ --model_args pretrained=NousResearch/Llama-2-7b-chat-hf,dtype=float16 \ --num_fewshot 0 \ --batch_size 4 \ --tasks kmmlu \ --device cuda:0 ``` To install lm-eval-harness refer to : [https://github.com/EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) ### Point of Contact For any questions contact us via the following email:) ``` [email protected] ```
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jherng/xd-violence
jherng
2023-12-17T10:40:51Z
1,177
0
[ "license:mit", "region:us" ]
null
2023-11-19T10:52:59Z
--- license: mit dataset_info: config_name: video features: - name: id dtype: string - name: path dtype: string - name: binary_target dtype: class_label: names: '0': Non-violence '1': Violence - name: multilabel_targets sequence: class_label: names: '0': Normal '1': Fighting '2': Shooting '3': Riot '4': Abuse '5': Car accident '6': Explosion - name: frame_annotations sequence: - name: start dtype: int32 - name: end dtype: int32 splits: - name: train num_bytes: 780973 num_examples: 3942 - name: test num_bytes: 118898 num_examples: 500 download_size: 79370542052 dataset_size: 899871 ---
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argilla/end2end_textclassification
argilla
2023-12-15T16:51:44Z
1,074
0
[ "size_categories:1K<n<10K", "rlfh", "argilla", "human-feedback", "region:us" ]
null
2023-11-13T17:25:52Z
--- size_categories: 1K<n<10K tags: - rlfh - argilla - human-feedback --- # Dataset Card for end2end_textclassification This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("argilla/end2end_textclassification") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("argilla/end2end_textclassification") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | text | Text | text | True | False | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | label | Label | label_selection | True | N/A | ['World', 'Sports', 'Business', 'Sci/Tech'] | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": "record-0", "fields": { "text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again." }, "metadata": {}, "responses": [], "suggestions": [], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": "record-0", "label": [], "label-suggestion": null, "label-suggestion-metadata": { "agent": null, "score": null, "type": null }, "metadata": "{}", "text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again." } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **text** is of type `text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **label** is of type `label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech']. * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **label-suggestion** is of type `label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech']. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## 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 guidelines Classify the articles into one of the four categories. #### 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]
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ai4privacy/pii-masking-200k
ai4privacy
2023-12-12T11:23:18Z
981
16
[ "task_categories:conversational", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:text-generation", "task_categories:text2text-generation", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:fr", "language:de", "language:it", "legal", "business", "psychology", "privacy", "region:us" ]
[ "conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation" ]
2023-11-06T03:34:07Z
--- language: - en - fr - de - it task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation multilinguality: - multilingual tags: - legal - business - psychology - privacy size_categories: - 100K<n<1M pretty_name: Ai4Privacy PII200k Dataset source_datasets: - original --- # Purpose and Features World's largest open source privacy dataset. The purpose of the dataset is to train models to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The example texts have **54 PII classes** (types of sensitive data), targeting **229 discussion subjects / use cases** split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...). Key facts: - Size: 13.6m text tokens in ~209k examples with 649k PII tokens (see [summary.json](summary.json)) - 4 languages, more to come! - English - French - German - Italian - Synthetic data generated using proprietary algorithms - No privacy violations! - Human-in-the-loop validated high quality dataset # Getting started Option 1: Python ```terminal pip install datasets ``` ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k", data_files=["*.jsonl"]) ``` # Token distribution across PII classes We have taken steps to balance the token distribution across PII classes covered by the dataset. This graph shows the distribution of observations across the different PII classes in this release: ![Token distribution across PII classes](pii_class_count_histogram.png) There is 1 class that is still overrepresented in the dataset: firstname. We will further improve the balance with future dataset releases. This is the token distribution excluding the FIRSTNAME class: ![Token distribution across PII classes excluding `FIRSTNAME`](pii_class_count_histogram_without_FIRSTNAME.png) # Compatible Machine Learning Tasks: - Tokenclassification. Check out a HuggingFace's [guide on token classification](https://huggingface.co/docs/transformers/tasks/token_classification). - [ALBERT](https://huggingface.co/docs/transformers/model_doc/albert), [BERT](https://huggingface.co/docs/transformers/model_doc/bert), [BigBird](https://huggingface.co/docs/transformers/model_doc/big_bird), [BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt), [BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom), [BROS](https://huggingface.co/docs/transformers/model_doc/bros), [CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert), [CANINE](https://huggingface.co/docs/transformers/model_doc/canine), [ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert), [Data2VecText](https://huggingface.co/docs/transformers/model_doc/data2vec-text), [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta), [DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2), [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert), [ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra), [ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie), [ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m), [ESM](https://huggingface.co/docs/transformers/model_doc/esm), [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon), [FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert), [FNet](https://huggingface.co/docs/transformers/model_doc/fnet), [Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel), [GPT-Sw3](https://huggingface.co/docs/transformers/model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo), [GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox), [I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert), [LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm), [LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2), [LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3), [LiLT](https://huggingface.co/docs/transformers/model_doc/lilt), [Longformer](https://huggingface.co/docs/transformers/model_doc/longformer), [LUKE](https://huggingface.co/docs/transformers/model_doc/luke), [MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm), [MEGA](https://huggingface.co/docs/transformers/model_doc/mega), [Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert), [MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert), [MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet), [MPT](https://huggingface.co/docs/transformers/model_doc/mpt), [MRA](https://huggingface.co/docs/transformers/model_doc/mra), [Nezha](https://huggingface.co/docs/transformers/model_doc/nezha), [Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer), [QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert), [RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert), [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co/docs/transformers/model_doc/roc_bert), [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer), [SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm), [XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet), [X-MOD](https://huggingface.co/docs/transformers/model_doc/xmod), [YOSO](https://huggingface.co/docs/transformers/model_doc/yoso) - Text Generation: Mapping the unmasked_text to to the masked_text or privacy_mask attributes. Check out HuggingFace's [guide to fine-tunning](https://huggingface.co/docs/transformers/v4.15.0/training) - [T5 Family](https://huggingface.co/docs/transformers/model_doc/t5), [Llama2](https://huggingface.co/docs/transformers/main/model_doc/llama2) # Information regarding the rows: - Each row represents a json object with a natural language text that includes placeholders for PII (and could plausibly be written by a human to an AI assistant). - Sample row: - "masked_text" contains a PII free natural text - "Product officially launching in [COUNTY_1]. Estimate profit of [CURRENCYSYMBOL_1][AMOUNT_1]. Expenses by [ACCOUNTNAME_1].", - "unmasked_text" shows a natural sentence containing PII - "Product officially launching in Washington County. Estimate profit of $488293.16. Expenses by Checking Account." - "privacy_mask" indicates the mapping between the privacy token instances and the string within the natural text.* - "{'[COUNTY_1]': 'Washington County', '[CURRENCYSYMBOL_1]': '$', '[AMOUNT_1]': '488293.16', '[ACCOUNTNAME_1]': 'Checking Account'}" - "span_labels" is an array of arrays formatted in the following way [start, end, pii token instance].* - "[[0, 32, 'O'], [32, 49, 'COUNTY_1'], [49, 70, 'O'], [70, 71, 'CURRENCYSYMBOL_1'], [71, 80, 'AMOUNT_1'], [80, 94, 'O'], [94, 110, 'ACCOUNTNAME_1'], [110, 111, 'O']]", - "bio_labels" follows the common place notation for "beginning", "inside" and "outside" of where each private tokens starts.[original paper](https://arxiv.org/abs/cmp-lg/9505040) -["O", "O", "O", "O", "B-COUNTY", "I-COUNTY", "O", "O", "O", "O", "B-CURRENCYSYMBOL", "O", "O", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "O", "O", "O", "B-ACCOUNTNAME", "I-ACCOUNTNAME", "O"], - "tokenised_text" breaks down the unmasked sentence into tokens using Bert Family tokeniser to help fine-tune large language models. - ["product", "officially", "launching", "in", "washington", "county", ".", "estimate", "profit", "of", "$", "48", "##8", "##29", "##3", ".", "16", ".", "expenses", "by", "checking", "account", "."] *note for the nested objects, we store them as string to maximise compability between various software. # About Us: At Ai4Privacy, we are commited to building the global seatbelt of the 21st century for Artificial Intelligence to help fight against potential risks of personal information being integrated into data pipelines. Newsletter & updates: [www.Ai4Privacy.com](www.Ai4Privacy.com) - Looking for ML engineers, developers, beta-testers, human in the loop validators (all languages) - Integrations with already existing open source solutions - Ask us a question on discord: [https://discord.gg/kxSbJrUQZF](https://discord.gg/kxSbJrUQZF) # Roadmap and Future Development - Carbon Neutral - Benchmarking - Better multilingual and especially localisation - Extended integrations - Continuously increase the training set - Further optimisation to the model to reduce size and increase generalisability - Next released major update is planned for the 14th of December 2023 (subscribe to newsletter for updates) # Use Cases and Applications **Chatbots**: Incorporating a PII masking model into chatbot systems can ensure the privacy and security of user conversations by automatically redacting sensitive information such as names, addresses, phone numbers, and email addresses. **Customer Support Systems**: When interacting with customers through support tickets or live chats, masking PII can help protect sensitive customer data, enabling support agents to handle inquiries without the risk of exposing personal information. **Email Filtering**: Email providers can utilize a PII masking model to automatically detect and redact PII from incoming and outgoing emails, reducing the chances of accidental disclosure of sensitive information. **Data Anonymization**: Organizations dealing with large datasets containing PII, such as medical or financial records, can leverage a PII masking model to anonymize the data before sharing it for research, analysis, or collaboration purposes. **Social Media Platforms**: Integrating PII masking capabilities into social media platforms can help users protect their personal information from unauthorized access, ensuring a safer online environment. **Content Moderation**: PII masking can assist content moderation systems in automatically detecting and blurring or redacting sensitive information in user-generated content, preventing the accidental sharing of personal details. **Online Forms**: Web applications that collect user data through online forms, such as registration forms or surveys, can employ a PII masking model to anonymize or mask the collected information in real-time, enhancing privacy and data protection. **Collaborative Document Editing**: Collaboration platforms and document editing tools can use a PII masking model to automatically mask or redact sensitive information when multiple users are working on shared documents. **Research and Data Sharing**: Researchers and institutions can leverage a PII masking model to ensure privacy and confidentiality when sharing datasets for collaboration, analysis, or publication purposes, reducing the risk of data breaches or identity theft. **Content Generation**: Content generation systems, such as article generators or language models, can benefit from PII masking to automatically mask or generate fictional PII when creating sample texts or examples, safeguarding the privacy of individuals. (...and whatever else your creative mind can think of) # Support and Maintenance AI4Privacy is a project affiliated with [AISuisse SA](https://www.aisuisse.com/).
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bzantium/LITM
bzantium
2023-12-12T01:46:34Z
979
0
[ "task_categories:question-answering", "size_categories:n<1K", "license:apache-2.0", "lost-in-the-middle", "arxiv:2307.03172", "region:us" ]
[ "question-answering" ]
2023-11-26T03:54:15Z
--- license: apache-2.0 configs: - config_name: kv75 data_files: - split: test path: "data/kv75.jsonl" - config_name: kv140 data_files: - split: test path: "data/kv140.jsonl" - config_name: kv300 data_files: - split: test path: "data/kv300.jsonl" - config_name: qa10 data_files: - split: test path: "data/qa10.jsonl" - config_name: qa20 data_files: - split: test path: "data/qa20.jsonl" - config_name: qa30 data_files: - split: test path: "data/qa30.jsonl" task_categories: - question-answering tags: - lost-in-the-middle size_categories: - n<1K --- # Datasets for Lost In The Middle This repository contains datasets used in the paper ["Lost in the Middle: How Language Models Use Long Contexts"](https://arxiv.org/abs/2307.03172), focusing on multi-document question answering and key-value retrieval tasks. ## Datasets Overview The datasets provided are as follows: - **Key-Value Retrieval Datasets** - `kv75`: Key-Value pairs with 75 keys. - `kv140`: Key-Value pairs with 140 keys. - `kv300`: Key-Value pairs with 300 keys. - **Multi-Document Question Answering Datasets** - `qa10`: Questions with answers derived from 10 documents. - `qa20`: Questions with answers derived from 20 documents. - `qa30`: Questions with answers derived from 30 documents. ## Loading the Data You can load these datasets using the Hugging Face `datasets` library: ```python from datasets import load_dataset ### Example for loading the kv75 dataset dataset = load_dataset("bzantium/LITM", "kv75") ### Example for loading the qa20 dataset dataset = load_dataset("bzantium/LITM", "qa20") ```
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nielsr/breast-cancer
nielsr
2023-12-14T16:17:41Z
954
6
[ "region:us" ]
null
2023-05-01T18:20:05Z
--- dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 42431652.0 num_examples: 130 download_size: 0 dataset_size: 42431652.0 --- # Dataset Card for "breast-cancer" Dataset was taken from the MedSAM project and used in [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb) which fine-tunes Meta's SAM model on the dataset. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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llm-book/ner-wikipedia-dataset
llm-book
2023-12-12T02:25:51Z
667
0
[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:ja", "license:cc-by-sa-3.0", "region:us" ]
[ "token-classification" ]
2023-04-15T10:43:21Z
--- language: - ja license: - cc-by-sa-3.0 size_categories: - 1K<n<10K task_categories: - token-classification --- # Dataset Card for llm-book/ner-wikipedia-dataset 書籍『大規模言語モデル入門』で使用する、ストックマーク株式会社により作成された「Wikipediaを用いた日本語の固有表現抽出データセット」(Version 2.0)です。 Githubリポジトリ[stockmarkteam/ner-wikipedia-dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)で公開されているデータセットを利用しています。 ### Citation ```bibtex @inproceedings{omi-2021-wikipedia, title = "Wikipediaを用いた日本語の固有表現抽出のデータセットの構築", author = "近江 崇宏", booktitle = "言語処理学会第27回年次大会", year = "2021", url = "https://anlp.jp/proceedings/annual_meeting/2021/pdf_dir/P2-7.pdf", } ``` ### Licence Wikipedia日本語版と同じCC-BY-SA 3.0のライセンスに従います。
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tomasg25/scientific_lay_summarisation
tomasg25
2023-12-12T07:51:52Z
655
14
[ "task_categories:summarization", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:cc-by-4.0", "abstractive-summarization", "scientific-papers", "lay-summarization", "PLOS", "eLife", "arxiv:2210.09932", "region:us" ]
[ "summarization" ]
2022-10-19T14:46:52Z
--- annotations_creators: - found language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: ScientificLaySummarisation size_categories: - 10K<n<100K - 1K<n<10K source_datasets: - original tags: - abstractive-summarization - scientific-papers - lay-summarization - PLOS - eLife task_categories: - summarization task_ids: [] --- # Dataset Card for "scientific_lay_summarisation" - **Repository:** https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation - **Paper:** [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932) - **Size of downloaded dataset files:** 850.44 MB - **Size of the generated dataset:** 1.32 GB - **Total amount of disk used:** 2.17 GB ### Dataset Summary This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature ](https://arxiv.org/abs/2210.09932)" . Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by [the Public Library of Science (PLOS)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) journal. More details/analyses on the content of each dataset are provided in the paper. Both "elife" and "plos" have 6 features: - "article": the body of the document (including the abstract), sections separated by "/n". - "section_headings": the title of each section, separated by "/n". - "keywords": keywords describing the topic of the article, separated by "/n". - "title": the title of the article. - "year": the year the article was published. - "summary": the lay summary of the document. **Note:** The format of both datasets differs from that used in the original repository (given above) in order to make them compatible with the `run_summarization.py` script of Transformers. Specifically, sentence tokenization is removed via " ".join(text), and the abstract and article sections, previously lists of sentences, are combined into a single `string` feature ("article") with each section separated by "\n". For the sentence-tokenized version of the dataset, please use the original git repository. ### Supported Tasks and Leaderboards Papers with code - [PLOS](https://paperswithcode.com/sota/lay-summarization-on-plos) and [eLife](https://paperswithcode.com/sota/lay-summarization-on-elife). ### Languages English ## Dataset Structure ### Data Instances #### plos - **Size of downloaded dataset files:** 425.22 MB - **Size of the generated dataset:** 1.05 GB - **Total amount of disk used:** 1.47 GB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "summary": "In the kidney , structures known as nephrons are responsible for collecting metabolic waste . Nephrons are composed of a ...", "article": "Kidney function depends on the nephron , which comprises a 'blood filter , a tubule that is subdivided into functionally ...", "section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and Methods'", "keywords": "developmental biology\ndanio (zebrafish)\nvertebrates\nteleost fishes\nnephrology", "title": "The cdx Genes and Retinoic Acid Control the Positioning and Segmentation of the Zebrafish Pronephros", "year": "2007" } ``` #### elife - **Size of downloaded dataset files:** 425.22 MB - **Size of the generated dataset:** 275.99 MB - **Total amount of disk used:** 1.47 MB An example of 'train' looks as follows. ``` This example was too long and was cropped: { "summary": "In the USA , more deaths happen in the winter than the summer . But when deaths occur varies greatly by sex , age , cause of ...", "article": "In temperate climates , winter deaths exceed summer ones . However , there is limited information on the timing and the ...", "section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and methods", "keywords": "epidemiology and global health", "title": "National and regional seasonal dynamics of all-cause and cause-specific mortality in the USA from 1980 to 2016", "year": "2018" } ``` ### Data Fields The data fields are the same among all splits. #### plos - `article`: a `string` feature. - `section_headings`: a `string` feature. - `keywords`: a `string` feature. - `title` : a `string` feature. - `year` : a `string` feature. - `summary`: a `string` feature. #### elife - `article`: a `string` feature. - `section_headings`: a `string` feature. - `keywords`: a `string` feature. - `title` : a `string` feature. - `year` : a `string` feature. - `summary`: a `string` feature. ### Data Splits | name |train |validation|test| |------|-----:|---------:|---:| |plos | 24773| 1376|1376| |elife | 4346| 241| 241| ## 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 ``` "Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature" Tomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina Scarton EMNLP 2022 ```
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visheratin/laion-coco-nllb
visheratin
2023-12-14T15:18:01Z
650
14
[ "task_categories:image-to-text", "task_categories:translation", "size_categories:100K<n<1M", "language:ace", "language:acm", "language:acq", "language:aeb", "language:af", "language:ajp", "language:ak", "language:als", "language:am", "language:apc", "language:ar", "language:ars", "language:ary", "language:arz", "language:as", "language:ast", "language:awa", "language:ayr", "language:azb", "language:azj", "language:ba", "language:bm", "language:ban", "language:be", "language:bem", "language:bn", "language:bho", "language:bjn", "language:bo", "language:bs", "language:bug", "language:bg", "language:ca", "language:ceb", "language:cs", "language:cjk", "language:ckb", "language:crh", "language:cy", "language:da", "language:de", "language:dik", "language:dyu", "language:dz", "language:el", "language:en", "language:eo", "language:et", "language:eu", "language:ee", "language:fo", "language:fj", "language:fi", "language:fon", "language:fr", "language:fur", "language:fuv", "language:gaz", "language:gd", "language:ga", "language:gl", "language:gn", "language:gu", "language:ht", "language:ha", "language:he", "language:hi", "language:hne", "language:hr", "language:hu", "language:hy", "language:ig", "language:ilo", "language:id", "language:is", "language:it", "language:jv", "language:ja", "language:kab", "language:kac", "language:kam", "language:kn", "language:ks", "language:ka", "language:kk", "language:kbp", "language:kea", "language:khk", "language:km", "language:ki", "language:rw", "language:ky", "language:kmb", "language:kmr", "language:knc", "language:kg", "language:ko", "language:lo", "language:lij", "language:li", "language:ln", "language:lt", "language:lmo", "language:ltg", "language:lb", "language:lua", "language:lg", "language:luo", "language:lus", "language:lvs", "language:mag", "language:mai", "language:ml", "language:mar", "language:min", "language:mk", "language:mt", "language:mni", "language:mos", "language:mi", "language:my", "language:nl", "language:nn", "language:nb", "language:npi", "language:nso", "language:nus", "language:ny", "language:oc", "language:ory", "language:pag", "language:pa", "language:pap", "language:pbt", "language:pes", "language:plt", "language:pl", "language:pt", "language:prs", "language:quy", "language:ro", "language:rn", "language:ru", "language:sg", "language:sa", "language:sat", "language:scn", "language:shn", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:sd", "language:so", "language:st", "language:es", "language:sc", "language:sr", "language:ss", "language:su", "language:sv", "language:swh", "language:szl", "language:ta", "language:taq", "language:tt", "language:te", "language:tg", "language:tl", "language:th", "language:ti", "language:tpi", "language:tn", "language:ts", "language:tk", "language:tum", "language:tr", "language:tw", "language:tzm", "language:ug", "language:uk", "language:umb", "language:ur", "language:uzn", "language:vec", "language:vi", "language:war", "language:wo", "language:xh", "language:ydd", "language:yo", "language:yue", "language:zh", "language:zsm", "language:zu", "license:cc-by-nc-4.0", "arxiv:2309.01859", "doi:10.57967/hf/1006", "region:us" ]
[ "image-to-text", "translation" ]
2023-06-18T06:58:28Z
--- language: - ace - acm - acq - aeb - af - ajp - ak - als - am - apc - ar - ars - ary - arz - as - ast - awa - ayr - azb - azj - ba - bm - ban - be - bem - bn - bho - bjn - bo - bs - bug - bg - ca - ceb - cs - cjk - ckb - crh - cy - da - de - dik - dyu - dz - el - en - eo - et - eu - ee - fo - fj - fi - fon - fr - fur - fuv - gaz - gd - ga - gl - gn - gu - ht - ha - he - hi - hne - hr - hu - hy - ig - ilo - id - is - it - jv - ja - kab - kac - kam - kn - ks - ka - kk - kbp - kea - khk - km - ki - rw - ky - kmb - kmr - knc - kg - ko - lo - lij - li - ln - lt - lmo - ltg - lb - lua - lg - luo - lus - lvs - mag - mai - ml - mar - min - mk - mt - mni - mos - mi - my - nl - nn - nb - npi - nso - nus - ny - oc - ory - pag - pa - pap - pbt - pes - plt - pl - pt - prs - quy - ro - rn - ru - sg - sa - sat - scn - shn - si - sk - sl - sm - sn - sd - so - st - es - sc - sr - ss - su - sv - swh - szl - ta - taq - tt - te - tg - tl - th - ti - tpi - tn - ts - tk - tum - tr - tw - tzm - ug - uk - umb - ur - uzn - vec - vi - war - wo - xh - ydd - yo - yue - zh - zsm - zu license: cc-by-nc-4.0 size_categories: - 100K<n<1M task_categories: - image-to-text - translation pretty_name: LAION-COCO translated to 200 languages dataset_info: features: - name: id dtype: string - name: url dtype: string - name: eng_caption dtype: string - name: captions sequence: sequence: string - name: score dtype: float64 splits: - name: test num_bytes: 271360114 num_examples: 14906 - name: train num_bytes: 13371094087 num_examples: 735097 download_size: 8682991817 dataset_size: 13642454201 language_details: ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* --- # LAION COCO translated into 200 languages This dataset contains the samples of the [LAION-COCO](https://huggingface.co/datasets/laion/laion-coco) dataset translated to 200 languages using the largest [NLLB-200 model](https://huggingface.co/facebook/nllb-200-3.3B) (3.3B parameters). ## Fields description 1. `id` - unique ID of the image. 2. `url` - original URL of the image from the LAION-COCO dataset. 3. `eng_caption` - original English caption from the LAION-COCO dataset. 4. `captions` - a list of captions translated to the languages from the Flores 200 dataset. Every item in the list is a list where the first element is a BCP-47 language code, and the second one is a caption in this language. The list of all language codes for the Flores 200 dataset can be found [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200). 5. `score` - aesthetic score generated using [LAION aesthetic predictor](https://github.com/christophschuhmann/improved-aesthetic-predictor/). The images in the dataset have the score of 4.5+. ## Images The dataset was filtered to contain only working image URLs. However, the availability may change in the future. Because of that, all images from this dataset are available at [https://nllb-data.com/](https://nllb-data.com/). To get the image, use the following format: ``` https://nllb-data.com/{id}.jpg ``` ## Paper The dataset was used to train the models in the paper: "[NLLB-CLIP - train performant multilingual image retrieval model on a budget](https://arxiv.org/abs/2309.01859)".
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kira/math-dpo
kira
2023-12-13T12:25:28Z
641
0
[ "region:us" ]
null
2023-12-13T12:21:38Z
--- dataset_info: features: - name: metadata dtype: string id: metadata - name: chosen_rating dtype: float64 - name: rejected_rating dtype: float64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 6976299.638544251 num_examples: 2393 - name: test num_bytes: 72882.36145574856 num_examples: 25 download_size: 3135711 dataset_size: 7049182.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- ``` DatasetDict({ train: Dataset({ features: ['metadata', 'chosen_rating', 'rejected_rating', 'prompt', 'chosen', 'rejected'], num_rows: 2393 }) test: Dataset({ features: ['metadata', 'chosen_rating', 'rejected_rating', 'prompt', 'chosen', 'rejected'], num_rows: 25 }) }) ```
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pufanyi/MIMICIT
pufanyi
2023-12-13T04:16:00Z
606
20
[ "size_categories:1M<n<10M", "language:en", "language:zh", "language:es", "language:ja", "language:fr", "language:ko", "language:ar", "license:mit", "arxiv:2306.05425", "region:us" ]
null
2023-07-12T07:22:42Z
--- license: mit language: - en - zh - es - ja - fr - ko - ar arxiv: 2306.05425 extra_gated_prompt: | <h1>MIMIC-IT Dataset Download Agreement</h1> <p>S-Lab, Nanyang Technological University (S-Lab) provides access to the MIMIC-IT Dataset (referred to as the Dataset) under the following conditions.</p> <p>By signing, the researcher agrees to the following terms of use:</p> <ol type="1"> <li>S-Lab makes no warranties regarding the Dataset, including but not limited to being up-to-date, correct or complete. S-Lab cannot be held liable for providing access to the Dataset or usage of the Dataset.</li> <li>The Dataset should only be used for scientific or research purposes. Any other use is explicitly prohibited.</li> <li>The researcher agrees to the following terms and conditions of data sources of the Dataset: <ul> <li>TVC: <a href="https://tvqa.cs.unc.edu/">https://tvqa.cs.unc.edu/</a></li> <li>LLaVA: <a href="https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K">https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K</a>; <a href="https://cocodataset.org/#termsofuse">https://cocodataset.org/#termsofuse</a></li> <li>DC: <a href="http://activity-net.org/index.html">http://activity-net.org/index.html</a></li> <li>VIST: <a href="https://visionandlanguage.net/VIST/index.html">https://visionandlanguage.net/VIST/index.html</a></li> <li>SD: <a href="https://goo.gl/forms/HJiFJSllupqeCbax1">https://goo.gl/forms/HJiFJSllupqeCbax1</a></li> <li>Ego4D: <a href="https://ego4ddataset.com/">https://ego4ddataset.com/</a></li> </ul></li> <li>The researcher takes full responsibility for usage of the Dataset at any time.</li> <li>S-Lab reserves the right to terminate the researcher's access to the Dataset at any time.</li> <li>The place of jurisdiction is Singapore.</li> <li>If any part of this agreement is legally invalid, this shall not affect the remaining agreement.</li> </ol> extra_gated_fields: Verifiable Name: text Institution Email: text Institutional Affiliation: text I agree with the agreement: checkbox pretty_name: 'MIMIC-IT: Multi-Modal In-Context Instruction Tuning' size_categories: - 1M<n<10M --- <p align="center" width="100%"> <img src="https://i.postimg.cc/sxy8v9PS/mimicit-logo.png" width="80%" height="80%"> </p> <div> <div align="center"> <a href='https://brianboli.com/' target='_blank'>Bo Li<sup>*,♠,1</sup></a>&emsp; <a href='https://zhangyuanhan-ai.github.io/' target='_blank'>Yuanhan Zhang<sup>*,♠,1</sup></a>&emsp; <a href='https://cliangyu.com/' target='_blank'>Liangyu Chen<sup>*,1</sup></a>&emsp; <a href='https://king159.github.io/' target='_blank'>Jinghao Wang<sup>*,1</sup></a>&emsp; <a href='https://pufanyi.github.io/' target='_blank'>Fanyi Pu<sup>*,1</sup></a>&emsp; </br> <a href='https://jingkang50.github.io/' target='_blank'>Jingkang Yang<sup>1</sup></a>&emsp; <a href='https://chunyuan.li/' target='_blank'>Chunyuan Li<sup>2</sup></a>&emsp; <a href='https://liuziwei7.github.io/' target='_blank'>Ziwei Liu<sup>&#x2709,1</sup></a> </div> <div> <div align="center"> <sup>1</sup>S-Lab, Nanyang Technological University&emsp; <sup>2</sup>Microsoft Research, Redmond </br> <sup>♠</sup> Co-Project Lead&emsp; <sup>*</sup> Equal Contribution&emsp; <sup>&#x2709</sup> Corresponding Author </div> ## Dataset Description - **Homepage: https://otter-ntu.github.io** - **Repository: https://github.com/Luodian/Otter** - **Paper: https://arxiv.org/abs/2306.05425** **Note 1: To reduce memory consumption during image loading and improve loading speed, we are converting the JSON format of images to the Parquet format. For detailed information, please refer to [this link](https://github.com/Luodian/Otter/blob/main/docs/mimicit_format.md).** **Note 2: We are uploading the full version of `DC` and `E4D`, the new files are indicated by the suffix `1207`.** ### Dataset Summary MIMIC-IT offers a diverse and extensive dataset of 2.8M multimodal instruction-response pairs, designed to enhance the performance of Vision-Language Models (VLMs) in real-life scenarios, enabling VLMs to excel in perception, reasoning, and planning while also catering to a multilingual audience. MIMIC-IT enables the application of egocentric visual assistant model that can serve that can answer your questions like **Hey, Do you think I left my keys on the table?**. Harness the power of MIMIC-IT to unlock the full potential of your AI-driven visual assistant and elevate your interactive vision-language tasks to new heights. MIMIC-IT provides multilingual instructions, supporting English, Chinese, Korean, Japanese, German, French, Spanish, and Arabic, thereby allowing a larger global audience to altogether enjoy from the convenience brought about by advancements in artificial intelligence. <p align="center" width="100%"> <img src="https://i.postimg.cc/4x66gHhw/mimic-it.jpg" width="100%" height="100%"> </p> ## Using MIMIC-IT We have already upload the `images.parquet` file. You can check [`tools/load.py`](tools/load.py) to learn how to load the dataset (`instruction.json` + `images.parquet`) and check the integrity of the whole dataset. You can also use [this code](https://huggingface.co/datasets/pufanyi/MIMICIT/blob/main/tools/convert_to_parquet.py) to convert `image.json` to `parquet` version by yourself. You can following the steps to obtain the MIMIC-IT dataset. Each task (e.g. `DC`, `LA`) in MIMIC-IT is composed of three parts, including: 1. `xx.json` file: the images in base64 format. 2. `xx_instructions.json` file: the instruction-response pairs (also includes image ids and related instructions ids for each instruction-response pair) for each task. 3. `xx_train.json` file: the customized related instruction-response pairs for each instruction. You can directly download the contents in the `data` folder. The distribution of the `data` folder is as follows: ```plain data/ CGD/ CGD.json CGD_images_preview.csv CGD_instructions.json ... ``` For each `dataset_name`, there are three main files **except for `DC` and `E4D`**: 1. `{dataset_name}.json`: Stores the image numbers and their corresponding base64 codes in lossless compressed PNG format. ```json { "image_id_1": "base64_code_1", "image_id_2": "base64_code_2", ... } ``` 2. `{dataset_name}_images_preview.csv`: Stores the image numbers and their corresponding base64 codes in lossy compressed JPG format, mainly used for display in the Dataset Card. ```csv id, image "image_id_1", "base64_code_1" "image_id_2", "base64_code_2" ... ``` 3. `{dataset_name}_instructions.json`: Stores each instruction and its associated answer. ```json { "meta": { "version": current_version, "time": update_time, "author": "ntu" }, "data": { "instruction_id_1": { "instruction": "instruction_1", "answer": "answer_of_instruction_1", "image_ids": [ "image_id_1", "image_id_2", ... ], "rel_ins_ids": [ "related_instruction_id_1", "related_instruction_id_2", ... ] }, ... } } ``` Of course, you can also use `wget` or `curl` for direct downloads. Below is an example. Before proceeding with the downloads, you need to set your Hugging Face token. For that, please refer to [this page](https://huggingface.co/docs/hub/security-tokens). ```shell $ # Set Hugging Face Token $ HF_TOKEN="YOUR_HUGGING_FACE_TOKEN" $ # Set the dataset you want to download $ DATASET_NAME="DATASET_YOU_WANT_TO_DOWNLOAD" # e.g. CGD $ # Download {DATASET_NAME}.json $ wget --header="Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}.json" $ # Download {DATASET_NAME}_instructions.json $ wget --header="Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_instructions.json" $ # Download {DATASET_NAME}_images_preview.csv (usually not necessary) $ wget --header="Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_images_preview.csv" ``` Or ```shell $ # Set Hugging Face Token $ HF_TOKEN="YOUR_HUGGING_FACE_TOKEN" $ # Set the dataset you want to download $ DATASET_NAME="DATASET_YOU_WANT_TO_DOWNLOAD" # e.g. CGD $ # Download {DATASET_NAME}.json $ curl -LJO -H "Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}.json" $ # Download {DATASET_NAME}_instructions.json $ curl -LJO -H "Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_instructions.json" $ # Download {DATASET_NAME}_images_preview.csv (usually not necessary) $ curl -LJO -H "Authorization: Bearer $HF_TOKEN" "https://huggingface.co/datasets/pufanyi/MIMICIT/resolve/main/data/${DATASET_NAME}/${DATASET_NAME}_images_preview.csv" ``` Alternatively, you can use `dataset.load_dataset` for downloading. However, due to Hugging Face's size limitations, all images can only be loaded in JPG format. Below is an example using `CGD` dataset: ### CGD_Images Download the JPG format images and their corresponding identifiers: ```python from datasets import load_dataset data = load_dataset("pufanyi/MIMICIT", "CGD_Images") ``` The format will be like: ```json { "id": "CGD_IMG_000000426149", "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7F84601D62F0> } ``` It should be noted that, due to size limitations, for `DC` (Dense Captions), this command will only extract a portion of the images from the `DC` collection for downloading. ### CGD_Instructions Download all instructions: ```python from datasets import load_dataset data = load_dataset("pufanyi/MIMICIT", "CGD_Instructions") ``` The format will be like: ```json { "id": "CGD_INS_000000", "instruction": "What is the difference between the two pizzas in these images?", "answer": "The pizza in the first image is on a red plate and being held by an old lady, while the pizza in the second image is on a metal counter being prepared by a woman in a blue shirt.", "images": [ "CGD_IMG_000000069568", "CGD_IMG_000000328270" ], "related instructions": [ "CGD_INS_000001" ] } ``` ### CGD_Preview Download all instructions along with their corresponding JPG images: ```python from datasets import load_dataset data = load_dataset("pufanyi/MIMICIT", "CGD_Preview") ``` The format will be like: ```json { "id": "CGD_INS_000000", "instruction": "What is the difference between the two pizzas in these images?", "answer": "The pizza in the first image is on a red plate and being held by an old lady, while the pizza in the second image is on a metal counter being prepared by a woman in a blue shirt.", "images": [ <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7F8460267DF0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=224x224 at 0x7F8460267700> ], "related instructions": [ "CGD_INS_000001" ] } ``` It should be noted that, due to size limitations, for `DC` (Dense Captions), this command will only extract a portion of the images from the `DC` collection for downloading.
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jat-project/jat-dataset-tokenized
jat-project
2023-12-17T21:20:21Z
579
0
[ "region:us" ]
null
2023-12-16T10:10:31Z
--- dataset_info: - config_name: atari-alien features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51690016760 num_examples: 14134 - name: test num_bytes: 5412567200 num_examples: 1480 download_size: 847084352 dataset_size: 57102583960 - config_name: atari-amidar features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 52366587660 num_examples: 14319 - name: test num_bytes: 4809139100 num_examples: 1315 download_size: 645230130 dataset_size: 57175726760 - config_name: atari-assault features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 52761558780 num_examples: 14427 - name: test num_bytes: 4421482260 num_examples: 1209 download_size: 253427406 dataset_size: 57183041040 - config_name: atari-asterix features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 52867615840 num_examples: 14456 - name: test num_bytes: 5138281700 num_examples: 1405 download_size: 293294825 dataset_size: 58005897540 - config_name: atari-asteroids features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 52472644720 num_examples: 14348 - name: test num_bytes: 3605940040 num_examples: 986 download_size: 316920881 dataset_size: 56078584760 - config_name: atari-atlantis features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 52388530500 num_examples: 14325 - name: test num_bytes: 3975311180 num_examples: 1087 download_size: 274044761 dataset_size: 56363841680 - config_name: atari-bankheist features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51810702380 num_examples: 14167 - name: test num_bytes: 5836795440 num_examples: 1596 download_size: 879913210 dataset_size: 57647497820 - config_name: atari-battlezone features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51130474340 num_examples: 13981 - name: test num_bytes: 6092795240 num_examples: 1666 download_size: 530279633 dataset_size: 57223269580 - config_name: atari-beamrider features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 49159275880 num_examples: 13442 - name: test num_bytes: 7881136700 num_examples: 2155 download_size: 427038477 dataset_size: 57040412580 - config_name: atari-berzerk features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 49495732760 num_examples: 13534 - name: test num_bytes: 6173252320 num_examples: 1688 download_size: 351457396 dataset_size: 55668985080 - config_name: atari-bowling features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51602245400 num_examples: 14110 - name: test num_bytes: 5898966820 num_examples: 1613 download_size: 163636375 dataset_size: 57501212220 - config_name: atari-boxing features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 53182129880 num_examples: 14542 - name: test num_bytes: 5884338260 num_examples: 1609 download_size: 662716999 dataset_size: 59066468140 - config_name: atari-breakout features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 49276304360 num_examples: 13474 - name: test num_bytes: 6612109120 num_examples: 1808 download_size: 265061412 dataset_size: 55888413480 - config_name: atari-centipede features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51916759440 num_examples: 14196 - name: test num_bytes: 6026966720 num_examples: 1648 download_size: 269117925 dataset_size: 57943726160 - config_name: atari-choppercommand features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 48994704580 num_examples: 13397 - name: test num_bytes: 7157022980 num_examples: 1957 download_size: 425098755 dataset_size: 56151727560 - config_name: atari-crazyclimber features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51295045640 num_examples: 14026 - name: test num_bytes: 5712452680 num_examples: 1562 download_size: 458327032 dataset_size: 57007498320 - config_name: atari-defender features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 49386018560 num_examples: 13504 - name: test num_bytes: 6173252320 num_examples: 1688 download_size: 217547225 dataset_size: 55559270880 - config_name: atari-demonattack features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 49367732860 num_examples: 13499 - name: test num_bytes: 6173252320 num_examples: 1688 download_size: 209153434 dataset_size: 55540985180 - config_name: atari-doubledunk features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: test num_bytes: 5800224040 num_examples: 1586 - name: train num_bytes: 52267844880 num_examples: 14292 download_size: 585277337 dataset_size: 58068068920 - config_name: atari-enduro features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 48493676400 num_examples: 13260 - name: test num_bytes: 6173252320 num_examples: 1688 download_size: 696325463 dataset_size: 54666928720 - config_name: atari-fishingderby features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51466931220 num_examples: 14073 - name: test num_bytes: 6085480960 num_examples: 1664 download_size: 817620849 dataset_size: 57552412180 - config_name: atari-freeway features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51258474240 num_examples: 14016 - name: test num_bytes: 5851424000 num_examples: 1600 download_size: 684682229 dataset_size: 57109898240 - config_name: atari-frostbite features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 51474245500 num_examples: 14075 - name: test num_bytes: 5898966820 num_examples: 1613 download_size: 629905232 dataset_size: 57373212320 - config_name: atari-gopher features: - name: image_observations sequence: sequence: sequence: sequence: float32 - name: rewards sequence: float64 - name: discrete_actions sequence: int64 - 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name: test num_bytes: 151174400 num_examples: 1600 download_size: 169540230 dataset_size: 1662918400 - config_name: metaworld-drawer-close features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 149251099 dataset_size: 1662918400 - config_name: metaworld-drawer-open features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 140934843 dataset_size: 1662918400 - config_name: metaworld-faucet-close features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 136063537 dataset_size: 1662918400 - config_name: metaworld-faucet-open features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 145250439 dataset_size: 1662918400 - config_name: metaworld-hammer features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 241525725 dataset_size: 1662918400 - config_name: metaworld-hand-insert features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 194537498 dataset_size: 1662918400 - config_name: metaworld-handle-press features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 150638456 dataset_size: 1662918400 - config_name: metaworld-handle-press-side features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 148362099 dataset_size: 1662918400 - config_name: metaworld-handle-pull features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 172672050 dataset_size: 1662918400 - config_name: metaworld-handle-pull-side features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 169115742 dataset_size: 1662918400 - config_name: metaworld-lever-pull features: - name: continuous_observations sequence: sequence: float32 length: 39 - name: continuous_actions sequence: sequence: float32 length: 4 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 758080000 num_examples: 16000 - name: test num_bytes: 75808000 num_examples: 1600 download_size: 168778000 dataset_size: 833888000 - config_name: metaworld-peg-insert-side features: - name: continuous_observations sequence: sequence: float32 length: 39 - name: continuous_actions sequence: sequence: float32 length: 4 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 758080000 num_examples: 16000 - name: test num_bytes: 75808000 num_examples: 1600 download_size: 153706742 dataset_size: 833888000 - config_name: metaworld-peg-unplug-side features: - name: continuous_observations sequence: sequence: float32 length: 39 - name: continuous_actions sequence: sequence: float32 length: 4 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 758080000 num_examples: 16000 - name: test num_bytes: 75808000 num_examples: 1600 download_size: 171743306 dataset_size: 833888000 - config_name: metaworld-pick-out-of-hole features: - name: continuous_observations sequence: sequence: float32 length: 39 - name: continuous_actions sequence: sequence: float32 length: 4 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 758080000 num_examples: 16000 - name: test num_bytes: 75808000 num_examples: 1600 download_size: 22275452 dataset_size: 833888000 - config_name: metaworld-pick-place features: - name: continuous_observations sequence: sequence: float32 length: 39 - name: continuous_actions sequence: sequence: float32 length: 4 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 758080000 num_examples: 16000 - name: test num_bytes: 75808000 num_examples: 1600 download_size: 176679644 dataset_size: 833888000 - config_name: metaworld-pick-place-wall features: - name: continuous_observations sequence: sequence: float32 length: 39 - name: continuous_actions sequence: sequence: float32 length: 4 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 758080000 num_examples: 16000 - name: test num_bytes: 75808000 num_examples: 1600 download_size: 172258683 dataset_size: 833888000 - config_name: metaworld-plate-slide features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 153944950 dataset_size: 1662918400 - config_name: metaworld-plate-slide-back features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 38041155 dataset_size: 1662918400 - config_name: metaworld-plate-slide-back-side features: - name: continuous_observations sequence: sequence: float32 length: 39 - name: continuous_actions sequence: sequence: float32 length: 4 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 758080000 num_examples: 16000 - name: test num_bytes: 75808000 num_examples: 1600 download_size: 33763310 dataset_size: 833888000 - config_name: metaworld-plate-slide-side features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 136176244 dataset_size: 1662918400 - config_name: metaworld-push features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 227615990 dataset_size: 1662918400 - config_name: metaworld-push-back features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 183750315 dataset_size: 1662918400 - config_name: metaworld-push-wall features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 218734341 dataset_size: 1662918400 - config_name: metaworld-reach features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 226869584 dataset_size: 1662918400 - config_name: metaworld-reach-wall features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 232192061 dataset_size: 1662918400 - config_name: metaworld-shelf-place features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 192622122 dataset_size: 1662918400 - config_name: metaworld-soccer features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 218877381 dataset_size: 1662918400 - config_name: metaworld-stick-pull features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 232576271 dataset_size: 1662918400 - config_name: metaworld-stick-push features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 227772744 dataset_size: 1662918400 - config_name: metaworld-sweep features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 227425624 dataset_size: 1662918400 - config_name: metaworld-sweep-into features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 178015690 dataset_size: 1662918400 - config_name: metaworld-window-close features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1511744000 num_examples: 16000 - name: test num_bytes: 151174400 num_examples: 1600 download_size: 142258417 dataset_size: 1662918400 - config_name: metaworld-window-open features: - name: continuous_observations sequence: sequence: float32 length: 39 - name: continuous_actions sequence: sequence: float32 length: 4 - name: rewards sequence: float32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 758080000 num_examples: 16000 - name: test num_bytes: 75808000 num_examples: 1600 download_size: 95794869 dataset_size: 833888000 - config_name: mujoco-ant features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 2758257700 num_examples: 35317 - name: test num_bytes: 307714000 num_examples: 3940 download_size: 2220110032 dataset_size: 3065971700 - config_name: mujoco-doublependulum features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1114678152 num_examples: 35962 - name: test num_bytes: 123705036 num_examples: 3991 download_size: 624008416 dataset_size: 1238383188 - config_name: mujoco-halfcheetah features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1926864000 num_examples: 36000 - name: test num_bytes: 214096000 num_examples: 4000 download_size: 1539169069 dataset_size: 2140960000 - config_name: mujoco-hopper features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 708507480 num_examples: 20190 - name: test num_bytes: 78360436 num_examples: 2233 download_size: 447516945 dataset_size: 786867916 - config_name: mujoco-humanoid features: - name: continuous_observations sequence: sequence: float64 - name: rewards sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 27053887548 num_examples: 33347 - name: test num_bytes: 3010674924 num_examples: 3711 download_size: 16242559940 dataset_size: 30064562472 - config_name: mujoco-pendulum features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 353475220 num_examples: 21217 - name: test num_bytes: 39200980 num_examples: 2353 download_size: 164519563 dataset_size: 392676200 - config_name: mujoco-pusher features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 610740000 num_examples: 9000 - name: test num_bytes: 67860000 num_examples: 1000 download_size: 191257969 dataset_size: 678600000 - config_name: mujoco-reacher features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 297396000 num_examples: 9000 - name: test num_bytes: 33044000 num_examples: 1000 download_size: 51166246 dataset_size: 330440000 - config_name: mujoco-standup features: - name: rewards sequence: float64 - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 29206224000 num_examples: 36000 - name: test num_bytes: 3245136000 num_examples: 4000 download_size: 18170498165 dataset_size: 32451360000 - config_name: mujoco-swimmer features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 968400000 num_examples: 36000 - name: test num_bytes: 107600000 num_examples: 4000 download_size: 714619805 dataset_size: 1076000000 - config_name: mujoco-walker features: - name: continuous_observations sequence: sequence: float64 - name: continuous_actions sequence: sequence: float64 - name: rewards sequence: float64 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 1810449300 num_examples: 33825 - name: test num_bytes: 200875572 num_examples: 3753 download_size: 1361666332 dataset_size: 2011324872 - config_name: ok-vqa features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: pixel_values sequence: sequence: sequence: float32 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 5485904424 num_examples: 9009 - name: test num_bytes: 3072691056 num_examples: 5046 download_size: 2461084762 dataset_size: 8558595480 - config_name: oscar features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 84100183340 num_examples: 12612505 - name: test num_bytes: 92225108 num_examples: 13831 download_size: 10790997279 dataset_size: 84192408448 - config_name: wikipedia features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: loss_weight sequence: float64 splits: - name: train num_bytes: 85578352248 num_examples: 12834186 - name: test num_bytes: 84023468 num_examples: 12601 download_size: 10104267816 dataset_size: 85662375716 configs: - config_name: atari-alien data_files: - split: train path: atari-alien/train-* - split: test path: atari-alien/test-* - config_name: atari-amidar data_files: - split: train path: atari-amidar/train-* - split: test path: atari-amidar/test-* - config_name: atari-assault data_files: - split: train path: atari-assault/train-* - split: test path: atari-assault/test-* - config_name: atari-asterix data_files: - split: train path: atari-asterix/train-* - split: test path: atari-asterix/test-* - config_name: atari-asteroids data_files: - split: train path: atari-asteroids/train-* - split: test path: atari-asteroids/test-* - config_name: atari-atlantis data_files: - split: train path: atari-atlantis/train-* - split: test path: atari-atlantis/test-* - config_name: atari-bankheist data_files: - split: train path: atari-bankheist/train-* - split: test path: atari-bankheist/test-* - config_name: atari-battlezone data_files: - split: train path: atari-battlezone/train-* - split: test path: atari-battlezone/test-* - config_name: atari-beamrider data_files: - split: train path: atari-beamrider/train-* - split: test path: atari-beamrider/test-* - config_name: atari-berzerk data_files: - split: train path: atari-berzerk/train-* - split: test path: atari-berzerk/test-* - config_name: atari-bowling data_files: - split: train path: atari-bowling/train-* - split: test path: atari-bowling/test-* - config_name: atari-boxing data_files: - split: train path: atari-boxing/train-* - split: test path: atari-boxing/test-* - config_name: atari-breakout data_files: - split: train path: atari-breakout/train-* - split: test path: atari-breakout/test-* - config_name: atari-centipede data_files: - split: train path: atari-centipede/train-* - split: test path: atari-centipede/test-* - config_name: atari-choppercommand data_files: - split: train path: atari-choppercommand/train-* - split: test path: atari-choppercommand/test-* - config_name: atari-crazyclimber data_files: - split: train path: atari-crazyclimber/train-* - split: test path: atari-crazyclimber/test-* - config_name: atari-defender data_files: - split: train path: atari-defender/train-* - split: test path: atari-defender/test-* - config_name: atari-demonattack data_files: - split: train path: atari-demonattack/train-* - split: test path: atari-demonattack/test-* - config_name: atari-doubledunk data_files: - split: test path: atari-doubledunk/test-* - split: train path: atari-doubledunk/train-* - config_name: atari-enduro data_files: - split: train path: atari-enduro/train-* - split: test path: atari-enduro/test-* - config_name: atari-fishingderby data_files: - split: train path: atari-fishingderby/train-* - split: test path: atari-fishingderby/test-* - config_name: atari-freeway data_files: - split: train path: atari-freeway/train-* - split: test path: atari-freeway/test-* - config_name: atari-frostbite data_files: - split: train path: atari-frostbite/train-* - split: test path: atari-frostbite/test-* - config_name: atari-gopher data_files: - split: train path: atari-gopher/train-* - split: test path: atari-gopher/test-* - config_name: atari-gravitar data_files: - split: train path: atari-gravitar/train-* - split: test path: atari-gravitar/test-* - config_name: atari-hero data_files: - split: train path: atari-hero/train-* - split: test path: atari-hero/test-* - config_name: atari-icehockey data_files: - split: train path: atari-icehockey/train-* - split: test path: atari-icehockey/test-* - config_name: atari-jamesbond data_files: - split: train path: atari-jamesbond/train-* - split: test path: atari-jamesbond/test-* - config_name: atari-kangaroo data_files: - split: train path: atari-kangaroo/train-* - split: test path: atari-kangaroo/test-* - config_name: atari-krull data_files: - split: train path: atari-krull/train-* - split: test path: atari-krull/test-* - config_name: atari-kungfumaster data_files: - split: train path: atari-kungfumaster/train-* - split: test path: atari-kungfumaster/test-* - config_name: atari-montezumarevenge data_files: - split: train path: atari-montezumarevenge/train-* - split: test path: atari-montezumarevenge/test-* - config_name: atari-mspacman data_files: - split: train path: atari-mspacman/train-* - split: test path: atari-mspacman/test-* - config_name: atari-namethisgame data_files: - split: train path: atari-namethisgame/train-* - split: test path: atari-namethisgame/test-* - config_name: atari-phoenix data_files: - split: train path: atari-phoenix/train-* - split: test path: atari-phoenix/test-* - config_name: atari-pitfall data_files: - split: train path: atari-pitfall/train-* - split: test path: atari-pitfall/test-* - config_name: atari-pong data_files: - split: test path: atari-pong/test-* - split: train path: atari-pong/train-* - config_name: atari-privateeye data_files: - split: test path: atari-privateeye/test-* - split: train path: atari-privateeye/train-* - config_name: atari-qbert data_files: - split: test path: atari-qbert/test-* - split: train path: atari-qbert/train-* - config_name: atari-riverraid data_files: - split: test path: atari-riverraid/test-* - split: train path: atari-riverraid/train-* - config_name: atari-roadrunner data_files: - split: test path: atari-roadrunner/test-* - split: train path: atari-roadrunner/train-* - config_name: atari-robotank data_files: - split: test path: atari-robotank/test-* - split: train path: atari-robotank/train-* - config_name: babyai-action-obj-door data_files: - split: train path: babyai-action-obj-door/train-* - split: test path: babyai-action-obj-door/test-* - config_name: babyai-blocked-unlock-pickup data_files: - split: test path: babyai-blocked-unlock-pickup/test-* - split: train path: babyai-blocked-unlock-pickup/train-* - config_name: babyai-boss-level data_files: - split: test path: babyai-boss-level/test-* - split: train path: babyai-boss-level/train-* - config_name: babyai-boss-level-no-unlock data_files: - split: test path: babyai-boss-level-no-unlock/test-* - split: train path: babyai-boss-level-no-unlock/train-* - config_name: babyai-find-obj-s5 data_files: - split: train path: babyai-find-obj-s5/train-* - split: test path: babyai-find-obj-s5/test-* - config_name: babyai-go-to data_files: - split: train path: babyai-go-to/train-* - split: test path: babyai-go-to/test-* - config_name: babyai-go-to-door data_files: - split: train path: babyai-go-to-door/train-* - split: test path: babyai-go-to-door/test-* - config_name: babyai-go-to-imp-unlock data_files: - split: train path: babyai-go-to-imp-unlock/train-* - split: test path: babyai-go-to-imp-unlock/test-* - config_name: babyai-go-to-local data_files: - split: train path: babyai-go-to-local/train-* - split: test path: babyai-go-to-local/test-* - config_name: babyai-go-to-obj data_files: - split: train path: babyai-go-to-obj/train-* - split: test path: babyai-go-to-obj/test-* - config_name: babyai-go-to-obj-door data_files: - split: train path: babyai-go-to-obj-door/train-* - split: test path: babyai-go-to-obj-door/test-* - config_name: babyai-go-to-red-ball data_files: - split: train path: babyai-go-to-red-ball/train-* - split: test path: babyai-go-to-red-ball/test-* - config_name: babyai-go-to-red-ball-grey data_files: - split: train path: babyai-go-to-red-ball-grey/train-* - split: test path: babyai-go-to-red-ball-grey/test-* - config_name: babyai-go-to-red-ball-no-dists data_files: - split: train path: babyai-go-to-red-ball-no-dists/train-* - split: test path: babyai-go-to-red-ball-no-dists/test-* - config_name: babyai-go-to-red-blue-ball data_files: - split: train path: babyai-go-to-red-blue-ball/train-* - split: test path: babyai-go-to-red-blue-ball/test-* - config_name: babyai-go-to-seq data_files: - split: train path: babyai-go-to-seq/train-* - split: test path: babyai-go-to-seq/test-* - config_name: babyai-key-corridor data_files: - split: test path: babyai-key-corridor/test-* - split: train path: babyai-key-corridor/train-* - config_name: babyai-mini-boss-level data_files: - split: test path: babyai-mini-boss-level/test-* - split: train path: babyai-mini-boss-level/train-* - config_name: babyai-move-two-across-s8n9 data_files: - split: test path: babyai-move-two-across-s8n9/test-* - split: train path: babyai-move-two-across-s8n9/train-* - config_name: babyai-one-room-s8 data_files: - split: test path: babyai-one-room-s8/test-* - split: train path: babyai-one-room-s8/train-* - config_name: babyai-open data_files: - split: test path: babyai-open/test-* - split: train path: babyai-open/train-* - config_name: babyai-open-door data_files: - split: test path: babyai-open-door/test-* - split: train path: babyai-open-door/train-* - config_name: babyai-open-doors-order-n4 data_files: - split: test path: babyai-open-doors-order-n4/test-* - split: train path: babyai-open-doors-order-n4/train-* - config_name: babyai-open-red-door data_files: - split: test path: babyai-open-red-door/test-* - split: train path: babyai-open-red-door/train-* - config_name: babyai-open-two-doors data_files: - split: test path: babyai-open-two-doors/test-* - split: train path: babyai-open-two-doors/train-* - config_name: babyai-pickup data_files: - split: test path: babyai-pickup/test-* - split: train path: babyai-pickup/train-* - config_name: babyai-pickup-above data_files: - split: test path: babyai-pickup-above/test-* - split: train path: babyai-pickup-above/train-* - config_name: babyai-pickup-dist data_files: - split: test path: babyai-pickup-dist/test-* - split: train path: babyai-pickup-dist/train-* - config_name: babyai-pickup-loc data_files: - split: test path: babyai-pickup-loc/test-* - split: train path: babyai-pickup-loc/train-* - config_name: babyai-put-next data_files: - split: train path: babyai-put-next/train-* - split: test path: babyai-put-next/test-* - config_name: babyai-put-next-local data_files: - split: train path: babyai-put-next-local/train-* - split: test path: babyai-put-next-local/test-* - config_name: babyai-synth-loc data_files: - split: test path: babyai-synth-loc/test-* - split: train path: babyai-synth-loc/train-* - config_name: babyai-synth-seq data_files: - split: test path: babyai-synth-seq/test-* - split: train path: babyai-synth-seq/train-* - config_name: babyai-unblock-pickup data_files: - split: test path: babyai-unblock-pickup/test-* - split: train path: babyai-unblock-pickup/train-* - config_name: babyai-unlock data_files: - split: train path: babyai-unlock/train-* - split: test path: babyai-unlock/test-* - config_name: babyai-unlock-local data_files: - split: test path: babyai-unlock-local/test-* - split: train path: babyai-unlock-local/train-* - config_name: babyai-unlock-pickup data_files: - split: test path: babyai-unlock-pickup/test-* - split: train path: babyai-unlock-pickup/train-* - config_name: babyai-unlock-to-unlock data_files: - split: train path: babyai-unlock-to-unlock/train-* - split: test path: babyai-unlock-to-unlock/test-* - config_name: conceptual-captions data_files: - split: test path: conceptual-captions/test-* - split: train path: conceptual-captions/train-* - config_name: metaworld-assembly data_files: - split: train path: metaworld-assembly/train-* - split: test path: metaworld-assembly/test-* - config_name: metaworld-basketball data_files: - split: train path: metaworld-basketball/train-* - split: test path: metaworld-basketball/test-* - config_name: metaworld-bin-picking data_files: - split: train path: metaworld-bin-picking/train-* - split: test path: metaworld-bin-picking/test-* - config_name: metaworld-box-close data_files: - split: train path: metaworld-box-close/train-* - split: test path: metaworld-box-close/test-* - config_name: metaworld-button-press data_files: - split: train path: metaworld-button-press/train-* - split: test path: metaworld-button-press/test-* - config_name: metaworld-button-press-topdown data_files: - split: train path: metaworld-button-press-topdown/train-* - split: test path: metaworld-button-press-topdown/test-* - config_name: metaworld-button-press-topdown-wall data_files: - split: train path: metaworld-button-press-topdown-wall/train-* - split: test path: metaworld-button-press-topdown-wall/test-* - config_name: metaworld-button-press-wall data_files: - split: train path: metaworld-button-press-wall/train-* - split: test path: metaworld-button-press-wall/test-* - config_name: metaworld-coffee-button data_files: - split: train path: metaworld-coffee-button/train-* - split: test path: metaworld-coffee-button/test-* - config_name: metaworld-coffee-pull data_files: - split: train path: metaworld-coffee-pull/train-* - split: test path: metaworld-coffee-pull/test-* - config_name: metaworld-coffee-push data_files: - split: train path: metaworld-coffee-push/train-* - split: test path: metaworld-coffee-push/test-* - config_name: metaworld-dial-turn data_files: - split: train path: metaworld-dial-turn/train-* - split: test path: metaworld-dial-turn/test-* - config_name: metaworld-disassemble data_files: - split: train path: metaworld-disassemble/train-* - split: test path: metaworld-disassemble/test-* - config_name: metaworld-door-close data_files: - split: train path: metaworld-door-close/train-* - split: test path: metaworld-door-close/test-* - config_name: metaworld-door-lock data_files: - split: train path: metaworld-door-lock/train-* - split: test path: metaworld-door-lock/test-* - config_name: metaworld-door-open data_files: - split: train path: metaworld-door-open/train-* - split: test path: metaworld-door-open/test-* - config_name: metaworld-door-unlock data_files: - split: train path: metaworld-door-unlock/train-* - split: test path: metaworld-door-unlock/test-* - config_name: metaworld-drawer-close data_files: - split: train path: metaworld-drawer-close/train-* - split: test path: metaworld-drawer-close/test-* - config_name: metaworld-drawer-open data_files: - split: train path: metaworld-drawer-open/train-* - split: test path: metaworld-drawer-open/test-* - config_name: metaworld-faucet-close data_files: - split: train path: metaworld-faucet-close/train-* - split: test path: metaworld-faucet-close/test-* - config_name: metaworld-faucet-open data_files: - split: train path: metaworld-faucet-open/train-* - split: test path: metaworld-faucet-open/test-* - config_name: metaworld-hammer data_files: - split: train path: metaworld-hammer/train-* - split: test path: metaworld-hammer/test-* - config_name: metaworld-hand-insert data_files: - split: train path: metaworld-hand-insert/train-* - split: test path: metaworld-hand-insert/test-* - config_name: metaworld-handle-press data_files: - split: train path: metaworld-handle-press/train-* - split: test path: metaworld-handle-press/test-* - config_name: metaworld-handle-press-side data_files: - split: train path: metaworld-handle-press-side/train-* - split: test path: metaworld-handle-press-side/test-* - config_name: metaworld-handle-pull data_files: - split: train path: metaworld-handle-pull/train-* - split: test path: metaworld-handle-pull/test-* - config_name: metaworld-handle-pull-side data_files: - split: train path: metaworld-handle-pull-side/train-* - split: test path: metaworld-handle-pull-side/test-* - config_name: metaworld-lever-pull data_files: - split: train path: metaworld-lever-pull/train-* - split: test path: metaworld-lever-pull/test-* - config_name: metaworld-peg-insert-side data_files: - split: train path: metaworld-peg-insert-side/train-* - split: test path: metaworld-peg-insert-side/test-* - config_name: metaworld-peg-unplug-side data_files: - split: train path: metaworld-peg-unplug-side/train-* - split: test path: metaworld-peg-unplug-side/test-* - config_name: metaworld-pick-out-of-hole data_files: - split: train path: metaworld-pick-out-of-hole/train-* - split: test path: metaworld-pick-out-of-hole/test-* - config_name: metaworld-pick-place data_files: - split: train path: metaworld-pick-place/train-* - split: test path: metaworld-pick-place/test-* - config_name: metaworld-pick-place-wall data_files: - split: train path: metaworld-pick-place-wall/train-* - split: test path: metaworld-pick-place-wall/test-* - config_name: metaworld-plate-slide data_files: - split: train path: metaworld-plate-slide/train-* - split: test path: metaworld-plate-slide/test-* - config_name: metaworld-plate-slide-back data_files: - split: train path: metaworld-plate-slide-back/train-* - split: test path: metaworld-plate-slide-back/test-* - config_name: metaworld-plate-slide-back-side data_files: - split: train path: metaworld-plate-slide-back-side/train-* - split: test path: metaworld-plate-slide-back-side/test-* - config_name: metaworld-plate-slide-side data_files: - split: train path: metaworld-plate-slide-side/train-* - split: test path: metaworld-plate-slide-side/test-* - config_name: metaworld-push data_files: - split: train path: metaworld-push/train-* - split: test path: metaworld-push/test-* - config_name: metaworld-push-back data_files: - split: train path: metaworld-push-back/train-* - split: test path: metaworld-push-back/test-* - config_name: metaworld-push-wall data_files: - split: train path: metaworld-push-wall/train-* - split: test path: metaworld-push-wall/test-* - config_name: metaworld-reach data_files: - split: train path: metaworld-reach/train-* - split: test path: metaworld-reach/test-* - config_name: metaworld-reach-wall data_files: - split: train path: metaworld-reach-wall/train-* - split: test path: metaworld-reach-wall/test-* - config_name: metaworld-shelf-place data_files: - split: train path: metaworld-shelf-place/train-* - split: test path: metaworld-shelf-place/test-* - config_name: metaworld-soccer data_files: - split: train path: metaworld-soccer/train-* - split: test path: metaworld-soccer/test-* - config_name: metaworld-stick-pull data_files: - split: train path: metaworld-stick-pull/train-* - split: test path: metaworld-stick-pull/test-* - config_name: metaworld-stick-push data_files: - split: train path: metaworld-stick-push/train-* - split: test path: metaworld-stick-push/test-* - config_name: metaworld-sweep data_files: - split: train path: metaworld-sweep/train-* - split: test path: metaworld-sweep/test-* - config_name: metaworld-sweep-into data_files: - split: train path: metaworld-sweep-into/train-* - split: test path: metaworld-sweep-into/test-* - config_name: metaworld-window-close data_files: - split: train path: metaworld-window-close/train-* - split: test path: metaworld-window-close/test-* - config_name: metaworld-window-open data_files: - split: train path: metaworld-window-open/train-* - split: test path: metaworld-window-open/test-* - config_name: mujoco-ant data_files: - split: train path: mujoco-ant/train-* - split: test path: mujoco-ant/test-* - config_name: mujoco-doublependulum data_files: - split: train path: mujoco-doublependulum/train-* - split: test path: mujoco-doublependulum/test-* - config_name: mujoco-halfcheetah data_files: - split: train path: mujoco-halfcheetah/train-* - split: test path: mujoco-halfcheetah/test-* - config_name: mujoco-hopper data_files: - split: train path: mujoco-hopper/train-* - split: test path: mujoco-hopper/test-* - config_name: mujoco-humanoid data_files: - split: train path: mujoco-humanoid/train-* - split: test path: mujoco-humanoid/test-* - config_name: mujoco-pendulum data_files: - split: train path: mujoco-pendulum/train-* - split: test path: mujoco-pendulum/test-* - config_name: mujoco-pusher data_files: - split: train path: mujoco-pusher/train-* - split: test path: mujoco-pusher/test-* - config_name: mujoco-reacher data_files: - split: train path: mujoco-reacher/train-* - split: test path: mujoco-reacher/test-* - config_name: mujoco-standup data_files: - split: train path: mujoco-standup/train-* - split: test path: mujoco-standup/test-* - config_name: mujoco-swimmer data_files: - split: train path: mujoco-swimmer/train-* - split: test path: mujoco-swimmer/test-* - config_name: mujoco-walker data_files: - split: train path: mujoco-walker/train-* - split: test path: mujoco-walker/test-* - config_name: ok-vqa data_files: - split: train path: ok-vqa/train-* - split: test path: ok-vqa/test-* - config_name: oscar data_files: - split: train path: oscar/train-* - split: test path: oscar/test-* - config_name: wikipedia data_files: - split: train path: wikipedia/train-* - split: test path: wikipedia/test-* --- # Dataset Card for "jat-dataset-tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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minimario/livecodebench-execute-v2
minimario
2023-12-17T03:57:09Z
567
0
[ "region:us" ]
null
2023-12-17T03:44:26Z
--- dataset_info: features: - name: id sequence: int64 - name: function_name dtype: string - name: code dtype: string - name: input dtype: string - name: output dtype: string splits: - name: test num_bytes: 838050 num_examples: 2069 download_size: 124406 dataset_size: 838050 configs: - config_name: default data_files: - split: test path: data/test-* ---
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ktrinh38/fashion-dataset
ktrinh38
2023-12-11T18:10:18Z
538
0
[ "region:us" ]
null
2023-12-11T18:02:50Z
--- dataset_info: features: - name: id dtype: int32 - name: brandName dtype: string - name: ageGroup dtype: string - name: gender dtype: string - name: baseColour dtype: string - name: season dtype: string - name: usage dtype: string - name: displayCategories dtype: string - name: productDisplayName dtype: string - name: image dtype: image splits: - name: train num_bytes: 20771830465.821 num_examples: 44441 download_size: 12665598994 dataset_size: 20771830465.821 configs: - config_name: default data_files: - split: train path: data/train-* ---
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argilla/ultrafeedback-binarized-preferences-cleaned
argilla
2023-12-11T14:22:19Z
536
9
[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:mit", "dpo", "preference", "ultrafeedback", "region:us" ]
[ "text-generation" ]
2023-12-05T11:07:34Z
--- language: - en license: mit size_categories: - 10K<n<100K task_categories: - text-generation pretty_name: UltraFeedback Binarized Preferences Cleaned dataset_info: features: - name: source dtype: string - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: chosen-rating dtype: float64 - name: chosen-model dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: rejected-rating dtype: float64 - name: rejected-model dtype: string splits: - name: train num_bytes: 284937773 num_examples: 60917 download_size: 143257393 dataset_size: 284937773 configs: - config_name: default data_files: - split: train path: data/train-* tags: - dpo - preference - ultrafeedback --- # UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned) This dataset represents a new iteration on top of [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/argilla/ultrafeedback-binarized-preferences), and is the **recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback**. Read more about Argilla's approach towards UltraFeedback binarization at [`argilla/ultrafeedback-binarized-preferences/README.md`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences/blob/main/README.md). ## Differences with `argilla/ultrafeedback-binarized-preferences` Thanks to the recent issue identified by [AllenAI](https://huggingface.co/allenai) related to the TruthfulQA contamination within the original UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking in the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) from HuggingFace H4), we also decided to follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which implied using the average of the preference ratings rather than the critique overall score, as [`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) did. Besides that, we also saw that not only the rows with the `source=truthful_qa` were contamined (for obvious reasons), but also some coming from ShareGPT, so we also removed those doing a left join with both subsets from the [`truthful_qa`](https://huggingface.co/datasets/truthful_qa) dataset. Additionally, we also modified the formatting to be aligned with both [`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), and [`allenai/ultrafeedback_binarized_cleaned`](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned) in order to ease the integration within the [`huggingface/alignment-handbook`](https://github.com/huggingface/alignment-handbook) so that the formatting is standardized. ## Reproduce <a target="_blank" href="https://colab.research.google.com/drive/1XR9P1St4yTNY0tjti_tIjm-yzP5Bfqc0?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> To reproduce the data processing combining both our approach and the suggestions from HuggingFace H4 w.r.t. the formatting and the ones from AllenAI to remove the TruthfulQA contamination, feel free to run the attached Colab Notebook or just view it at [`notebook.ipynb`](./notebook.ipynb) within this repository. From Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as ourselves, as well as the whole community, benefit a lot from open source and we also want to give back. ## Citation If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: https://huggingface.co/datasets/openbmb/UltraFeedback Additionally, you may also want to cite our work with Notus 7B, which lead the curation of the UltraFeedback dataset: ```bibtex @misc{notus2023, author = {Alvaro Bartolome and Gabriel Martin and Daniel Vila}, title = {Notus}, year = {2023}, publisher = {GitHub}, journal = {GitHub Repository}, howpublished = {\url{https://github.com/argilla-io/notus}} } ``` > Alphabetically ordered by last name due to equal contribution.
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llm-book/livedoor-news-corpus
llm-book
2023-12-12T02:19:43Z
503
3
[ "task_categories:summarization", "size_categories:1K<n<10K", "language:ja", "news", "region:us" ]
[ "summarization" ]
2023-06-21T07:16:52Z
--- task_categories: - summarization language: - ja tags: - news pretty_name: livedoor-news-corpus size_categories: - 1K<n<10K --- # Dataset Card for llm-book/ner-wikinews-dataset 書籍『大規模言語モデル入門』で使用する、株式会社ロンウイットが提供する「livedoorニュースコーパス」によるデータセットです。 [オリジナルのサイト](https://www.rondhuit.com/download.html)と同じものを使用しています。 本コーパスは、NHN Japan株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限りHTMLタグを取り除いて作成したものです。 ### Licence Attribution-NoDerivs 2.1 Japan (CC BY-ND 2.1 JP) License
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yashraizada/yelp-open-dataset-top-businesses
yashraizada
2023-12-12T04:30:03Z
490
0
[ "license:apache-2.0", "region:us" ]
null
2023-12-09T19:19:49Z
--- license: apache-2.0 ---
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jinaai/code_search_net_clean
jinaai
2023-12-11T10:42:18Z
460
0
[ "region:us" ]
null
2023-11-15T17:58:52Z
--- dataset_info: features: - name: doc dtype: string - name: code dtype: string splits: - name: train.go num_bytes: 196497807 num_examples: 311516 - name: train.java num_bytes: 404530092 num_examples: 417492 - name: train.javascript num_bytes: 128940104 num_examples: 109037 - name: train.php num_bytes: 414721432 num_examples: 466044 - name: train.python num_bytes: 415251838 num_examples: 389480 - name: train.ruby num_bytes: 36223265 num_examples: 45902 - name: test.go num_bytes: 8383012 num_examples: 14079 - name: test.java num_bytes: 22856950 num_examples: 24246 - name: test.javascript num_bytes: 6602003 num_examples: 5706 - name: test.php num_bytes: 21589553 num_examples: 25139 - name: test.python num_bytes: 22653368 num_examples: 21066 - name: test.ruby num_bytes: 1652983 num_examples: 2085 - name: validation.go num_bytes: 6844448 num_examples: 14051 - name: validation.java num_bytes: 12179837 num_examples: 14179 - name: validation.javascript num_bytes: 7791459 num_examples: 6995 - name: validation.php num_bytes: 20661926 num_examples: 22931 - name: validation.python num_bytes: 24904263 num_examples: 21955 - name: validation.ruby num_bytes: 1504017 num_examples: 2110 download_size: 632494450 dataset_size: 1753788357 configs: - config_name: default data_files: - split: train.go path: data/train.go-* - split: train.java path: data/train.java-* - split: train.javascript path: data/train.javascript-* - split: train.php path: data/train.php-* - split: train.python path: data/train.python-* - split: train.ruby path: data/train.ruby-* - split: test.go path: data/test.go-* - split: test.java path: data/test.java-* - split: test.javascript path: data/test.javascript-* - split: test.php path: data/test.php-* - split: test.python path: data/test.python-* - split: test.ruby path: data/test.ruby-* - split: validation.go path: data/validation.go-* - split: validation.java path: data/validation.java-* - split: validation.javascript path: data/validation.javascript-* - split: validation.php path: data/validation.php-* - split: validation.python path: data/validation.python-* - split: validation.ruby path: data/validation.ruby-* --- # Dataset Card for "code_search_net_clean" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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Siki-77/twitter2017
Siki-77
2023-12-17T09:03:49Z
428
0
[ "license:apache-2.0", "region:us" ]
null
2023-12-11T14:18:59Z
--- license: apache-2.0 --- ### dataset source excellent sharing: https://github.com/cbaziotis/datastories-semeval2017-task4/tree/master/dataset/Subtask_A/4A-English task government: https://alt.qcri.org/semeval2017/task4/index.php?id=data-and-tools
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Siki-77/amazon6_polarity
Siki-77
2023-12-17T08:40:51Z
413
0
[ "license:apache-2.0", "region:us" ]
null
2023-11-21T12:59:37Z
--- license: apache-2.0 ---
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gmongaras/BERT_Base_Cased_512_GLUE_Mapped
gmongaras
2023-12-11T20:34:53Z
394
0
[ "region:us" ]
null
2023-12-11T20:34:01Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: label dtype: float64 - name: dataset_name dtype: string - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 232895922 num_examples: 949728 - name: validation num_bytes: 17255970 num_examples: 69711 - name: test num_bytes: 96102951 num_examples: 425205 download_size: 123150665 dataset_size: 346254843 --- Original Dataset from: https://huggingface.co/datasets/glue This dataset is adapted from https://huggingface.co/datasets/gmongaras/BERT_Base_Cased_512_GLUE Every split besides the ax split is in this dataset. Lines above 512 tokens from the BERT-cased (bert-base-cased) tokenizer are removed in the original dataset If in any case the sentences are longer than 512 tokens, they are subsetted. Original labels and dataset categories are retained.
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Astris/Nectar-DeDup-Embed
Astris
2023-12-13T07:08:39Z
387
0
[ "region:us" ]
null
2023-12-13T03:48:23Z
--- dataset_info: features: - name: answer dtype: string - name: model dtype: string - name: rank dtype: uint16 - name: vector sequence: float32 splits: - name: train num_bytes: 5985643054 num_examples: 1197239 download_size: 2896281236 dataset_size: 5985643054 configs: - config_name: default data_files: - split: train path: data/train-* --- Berkeley Nest's Nectar dataset, with prompts removed and duplicate responses (regardless of model) removed. Vector embeddings were generated with BGE-Large-EN-V1.5
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IES-Rafael-Alberti/letras-carnaval-cadiz
IES-Rafael-Alberti
2023-12-13T19:57:16Z
363
2
[ "annotations_creators:no-annotation", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:es", "license:cc-by-sa-4.0", "lyrics", "carnival", "cadiz", "region:us" ]
[]
2023-04-04T10:34:51Z
--- annotations_creators: - no-annotation language: - es language_creators: - machine-generated license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: letrascarnavalcadiz size_categories: - 1K<n<10K source_datasets: - original tags: - lyrics - carnival - cadiz task_categories: [] task_ids: [] --- # Dataset Card for Letras Carnaval Cádiz ![logo](https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz/resolve/main/assets/logo.svg) <h4 align="center"> <p> <b>English</b> | <a href="https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz/blob/main/README_es.md">Español</a> <p> </h4> ## Dataset Description - **Homepage:** https://letrascarnavalcadiz.com - **Repository:** https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz - **Point of Contact:** [email protected] ### Changelog |Release|Description| |-|-| |v1.0| Initial release of the dataset. Included more than 1K lyrics. It is necessary to verify the accuracy of the data, especially the subset midaccurate. | ### Dataset Summary This dataset is a comprehensive collection of lyrics from the Carnaval de Cádiz, a significant cultural heritage of the city of Cádiz, Spain. Despite its cultural importance, there has been a lack of a structured database for these lyrics, hindering research and public access to this cultural heritage. This dataset aims to address this gap. The dataset was created by the Cádiz AI Learning Community, a branch of the non-profit association Spain AI, and was developed by Iván Romero Reyna and Jesús Federico Franco Medinilla, students of the Specialization Course in Artificial Intelligence and Big Data at IES Rafael Alberti during the 2022-2023 academic year. The project is supervised by Jesús Carlos Avecilla de la Herrán, a computational linguist. Collaboration is encouraged, with individuals able to verify the different records of the dataset at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com), ensuring the transcription of the lyrics and all data are correct. New lyrics can also be added to the dataset. Corrections and additions are not immediately reflected in the dataset but are updated periodically. For more information or to report a problem, you can write to [[email protected]](mailto:[email protected]). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is in Spanish, reflecting the language of the Carnaval de Cádiz. ## Dataset Structure ### Data Instances A typical instance in the dataset is formatted in JSON and contains the following fields: ```json { "id": "9de8647521b728c45ff45c1c11208708d055397fd7781b31cf91b473dff224d5", "authors": ["Juan Carlos Aragón Becerra"], "song_type": 2, "year": "2018", "group": "Los Mafiosos", "group_type": 2, "lyrics": [ "Mujer va llegando el momento", "de ser la que lleve la rienda", "el camino ha sido largo y polvoriento", "pero ya no habrá varón que te detenga", "gritad larga vida a la reina", "que va a comenzar tu gobierno", "ojalá no heredes nada", "de aquel macho que te odiaba", "porque en el fondo sabía", "que ya tú te le acercabas", "y el contigo no podía", "ten en cuenta cuando hagas justicia", "de volver a nivelar la balanza", "y aguantar aunque tragando saliva", "el deseo de venganza", "de ser oh humano fatal", "de ser o que puedo entender", "tan solo con una mirada", "la llaga que baña tu alma y tu piel", "que te sirva la experiencia", "del macho de la manada", "la fuerza no vale nada", "si no es con la inteligencia", "y ojalá que tu conciencia", "a mí me brinde la suerte", "de nunca volver a verte", "con los pies en una iglesia", "que ella fue quien escribió", "que ella fue quien escribió", "la historia contra vosotras", "y encima se la cobró", "y encima se la cobró", "con mil millones de devotas", "ojalá que tu corona y tu bandera", "abran paso a una vida nueva", "como un mundo en primavera", "ojalá que a ti no te envenene el poder", "y que no dejes nunca de ser la mujer", "que siempre fue nuestra gran compañera" ] } ``` The `id` field uniquely identifies each instance in the dataset, providing a way to reference specific entries. The `authors`, `song_type`, `year`, `group`, and `group_type` fields provide context for the lyrics, while the `lyrics` field itself contains the actual text of the song. The relationships between these fields are implicit in the structure of the dataset, with each instance representing a single song from the Carnaval de Cádiz. ### Data Fields `id` Unique identifier for each song in the dataset. A SHA-256 hash calculated from the first four verses of the lyrics and the group name, with all spaces removed and converted to lowercase (string). `authors` List of authors who have written the song (string array). `song_type` The type of song (1: presentación, 2: pasodoble/tango, 3: cuplé, 4: estribillo, 5: popurrí, 6: cuarteta). `year` Year the song was written or performed (string). `group` Name of the group that performed the song (string). `group_type` The type of the group (1: coro, 2: comparsa, 3: chirigota, 4: cuarteto). `lyrics` The lyrics of the song, represented as an array of verses (string array). ### Data Splits This dataset does not have traditional training, validation, and test splits. Instead, it is divided into two subsets: "accurate" and "midaccurate". The "accurate" subset contains 958 instances. All fields of first 957 instances in this subset have been obtained through web scraping and have undergone at least one human review for accuracy. The rest have been added by users at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com). The "midaccurate" subset contains 226 instances. The 'group' and 'lyrics' fields in this subset were collected through web scraping, but the remaining fields were filled in by querying language models connected to the Internet. Therefore, the data in these fields may not be accurate. | Subset | Instances | |-------------|----------:| | Accurate | 958 | | Midaccurate | 226 | Please note that the division into subsets is based on the method and reliability of data collection, rather than a random or stratified split typically used in machine learning tasks. Users of the dataset should consider this when deciding how to use the data. ## Dataset Creation ### Curation Rationale The dataset was created to address a significant need in the cultural heritage of the city of Cádiz, Spain. The Carnaval de Cádiz is a major cultural event, yet there was no structured database of its lyrics that could be consulted for research or public access. This lack of a structured database hindered the exploration and appreciation of this cultural heritage. The dataset was curated to respond to this need. ### Source Data #### Initial Data Collection and Normalization The initial collection of lyrics was carried out through automatic scraping of various websites and multimedia content on the Internet. To maximize the number of records with minimal effort, all collection is being done using different Artificial Intelligence models. #### Who are the source language producers? The source language producers of the dataset are the authors and performers of the songs from the Carnaval de Cádiz. These include a wide range of individuals and groups who have participated in the Carnaval over the years. The dataset does not include self-reported demographic or identity information for these individuals or groups. The data in the dataset was collected from two websites: https://www.alsondelcarnaval.es and http://letrasdesdeelparaiso.blogspot.com. The first 957 instances of "accurate" subset of the dataset was collected from the former, while the "midaccurate" subset was collected from the latter. The data was extracted through automatic web scraping, and in the case of the "midaccurate" subset, some fields were filled in by querying language models connected to the Internet. The rest of "accurate" subset have been added by users at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com). ### Personal and Sensitive Information The only sensitive information in the dataset is the names and surnames of the authors of the lyrics. ## Considerations for Using the Data ### Social Impact of Dataset The use of this dataset has significant social impact. Firstly, this dataset can positively contribute to the understanding and preservation of Cadiz's culture and traditions, as the Carnaval de Cádiz is an integral part of the city's cultural identity. By providing an accessible and easily searchable resource for carnival song lyrics, this dataset can assist cultural researchers, linguists, and the general public in better understanding and appreciating the rich tradition of the Carnaval de Cádiz. Additionally, this dataset can be utilized to enhance natural language processing (NLP) technologies in Spanish, a language that can sometimes be underrepresented in NLP research. By providing a high-quality, culture-specific Spanish text corpus, this dataset can aid in improving the accuracy and cultural relevance of Spanish NLP models. However, there are also risks associated with the use of this dataset. For instance, if used to train text generation models, these models could generate content that reinforces cultural stereotypes or perpetuates existing biases. Moreover, the automatic interpretation of carnival song lyrics can be challenging due to cultural and linguistic subtleties, and errors in this interpretation could lead to misunderstandings or misrepresentations of Cadiz's culture. Finally, although this dataset does not contain a low-resource or underrepresented language, it does focus on a specific cultural tradition from a specific region of Spain. Therefore, its use can impact the Cadiz community by helping to preserve and disseminate its unique culture and traditions. ### Discussion of Biases The dataset is subject to several biases due to the nature of the data collection and the historical context of the Cadiz Carnival. Firstly, there is a temporal bias in the dataset. More recent lyrics are overrepresented compared to older ones, as there is more information available on the internet about modern groups. This may lead to a skewed understanding of the evolution of the Carnival's themes over time. Secondly, the dataset exhibits a popularity bias. Lyrics from more popular groups are overrepresented because individuals have chosen to write about them more frequently. This could potentially limit the diversity of styles and themes represented in the dataset. Thirdly, there is a competition bias. Lyrics from groups that advanced further in the competition stages are overrepresented, resulting in more available lyrics from these groups. This might lead to an overemphasis on the styles and themes that tend to be more successful in the competition. Lastly, the dataset reflects a gender bias. Given that there have historically been more male authors than female authors in the Cadiz Carnival, the majority of the dataset consists of lyrics written by men. This could potentially limit the representation of diverse perspectives and themes in the lyrics. To mitigate these biases, we actively encourage the participation of the community. By verifying the different records of the dataset, reviewing the transcription of the lyrics and all the data for accuracy, and adding new lyrics, we hope to broaden the diversity and representation. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators - Iván Romero Reyna. Student of the Specialisation Course in Artificial Intelligence and Big Data at [IES Rafael Alberti](https://iesrafaelalberti.es) during the academic year 2022-2023. - Jesús Federico Franco Medinilla. Student of the Specialisation Course in Artificial Intelligence and Big Data at [IES Rafael Alberti](https://iesrafaelalberti.es) during the academic year 2022-2023. - Jesús Carlos Avecilla de la Herrán. Promoter in [Cádiz AI](https://www.spain-ai.com). ### Licensing Information [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0) ### Citation Information ``` @misc{letrascarnavalcadiz2023, author = {Romero Reyna, Iván and Franco Medinilla, Jesús Federico and Avecilla de la Herrán, Jesús Carlos}, title = {letras-carnaval-cadiz}, year = {2023}, url = {https://huggingface.co/datasets/IES-Rafael-Alberti/letras-carnaval-cadiz} } ``` ### Contributions Thanks to [@ivanro](https://huggingface.co/ivanro), [@jframed281](https://huggingface.co/jframed281) for adding this dataset. Thanks to all the reviewers and contributors at [letrascarnavalcadiz.com](https://letrascarnavalcadiz.com).
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open-llm-leaderboard/details_gpt2
open-llm-leaderboard
2023-12-16T13:35:31Z
339
0
[ "region:us" ]
null
2023-08-18T14:35:26Z
--- pretty_name: Evaluation run of gpt2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [gpt2](https://huggingface.co/gpt2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 65 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 19 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_gpt2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T13:32:55.332102](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2/blob/main/results_2023-12-16T13-32-55.332102.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\": 0.2578870030806924,\n\ \ \"acc_stderr\": 0.030639295152135662,\n \"acc_norm\": 0.2586921012373598,\n\ \ \"acc_norm_stderr\": 0.031410248419889694,\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4073203809998297,\n\ \ \"mc2_stderr\": 0.014931113118872399\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.19880546075085323,\n \"acc_stderr\": 0.011662850198175539,\n\ \ \"acc_norm\": 0.21928327645051193,\n \"acc_norm_stderr\": 0.012091245787615723\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.29267078271260705,\n\ \ \"acc_stderr\": 0.004540586983229993,\n \"acc_norm\": 0.3158733320055766,\n\ \ \"acc_norm_stderr\": 0.004639126951051421\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n\ \ \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n\ \ \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.0301675334686327,\n\ \ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.0301675334686327\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.16,\n\ \ \"acc_stderr\": 0.03684529491774711,\n \"acc_norm\": 0.16,\n \ \ \"acc_norm_stderr\": 0.03684529491774711\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.23773584905660378,\n \"acc_stderr\": 0.02619980880756194,\n\ \ \"acc_norm\": 0.23773584905660378,\n \"acc_norm_stderr\": 0.02619980880756194\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \ \ \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\"\ : 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-college_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\": 0.23699421965317918,\n\ \ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n\ \ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.16,\n\ \ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.2765957446808511,\n \"acc_stderr\": 0.029241883869628834,\n\ \ \"acc_norm\": 0.2765957446808511,\n \"acc_norm_stderr\": 0.029241883869628834\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n\ \ \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n\ \ \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.14285714285714285,\n\ \ \"acc_stderr\": 0.0312984318574381,\n \"acc_norm\": 0.14285714285714285,\n\ \ \"acc_norm_stderr\": 0.0312984318574381\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.15,\n \"acc_stderr\": 0.035887028128263686,\n \ \ \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.035887028128263686\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.3,\n \"acc_stderr\": 0.026069362295335137,\n \"acc_norm\": 0.3,\n\ \ \"acc_norm_stderr\": 0.026069362295335137\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.26108374384236455,\n \"acc_stderr\": 0.030903796952114475,\n\ \ \"acc_norm\": 0.26108374384236455,\n \"acc_norm_stderr\": 0.030903796952114475\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\"\ : 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.35353535353535354,\n \"acc_stderr\": 0.03406086723547153,\n \"\ acc_norm\": 0.35353535353535354,\n \"acc_norm_stderr\": 0.03406086723547153\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.36787564766839376,\n \"acc_stderr\": 0.03480175668466036,\n\ \ \"acc_norm\": 0.36787564766839376,\n \"acc_norm_stderr\": 0.03480175668466036\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2794871794871795,\n \"acc_stderr\": 0.022752388839776826,\n\ \ \"acc_norm\": 0.2794871794871795,\n \"acc_norm_stderr\": 0.022752388839776826\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073838,\n \ \ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073838\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.28991596638655465,\n \"acc_stderr\": 0.029472485833136098,\n\ \ \"acc_norm\": 0.28991596638655465,\n \"acc_norm_stderr\": 0.029472485833136098\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763744,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763744\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.3486238532110092,\n \"acc_stderr\": 0.020431254090714328,\n \"\ acc_norm\": 0.3486238532110092,\n \"acc_norm_stderr\": 0.020431254090714328\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\": 0.2549019607843137,\n\ \ \"acc_stderr\": 0.030587591351604243,\n \"acc_norm\": 0.2549019607843137,\n\ \ \"acc_norm_stderr\": 0.030587591351604243\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n\ \ \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3004484304932735,\n\ \ \"acc_stderr\": 0.030769352008229143,\n \"acc_norm\": 0.3004484304932735,\n\ \ \"acc_norm_stderr\": 0.030769352008229143\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.038808483010823944,\n\ \ \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.038808483010823944\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.32231404958677684,\n \"acc_stderr\": 0.04266416363352168,\n \"\ acc_norm\": 0.32231404958677684,\n \"acc_norm_stderr\": 0.04266416363352168\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.04059867246952688,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.04059867246952688\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.34951456310679613,\n \"acc_stderr\": 0.04721188506097173,\n\ \ \"acc_norm\": 0.34951456310679613,\n \"acc_norm_stderr\": 0.04721188506097173\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.1752136752136752,\n\ \ \"acc_stderr\": 0.02490443909891822,\n \"acc_norm\": 0.1752136752136752,\n\ \ \"acc_norm_stderr\": 0.02490443909891822\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.21839080459770116,\n\ \ \"acc_stderr\": 0.01477435831993449,\n \"acc_norm\": 0.21839080459770116,\n\ \ \"acc_norm_stderr\": 0.01477435831993449\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.23699421965317918,\n \"acc_stderr\": 0.02289408248992599,\n\ \ \"acc_norm\": 0.23699421965317918,\n \"acc_norm_stderr\": 0.02289408248992599\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.023805186524888146,\n\ \ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.023805186524888146\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.24758842443729903,\n\ \ \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.24758842443729903,\n\ \ \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.22530864197530864,\n \"acc_stderr\": 0.023246202647819746,\n\ \ \"acc_norm\": 0.22530864197530864,\n \"acc_norm_stderr\": 0.023246202647819746\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880592,\n \ \ \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880592\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24771838331160365,\n\ \ \"acc_stderr\": 0.011025499291443737,\n \"acc_norm\": 0.24771838331160365,\n\ \ \"acc_norm_stderr\": 0.011025499291443737\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.030161911930767102,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.030161911930767102\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2647058823529412,\n \"acc_stderr\": 0.017848089574913222,\n \ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.017848089574913222\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\ \ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\ \ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.031362502409358936,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.031362502409358936\n \ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n\ \ \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.22885572139303484,\n\ \ \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.1927710843373494,\n\ \ \"acc_stderr\": 0.030709824050565274,\n \"acc_norm\": 0.1927710843373494,\n\ \ \"acc_norm_stderr\": 0.030709824050565274\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22766217870257038,\n\ \ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.4073203809998297,\n\ \ \"mc2_stderr\": 0.014931113118872399\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.014051745961790513\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \ \ \"acc_stderr\": 0.0022675371022544736\n }\n}\n```" repo_url: https://huggingface.co/gpt2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|arc:challenge|25_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|arc:challenge|25_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-16T13-32-55.332102.parquet' - config_name: harness_drop_0 data_files: - split: 2023_09_14T13_54_21.687636 path: - '**/details_harness|drop|0_2023-09-14T13-54-21.687636.parquet' - split: 2023_09_15T12_28_23.937147 path: - '**/details_harness|drop|0_2023-09-15T12-28-23.937147.parquet' - split: 2023_09_15T12_47_31.231445 path: - '**/details_harness|drop|0_2023-09-15T12-47-31.231445.parquet' - split: latest path: - '**/details_harness|drop|0_2023-09-15T12-47-31.231445.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|drop|3_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '**/details_harness|drop|3_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '**/details_harness|drop|3_2023-11-29T12-58-42.860611.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-29T12-58-42.860611.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|gsm8k|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '**/details_harness|gsm8k|5_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '**/details_harness|gsm8k|5_2023-11-29T12-58-42.860611.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|gsm8k|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hellaswag|10_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hellaswag|10_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-21T18-07-07.067275.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-management|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-virology|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T13-32-55.332102.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|truthfulqa:mc|0_2023-11-21T18-07-07.067275.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T13-32-55.332102.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_06T15_19_52.414673 path: - '**/details_harness|winogrande|5_2023-09-06T15-19-52.414673.parquet' - split: 2023_09_06T15_22_24.734466 path: - '**/details_harness|winogrande|5_2023-09-06T15-22-24.734466.parquet' - split: 2023_09_06T15_24_04.768979 path: - '**/details_harness|winogrande|5_2023-09-06T15-24-04.768979.parquet' - split: 2023_09_07T12_01_51.839651 path: - '**/details_harness|winogrande|5_2023-09-07T12-01-51.839651.parquet' - split: 2023_09_07T12_04_01.189528 path: - '**/details_harness|winogrande|5_2023-09-07T12-04-01.189528.parquet' - split: 2023_09_07T12_08_17.821371 path: - '**/details_harness|winogrande|5_2023-09-07T12-08-17.821371.parquet' - split: 2023_09_07T12_10_30.286469 path: - '**/details_harness|winogrande|5_2023-09-07T12-10-30.286469.parquet' - split: 2023_11_21T18_07_07.067275 path: - '**/details_harness|winogrande|5_2023-11-21T18-07-07.067275.parquet' - split: 2023_11_29T12_47_35.686694 path: - '**/details_harness|winogrande|5_2023-11-29T12-47-35.686694.parquet' - split: 2023_11_29T12_58_42.860611 path: - '**/details_harness|winogrande|5_2023-11-29T12-58-42.860611.parquet' - split: 2023_12_16T13_32_55.332102 path: - '**/details_harness|winogrande|5_2023-12-16T13-32-55.332102.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-16T13-32-55.332102.parquet' - config_name: results data_files: - split: 2023_09_06T12_19_07.283399 path: - results_2023-09-06T12-19-07.283399.parquet - split: 2023_09_06T12_21_24.071294 path: - results_2023-09-06T12-21-24.071294.parquet - split: 2023_09_06T12_24_13.323279 path: - results_2023-09-06T12-24-13.323279.parquet - split: 2023_09_06T13_26_17.619860 path: - results_2023-09-06T13-26-17.619860.parquet - split: 2023_09_06T15_15_44.379880 path: - results_2023-09-06T15-15-44.379880.parquet - split: 2023_09_06T15_19_52.414673 path: - results_2023-09-06T15-19-52.414673.parquet - split: 2023_09_06T15_22_24.734466 path: - results_2023-09-06T15-22-24.734466.parquet - split: 2023_09_06T15_24_04.768979 path: - results_2023-09-06T15-24-04.768979.parquet - split: 2023_09_07T12_01_51.839651 path: - results_2023-09-07T12-01-51.839651.parquet - split: 2023_09_07T12_04_01.189528 path: - results_2023-09-07T12-04-01.189528.parquet - split: 2023_09_07T12_08_17.821371 path: - results_2023-09-07T12-08-17.821371.parquet - split: 2023_09_07T12_10_30.286469 path: - results_2023-09-07T12-10-30.286469.parquet - split: 2023_09_14T13_54_21.687636 path: - results_2023-09-14T13-54-21.687636.parquet - split: 2023_09_15T12_28_23.937147 path: - results_2023-09-15T12-28-23.937147.parquet - split: 2023_09_15T12_47_31.231445 path: - results_2023-09-15T12-47-31.231445.parquet - split: 2023_11_21T18_07_07.067275 path: - results_2023-11-21T18-07-07.067275.parquet - split: 2023_11_29T12_47_35.686694 path: - results_2023-11-29T12-47-35.686694.parquet - split: 2023_11_29T12_58_42.860611 path: - results_2023-11-29T12-58-42.860611.parquet - split: 2023_12_16T13_32_55.332102 path: - results_2023-12-16T13-32-55.332102.parquet - split: latest path: - results_2023-12-16T13-32-55.332102.parquet --- # Dataset Card for Evaluation run of gpt2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [gpt2](https://huggingface.co/gpt2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 65 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 19 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_gpt2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T13:32:55.332102](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2/blob/main/results_2023-12-16T13-32-55.332102.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": 0.2578870030806924, "acc_stderr": 0.030639295152135662, "acc_norm": 0.2586921012373598, "acc_norm_stderr": 0.031410248419889694, "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.4073203809998297, "mc2_stderr": 0.014931113118872399 }, "harness|arc:challenge|25": { "acc": 0.19880546075085323, "acc_stderr": 0.011662850198175539, "acc_norm": 0.21928327645051193, "acc_norm_stderr": 0.012091245787615723 }, "harness|hellaswag|10": { "acc": 0.29267078271260705, "acc_stderr": 0.004540586983229993, "acc_norm": 0.3158733320055766, "acc_norm_stderr": 0.004639126951051421 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.23703703703703705, "acc_stderr": 0.03673731683969506, "acc_norm": 0.23703703703703705, "acc_norm_stderr": 0.03673731683969506 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.16447368421052633, "acc_stderr": 0.0301675334686327, "acc_norm": 0.16447368421052633, "acc_norm_stderr": 0.0301675334686327 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.16, "acc_stderr": 0.03684529491774711, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774711 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.23773584905660378, "acc_stderr": 0.02619980880756194, "acc_norm": 0.23773584905660378, "acc_norm_stderr": 0.02619980880756194 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03476590104304134, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.19, "acc_stderr": 0.039427724440366234, "acc_norm": 0.19, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "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": 0.23699421965317918, "acc_stderr": 0.03242414757483098, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.03242414757483098 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.16, "acc_stderr": 0.03684529491774709, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2765957446808511, "acc_stderr": 0.029241883869628834, "acc_norm": 0.2765957446808511, "acc_norm_stderr": 0.029241883869628834 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2807017543859649, "acc_stderr": 0.042270544512322, "acc_norm": 0.2807017543859649, "acc_norm_stderr": 0.042270544512322 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.14285714285714285, "acc_stderr": 0.0312984318574381, "acc_norm": 0.14285714285714285, "acc_norm_stderr": 0.0312984318574381 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.15, "acc_stderr": 0.035887028128263686, "acc_norm": 0.15, "acc_norm_stderr": 0.035887028128263686 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3, "acc_stderr": 0.026069362295335137, "acc_norm": 0.3, "acc_norm_stderr": 0.026069362295335137 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.26108374384236455, "acc_stderr": 0.030903796952114475, "acc_norm": 0.26108374384236455, "acc_norm_stderr": 0.030903796952114475 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.35353535353535354, "acc_stderr": 0.03406086723547153, "acc_norm": 0.35353535353535354, "acc_norm_stderr": 0.03406086723547153 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.36787564766839376, "acc_stderr": 0.03480175668466036, "acc_norm": 0.36787564766839376, "acc_norm_stderr": 0.03480175668466036 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2794871794871795, "acc_stderr": 0.022752388839776826, "acc_norm": 0.2794871794871795, "acc_norm_stderr": 0.022752388839776826 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26666666666666666, "acc_stderr": 0.026962424325073838, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.026962424325073838 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.28991596638655465, "acc_stderr": 0.029472485833136098, "acc_norm": 0.28991596638655465, "acc_norm_stderr": 0.029472485833136098 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763744, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763744 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.3486238532110092, "acc_stderr": 0.020431254090714328, "acc_norm": 0.3486238532110092, "acc_norm_stderr": 0.020431254090714328 }, "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": 0.2549019607843137, "acc_stderr": 0.030587591351604243, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604243 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2489451476793249, "acc_stderr": 0.028146970599422644, "acc_norm": 0.2489451476793249, "acc_norm_stderr": 0.028146970599422644 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.3004484304932735, "acc_stderr": 0.030769352008229143, "acc_norm": 0.3004484304932735, "acc_norm_stderr": 0.030769352008229143 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.26717557251908397, "acc_stderr": 0.038808483010823944, "acc_norm": 0.26717557251908397, "acc_norm_stderr": 0.038808483010823944 }, "harness|hendrycksTest-international_law|5": { "acc": 0.32231404958677684, "acc_stderr": 0.04266416363352168, "acc_norm": 0.32231404958677684, "acc_norm_stderr": 0.04266416363352168 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.21296296296296297, "acc_stderr": 0.03957835471980981, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.03957835471980981 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.25766871165644173, "acc_stderr": 0.03436150827846917, "acc_norm": 0.25766871165644173, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.24107142857142858, "acc_stderr": 0.04059867246952688, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.04059867246952688 }, "harness|hendrycksTest-management|5": { "acc": 0.34951456310679613, "acc_stderr": 0.04721188506097173, "acc_norm": 0.34951456310679613, "acc_norm_stderr": 0.04721188506097173 }, "harness|hendrycksTest-marketing|5": { "acc": 0.1752136752136752, "acc_stderr": 0.02490443909891822, "acc_norm": 0.1752136752136752, "acc_norm_stderr": 0.02490443909891822 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.21839080459770116, "acc_stderr": 0.01477435831993449, "acc_norm": 0.21839080459770116, "acc_norm_stderr": 0.01477435831993449 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.23699421965317918, "acc_stderr": 0.02289408248992599, "acc_norm": 0.23699421965317918, "acc_norm_stderr": 0.02289408248992599 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2222222222222222, "acc_stderr": 0.023805186524888146, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.023805186524888146 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.24758842443729903, "acc_stderr": 0.024513879973621967, "acc_norm": 0.24758842443729903, "acc_norm_stderr": 0.024513879973621967 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.22530864197530864, "acc_stderr": 0.023246202647819746, "acc_norm": 0.22530864197530864, "acc_norm_stderr": 0.023246202647819746 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.26595744680851063, "acc_stderr": 0.026358065698880592, "acc_norm": 0.26595744680851063, "acc_norm_stderr": 0.026358065698880592 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.24771838331160365, "acc_stderr": 0.011025499291443737, "acc_norm": 0.24771838331160365, "acc_norm_stderr": 0.011025499291443737 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4411764705882353, "acc_stderr": 0.030161911930767102, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.030161911930767102 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2647058823529412, "acc_stderr": 0.017848089574913222, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.017848089574913222 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.4, "acc_stderr": 0.031362502409358936, "acc_norm": 0.4, "acc_norm_stderr": 0.031362502409358936 }, "harness|hendrycksTest-sociology|5": { "acc": 0.22885572139303484, "acc_stderr": 0.029705284056772426, "acc_norm": 0.22885572139303484, "acc_norm_stderr": 0.029705284056772426 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-virology|5": { "acc": 0.1927710843373494, "acc_stderr": 0.030709824050565274, "acc_norm": 0.1927710843373494, "acc_norm_stderr": 0.030709824050565274 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.21052631578947367, "acc_stderr": 0.0312678171466318, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.0312678171466318 }, "harness|truthfulqa:mc|0": { "mc1": 0.22766217870257038, "mc1_stderr": 0.01467925503211107, "mc2": 0.4073203809998297, "mc2_stderr": 0.014931113118872399 }, "harness|winogrande|5": { "acc": 0.505130228887135, "acc_stderr": 0.014051745961790513 }, "harness|gsm8k|5": { "acc": 0.006823351023502654, "acc_stderr": 0.0022675371022544736 } } ``` ## 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 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It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. 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]
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hezarai/persian-license-plate-v1
hezarai
2023-12-15T13:08:45Z
331
0
[ "task_categories:image-to-text", "language:fa", "region:us" ]
[ "image-to-text" ]
2023-10-14T12:56:01Z
--- task_categories: - image-to-text language: - fa pretty_name: PersianLicensePlate --- > Dataset is downloaded from [here](https://ceit.aut.ac.ir/~keyvanrad/download/ML971/project/) which was provided at Amirkabir University of Technology. > The datas then labeled by the authors. > Experimental results show that the fine-tuned model works well in Persian License Plate.
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nuprl/CanItEdit
nuprl
2023-12-14T20:57:48Z
326
0
[ "task_categories:text2text-generation", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:en", "license:mit", "code-generation", "region:us" ]
[ "text2text-generation" ]
2023-12-11T14:13:35Z
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual pretty_name: CanItEdit size_categories: - n<1K source_datasets: - original task_categories: - text2text-generation task_ids: [] tags: - code-generation paperswithcode_id: canitedit dataset_info: features: - name: id dtype: int64 - name: name dtype: string - name: full_name dtype: string - name: before dtype: string - name: after dtype: string - name: tests dtype: string - name: instruction_descriptive dtype: string - name: instruction_humane dtype: string - name: taxonomy struct: - name: change_kind dtype: string - name: libraries sequence: string - name: topic dtype: string splits: - name: test num_bytes: 301982 num_examples: 54 download_size: 136181 dataset_size: 301982 configs: - config_name: default data_files: - split: test path: data/test-* --- # Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions CanItEdit is a benchmark for evaluating LLMs on instructional code editing, the task of updating a program given a natural language instruction. The benchmark contains 54 hand-crafted Python programs with before and after code blocks, two types of natural language instructions (descriptive and lazy), and a hidden test suite. The dataset’s dual natural language instructions test model efficiency in two scenarios: 1) Descriptive: Detailed instructions replicate situations where users provide specific specifications or another model outlines a plan, similar to Reflexion prompting, 2) Lazy: Informal instructions resemble typical user queries for LLMs in code generation. For more information and results see [our paper](https://federico.codes/assets/papers/canitedit.pdf). ## How To Evaluate All the code for evaluating the benchmark can be found in our [GitHub repository](https://github.com/nuprl/CanItEdit).
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autumnjohnson/ceti_audio
autumnjohnson
2023-12-12T06:06:50Z
323
0
[ "size_categories:1K<n<10K", "region:us" ]
null
2023-11-06T10:15:17Z
--- size_categories: - 1K<n<10K pretty_name: Project CETI (Cetacean Translation Initiative) audio dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 decode: false - name: coda_type dtype: string - name: path dtype: string - name: sampling_rate dtype: int64 splits: - name: train num_bytes: 229777268.74202734 num_examples: 2458 - name: test num_bytes: 98529390.25797267 num_examples: 1054 download_size: 162197285 dataset_size: 328306659.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "ceti_audio" ## Table of Contents - [Dataset Card for "ceti\_audio"](#dataset-card-for-ceti_audio) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@autumnjohnson](https://github.com/<github-username>) for adding this dataset.
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open-llm-leaderboard/details_gpt2-xl
open-llm-leaderboard
2023-12-16T14:31:36Z
315
0
[ "region:us" ]
null
2023-09-02T12:01:52Z
--- pretty_name: Evaluation run of gpt2-xl dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [gpt2-xl](https://huggingface.co/gpt2-xl) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_gpt2-xl\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T14:28:59.235900](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2-xl/blob/main/results_2023-12-16T14-28-59.235900.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\": 0.26849437510168506,\n\ \ \"acc_stderr\": 0.031366498421091876,\n \"acc_norm\": 0.2702003428835176,\n\ \ \"acc_norm_stderr\": 0.03215444575128815,\n \"mc1\": 0.22031823745410037,\n\ \ \"mc1_stderr\": 0.0145090451714873,\n \"mc2\": 0.38536763571053717,\n\ \ \"mc2_stderr\": 0.014057464412774041\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.2593856655290102,\n \"acc_stderr\": 0.012808273573927104,\n\ \ \"acc_norm\": 0.302901023890785,\n \"acc_norm_stderr\": 0.013428241573185347\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3981278629755029,\n\ \ \"acc_stderr\": 0.004885116465550274,\n \"acc_norm\": 0.5136427006572396,\n\ \ \"acc_norm_stderr\": 0.004987923636628551\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2074074074074074,\n\ \ \"acc_stderr\": 0.03502553170678318,\n \"acc_norm\": 0.2074074074074074,\n\ \ \"acc_norm_stderr\": 0.03502553170678318\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.18421052631578946,\n \"acc_stderr\": 0.0315469804508223,\n\ \ \"acc_norm\": 0.18421052631578946,\n \"acc_norm_stderr\": 0.0315469804508223\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.29056603773584905,\n \"acc_stderr\": 0.027943219989337145,\n\ \ \"acc_norm\": 0.29056603773584905,\n \"acc_norm_stderr\": 0.027943219989337145\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2847222222222222,\n\ \ \"acc_stderr\": 0.03773809990686935,\n \"acc_norm\": 0.2847222222222222,\n\ \ \"acc_norm_stderr\": 0.03773809990686935\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n\ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-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\": 0.30057803468208094,\n\ \ \"acc_stderr\": 0.03496101481191181,\n \"acc_norm\": 0.30057803468208094,\n\ \ \"acc_norm_stderr\": 0.03496101481191181\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179962,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179962\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n\ \ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.2978723404255319,\n \"acc_stderr\": 0.029896145682095455,\n\ \ \"acc_norm\": 0.2978723404255319,\n \"acc_norm_stderr\": 0.029896145682095455\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n\ \ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.23809523809523808,\n \"acc_stderr\": 0.021935878081184756,\n \"\ acc_norm\": 0.23809523809523808,\n \"acc_norm_stderr\": 0.021935878081184756\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n\ \ \"acc_stderr\": 0.04240799327574925,\n \"acc_norm\": 0.3412698412698413,\n\ \ \"acc_norm_stderr\": 0.04240799327574925\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24838709677419354,\n\ \ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.24838709677419354,\n\ \ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.21674876847290642,\n \"acc_stderr\": 0.028990331252516235,\n\ \ \"acc_norm\": 0.21674876847290642,\n \"acc_norm_stderr\": 0.028990331252516235\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.3090909090909091,\n \"acc_stderr\": 0.036085410115739666,\n\ \ \"acc_norm\": 0.3090909090909091,\n \"acc_norm_stderr\": 0.036085410115739666\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2777777777777778,\n \"acc_stderr\": 0.03191178226713548,\n \"\ acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.03191178226713548\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.21243523316062177,\n \"acc_stderr\": 0.029519282616817258,\n\ \ \"acc_norm\": 0.21243523316062177,\n \"acc_norm_stderr\": 0.029519282616817258\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.34102564102564104,\n \"acc_stderr\": 0.02403548967633507,\n\ \ \"acc_norm\": 0.34102564102564104,\n \"acc_norm_stderr\": 0.02403548967633507\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2518518518518518,\n \"acc_stderr\": 0.02646611753895991,\n \ \ \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.02646611753895991\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.027381406927868956,\n\ \ \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.027381406927868956\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.26490066225165565,\n \"acc_stderr\": 0.03603038545360384,\n \"\ acc_norm\": 0.26490066225165565,\n \"acc_norm_stderr\": 0.03603038545360384\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.3321100917431193,\n \"acc_stderr\": 0.020192682985423347,\n \"\ acc_norm\": 0.3321100917431193,\n \"acc_norm_stderr\": 0.020192682985423347\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.39814814814814814,\n \"acc_stderr\": 0.033384734032074016,\n \"\ acc_norm\": 0.39814814814814814,\n \"acc_norm_stderr\": 0.033384734032074016\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.22549019607843138,\n \"acc_stderr\": 0.029331162294251714,\n \"\ acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.029331162294251714\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.2489451476793249,\n \"acc_stderr\": 0.028146970599422644,\n \ \ \"acc_norm\": 0.2489451476793249,\n \"acc_norm_stderr\": 0.028146970599422644\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.19282511210762332,\n\ \ \"acc_stderr\": 0.026478240960489365,\n \"acc_norm\": 0.19282511210762332,\n\ \ \"acc_norm_stderr\": 0.026478240960489365\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.26717557251908397,\n \"acc_stderr\": 0.03880848301082396,\n\ \ \"acc_norm\": 0.26717557251908397,\n \"acc_norm_stderr\": 0.03880848301082396\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2727272727272727,\n \"acc_stderr\": 0.04065578140908705,\n \"\ acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.04065578140908705\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2962962962962963,\n\ \ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n\ \ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.2392638036809816,\n \"acc_stderr\": 0.03351953879521269,\n\ \ \"acc_norm\": 0.2392638036809816,\n \"acc_norm_stderr\": 0.03351953879521269\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n\ \ \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n\ \ \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.2621359223300971,\n \"acc_stderr\": 0.04354631077260597,\n\ \ \"acc_norm\": 0.2621359223300971,\n \"acc_norm_stderr\": 0.04354631077260597\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.27350427350427353,\n\ \ \"acc_stderr\": 0.029202540153431177,\n \"acc_norm\": 0.27350427350427353,\n\ \ \"acc_norm_stderr\": 0.029202540153431177\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2681992337164751,\n\ \ \"acc_stderr\": 0.015842430835269445,\n \"acc_norm\": 0.2681992337164751,\n\ \ \"acc_norm_stderr\": 0.015842430835269445\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.2398843930635838,\n \"acc_stderr\": 0.02298959254312357,\n\ \ \"acc_norm\": 0.2398843930635838,\n \"acc_norm_stderr\": 0.02298959254312357\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\ \ \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n\ \ \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.28104575163398693,\n \"acc_stderr\": 0.02573885479781873,\n\ \ \"acc_norm\": 0.28104575163398693,\n \"acc_norm_stderr\": 0.02573885479781873\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.21221864951768488,\n\ \ \"acc_stderr\": 0.023222756797435122,\n \"acc_norm\": 0.21221864951768488,\n\ \ \"acc_norm_stderr\": 0.023222756797435122\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.24691358024691357,\n \"acc_stderr\": 0.02399350170904211,\n\ \ \"acc_norm\": 0.24691358024691357,\n \"acc_norm_stderr\": 0.02399350170904211\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432407,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432407\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.25945241199478486,\n\ \ \"acc_stderr\": 0.011195262076350316,\n \"acc_norm\": 0.25945241199478486,\n\ \ \"acc_norm_stderr\": 0.011195262076350316\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.20955882352941177,\n \"acc_stderr\": 0.02472311040767705,\n\ \ \"acc_norm\": 0.20955882352941177,\n \"acc_norm_stderr\": 0.02472311040767705\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.238562091503268,\n \"acc_stderr\": 0.017242385828779586,\n \ \ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.017242385828779586\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.3181818181818182,\n\ \ \"acc_stderr\": 0.04461272175910508,\n \"acc_norm\": 0.3181818181818182,\n\ \ \"acc_norm_stderr\": 0.04461272175910508\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.2897959183673469,\n \"acc_stderr\": 0.02904308868330435,\n\ \ \"acc_norm\": 0.2897959183673469,\n \"acc_norm_stderr\": 0.02904308868330435\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2537313432835821,\n\ \ \"acc_stderr\": 0.030769444967296024,\n \"acc_norm\": 0.2537313432835821,\n\ \ \"acc_norm_stderr\": 0.030769444967296024\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3373493975903614,\n\ \ \"acc_stderr\": 0.03680783690727581,\n \"acc_norm\": 0.3373493975903614,\n\ \ \"acc_norm_stderr\": 0.03680783690727581\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.32748538011695905,\n \"acc_stderr\": 0.03599335771456027,\n\ \ \"acc_norm\": 0.32748538011695905,\n \"acc_norm_stderr\": 0.03599335771456027\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22031823745410037,\n\ \ \"mc1_stderr\": 0.0145090451714873,\n \"mc2\": 0.38536763571053717,\n\ \ \"mc2_stderr\": 0.014057464412774041\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5824782951854776,\n \"acc_stderr\": 0.013859978264440248\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01288855193328279,\n \ \ \"acc_stderr\": 0.0031069012664996505\n }\n}\n```" repo_url: https://huggingface.co/gpt2-xl leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|arc:challenge|25_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|arc:challenge|25_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-16T14-28-59.235900.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_06T15_15_36.516909 path: - '**/details_harness|gsm8k|5_2023-12-06T15-15-36.516909.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|gsm8k|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hellaswag|10_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hellaswag|10_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-02T12:01:37.784135.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-28-59.235900.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-management|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T14-28-59.235900.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_02T12_01_37.784135 path: - '**/details_harness|truthfulqa:mc|0_2023-09-02T12:01:37.784135.parquet' - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T14-28-59.235900.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T14_28_59.235900 path: - '**/details_harness|winogrande|5_2023-12-16T14-28-59.235900.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-16T14-28-59.235900.parquet' - config_name: results data_files: - split: 2023_09_02T12_01_37.784135 path: - results_2023-09-02T12:01:37.784135.parquet - split: 2023_12_06T15_15_36.516909 path: - results_2023-12-06T15-15-36.516909.parquet - split: 2023_12_16T14_28_59.235900 path: - results_2023-12-16T14-28-59.235900.parquet - split: latest path: - results_2023-12-16T14-28-59.235900.parquet --- # Dataset Card for Evaluation run of gpt2-xl <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [gpt2-xl](https://huggingface.co/gpt2-xl) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_gpt2-xl", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T14:28:59.235900](https://huggingface.co/datasets/open-llm-leaderboard/details_gpt2-xl/blob/main/results_2023-12-16T14-28-59.235900.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": 0.26849437510168506, "acc_stderr": 0.031366498421091876, "acc_norm": 0.2702003428835176, "acc_norm_stderr": 0.03215444575128815, "mc1": 0.22031823745410037, "mc1_stderr": 0.0145090451714873, "mc2": 0.38536763571053717, "mc2_stderr": 0.014057464412774041 }, "harness|arc:challenge|25": { "acc": 0.2593856655290102, "acc_stderr": 0.012808273573927104, "acc_norm": 0.302901023890785, "acc_norm_stderr": 0.013428241573185347 }, "harness|hellaswag|10": { "acc": 0.3981278629755029, "acc_stderr": 0.004885116465550274, "acc_norm": 0.5136427006572396, "acc_norm_stderr": 0.004987923636628551 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2074074074074074, "acc_stderr": 0.03502553170678318, "acc_norm": 0.2074074074074074, "acc_norm_stderr": 0.03502553170678318 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.18421052631578946, "acc_stderr": 0.0315469804508223, "acc_norm": 0.18421052631578946, "acc_norm_stderr": 0.0315469804508223 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.29056603773584905, "acc_stderr": 0.027943219989337145, "acc_norm": 0.29056603773584905, "acc_norm_stderr": 0.027943219989337145 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2847222222222222, "acc_stderr": 0.03773809990686935, "acc_norm": 0.2847222222222222, "acc_norm_stderr": 0.03773809990686935 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.24, "acc_stderr": 0.04292346959909284, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909284 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.30057803468208094, "acc_stderr": 0.03496101481191181, "acc_norm": 0.30057803468208094, "acc_norm_stderr": 0.03496101481191181 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179962, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179962 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2978723404255319, "acc_stderr": 0.029896145682095455, "acc_norm": 0.2978723404255319, "acc_norm_stderr": 0.029896145682095455 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813344, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813344 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2689655172413793, "acc_stderr": 0.03695183311650232, "acc_norm": 0.2689655172413793, "acc_norm_stderr": 0.03695183311650232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.23809523809523808, "acc_stderr": 0.021935878081184756, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.021935878081184756 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3412698412698413, "acc_stderr": 0.04240799327574925, "acc_norm": 0.3412698412698413, "acc_norm_stderr": 0.04240799327574925 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24838709677419354, "acc_stderr": 0.024580028921481003, "acc_norm": 0.24838709677419354, "acc_norm_stderr": 0.024580028921481003 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.21674876847290642, "acc_stderr": 0.028990331252516235, "acc_norm": 0.21674876847290642, "acc_norm_stderr": 0.028990331252516235 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.3090909090909091, "acc_stderr": 0.036085410115739666, "acc_norm": 0.3090909090909091, "acc_norm_stderr": 0.036085410115739666 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2777777777777778, "acc_stderr": 0.03191178226713548, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.03191178226713548 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.21243523316062177, "acc_stderr": 0.029519282616817258, "acc_norm": 0.21243523316062177, "acc_norm_stderr": 0.029519282616817258 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.34102564102564104, "acc_stderr": 0.02403548967633507, "acc_norm": 0.34102564102564104, "acc_norm_stderr": 0.02403548967633507 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2518518518518518, "acc_stderr": 0.02646611753895991, "acc_norm": 0.2518518518518518, "acc_norm_stderr": 0.02646611753895991 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.23109243697478993, "acc_stderr": 0.027381406927868956, "acc_norm": 0.23109243697478993, "acc_norm_stderr": 0.027381406927868956 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.26490066225165565, "acc_stderr": 0.03603038545360384, "acc_norm": 0.26490066225165565, "acc_norm_stderr": 0.03603038545360384 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.3321100917431193, "acc_stderr": 0.020192682985423347, "acc_norm": 0.3321100917431193, "acc_norm_stderr": 0.020192682985423347 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.39814814814814814, "acc_stderr": 0.033384734032074016, "acc_norm": 0.39814814814814814, "acc_norm_stderr": 0.033384734032074016 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.22549019607843138, "acc_stderr": 0.029331162294251714, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.029331162294251714 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2489451476793249, "acc_stderr": 0.028146970599422644, "acc_norm": 0.2489451476793249, "acc_norm_stderr": 0.028146970599422644 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.19282511210762332, "acc_stderr": 0.026478240960489365, "acc_norm": 0.19282511210762332, "acc_norm_stderr": 0.026478240960489365 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.26717557251908397, "acc_stderr": 0.03880848301082396, "acc_norm": 0.26717557251908397, "acc_norm_stderr": 0.03880848301082396 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2727272727272727, "acc_stderr": 0.04065578140908705, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.04065578140908705 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.2962962962962963, "acc_stderr": 0.044143436668549335, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.044143436668549335 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.2392638036809816, "acc_stderr": 0.03351953879521269, "acc_norm": 0.2392638036809816, "acc_norm_stderr": 0.03351953879521269 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.26785714285714285, "acc_stderr": 0.04203277291467762, "acc_norm": 0.26785714285714285, "acc_norm_stderr": 0.04203277291467762 }, "harness|hendrycksTest-management|5": { "acc": 0.2621359223300971, "acc_stderr": 0.04354631077260597, "acc_norm": 0.2621359223300971, "acc_norm_stderr": 0.04354631077260597 }, "harness|hendrycksTest-marketing|5": { "acc": 0.27350427350427353, "acc_stderr": 0.029202540153431177, "acc_norm": 0.27350427350427353, "acc_norm_stderr": 0.029202540153431177 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2681992337164751, "acc_stderr": 0.015842430835269445, "acc_norm": 0.2681992337164751, "acc_norm_stderr": 0.015842430835269445 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2398843930635838, "acc_stderr": 0.02298959254312357, "acc_norm": 0.2398843930635838, "acc_norm_stderr": 0.02298959254312357 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.28104575163398693, "acc_stderr": 0.02573885479781873, "acc_norm": 0.28104575163398693, "acc_norm_stderr": 0.02573885479781873 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.21221864951768488, "acc_stderr": 0.023222756797435122, "acc_norm": 0.21221864951768488, "acc_norm_stderr": 0.023222756797435122 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.24691358024691357, "acc_stderr": 0.02399350170904211, "acc_norm": 0.24691358024691357, "acc_norm_stderr": 0.02399350170904211 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432407, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432407 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.25945241199478486, "acc_stderr": 0.011195262076350316, "acc_norm": 0.25945241199478486, "acc_norm_stderr": 0.011195262076350316 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.20955882352941177, "acc_stderr": 0.02472311040767705, "acc_norm": 0.20955882352941177, "acc_norm_stderr": 0.02472311040767705 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.238562091503268, "acc_stderr": 0.017242385828779586, "acc_norm": 0.238562091503268, "acc_norm_stderr": 0.017242385828779586 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.3181818181818182, "acc_stderr": 0.04461272175910508, "acc_norm": 0.3181818181818182, "acc_norm_stderr": 0.04461272175910508 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.2897959183673469, "acc_stderr": 0.02904308868330435, "acc_norm": 0.2897959183673469, "acc_norm_stderr": 0.02904308868330435 }, "harness|hendrycksTest-sociology|5": { "acc": 0.2537313432835821, "acc_stderr": 0.030769444967296024, "acc_norm": 0.2537313432835821, "acc_norm_stderr": 0.030769444967296024 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-virology|5": { "acc": 0.3373493975903614, "acc_stderr": 0.03680783690727581, "acc_norm": 0.3373493975903614, "acc_norm_stderr": 0.03680783690727581 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.32748538011695905, "acc_stderr": 0.03599335771456027, "acc_norm": 0.32748538011695905, "acc_norm_stderr": 0.03599335771456027 }, "harness|truthfulqa:mc|0": { "mc1": 0.22031823745410037, "mc1_stderr": 0.0145090451714873, "mc2": 0.38536763571053717, "mc2_stderr": 0.014057464412774041 }, "harness|winogrande|5": { "acc": 0.5824782951854776, "acc_stderr": 0.013859978264440248 }, "harness|gsm8k|5": { "acc": 0.01288855193328279, "acc_stderr": 0.0031069012664996505 } } ``` ## 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.). 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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]
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aeromaki/WOS46985
aeromaki
2023-12-14T00:38:13Z
310
0
[ "region:us" ]
null
2023-12-14T00:37:55Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: label sequence: int64 - name: text dtype: string splits: - name: train num_bytes: 59938702.67012876 num_examples: 42286 - name: test num_bytes: 3331030.394806853 num_examples: 2350 - name: validation num_bytes: 3329612.9350643824 num_examples: 2349 download_size: 37716140 dataset_size: 66599346.0 --- # Dataset Card for "WOS46985" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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martinsinnona/visdecode
martinsinnona
2023-12-11T16:31:24Z
238
0
[ "region:us" ]
null
2023-06-30T14:39:33Z
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 964808.0 num_examples: 100 - name: test num_bytes: 5194.0 num_examples: 1 download_size: 851229 dataset_size: 970002.0 --- # Dataset Card for "ploty" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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trl-lib/ultrachat_200k_chatml
trl-lib
2023-12-12T11:10:50Z
220
0
[ "region:us" ]
null
2023-12-12T11:10:22Z
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1224177384 num_examples: 207865 - name: test num_bytes: 135316994 num_examples: 23110 download_size: 676202243 dataset_size: 1359494378 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
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pinkieseb/auslegalcorpus
pinkieseb
2023-12-11T04:01:51Z
204
0
[ "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "legal", "region:us" ]
null
2023-12-08T22:24:58Z
--- license: apache-2.0 language: - en tags: - legal size_categories: - 1M<n<10M ---
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humane-lab/K-HATERS
humane-lab
2023-12-14T09:25:04Z
182
0
[ "language:ko", "license:cc-by-4.0", "hate speech detection", "arxiv:2310.15439", "region:us" ]
null
2023-10-16T04:29:58Z
--- license: cc-by-4.0 language: - ko pretty_name: K-Haters tags: - hate speech detection --- <!-- # ℹ️ Dataset card for K-HATERS ### Dataset summary We introduces **K-HATERS**, a new corpus for hate speech detection in Korean, comprising approximately 192K news comments with target-specific offensiveness ratings. The corpus consists of 192,158 news comments consisting of 184,117 news comments collected by ourselves and 8,041 comments collected from a [previous study](https://aclanthology.org/2020.socialnlp-1.4/). We collected news comments published through the politics, society and world news sections in Naver News over two months in 2021. All comments were annotated through CashMission, a crowdsourcing service run by SELECTSTAR. </br>For more information, please refer to the paper [K-HATERS](https://arxiv.org/abs/2310.15439) published at EMNLP 2023 Findings. ### Supported tasks - Hate speech detection - Multi class classification (labels: normal, offensive, L1_hate, L2_hate) - Binary classifiction (labels: normal, toxic(offensive, L1_hate, L2_hate)) - Rationale prediction (offensiveness, target rationale) ### Data describtion ``` data['train'][42] {'text': '군대도 안간 놈 이 주둥아리 는 씽씽하네..보수 놈 들..군대는 안가고 애국이냐..#@이름#,#@이름#,', 'label': 'L1_hate', 'target_label': ['political'], 'offensiveness_rationale': [[7, 8], [11, 15], [27, 28]], 'target_rationale': [[24, 26], [46, 51], [52, 57]]} ``` - Abusive language categories (**label**) - L2_hate: Comments with explicit forms of hate expressions toward one of the groups of protected attributes (e.g., gender, age, race, ...) - L1_hate: Comments with more implicit forms of hate expressions - Offensive: Comments that express offensiveness but not toward a protected attribute group - Normal: The rest comments - Multi-label target categories (**target_label**): list of offensiveness targets. A comment can have zero or multiple targets. - List of target categories: gender, age, race, religion, politics, job, disability, individuals, and others. - Annotators' rationales for the strength of ratings (**offensiveness_rationale**): lists providing annotators' rationales for the strength of ratings. The list includes the start and end indices of highlight spans. - Annotators' rationales for the target of offensiveness (**target_rationale**) ### Dataset split We provide the dataset in the form of splits as 172,158 (for train), 10,000 (for validation), and 10,000 (for test). Label ratio was preseved (stratified split). ### Labeling guidelines Labeling guidelines are available as a part of SELECTSTAR open datasets (in Korean). [link](https://open.selectstar.ai/ko/?page_id=5948) </br> # 📜 Data statement We present the data statement for responsible usage [(Bender and Friedman, 2018)](https://aclanthology.org/Q18-1041/). ### Curation Rationale We collected the raw data from the news aggregator of Naver, the largest news portal in Korea. We targeted news articles published in the society, world news, and politics sections because discussions are active in the hard news. ### Language Variety Our dataset consists of the news comments in Korean (ko-KR). ### Speaker Demographic The user demographic is not available. However, considering that the portal site has the largest share of Korean, it can be assumed that speakers are mostly Korean. ### Annotator Demographic A total of 405 workers participated in an annotation. 21 workers are 10s, 222 workers are 20s, 116 workers are 30s, 35 workers are 40s, 9 workers are 50s, and 2 workers are 60s. ### Speech Situation News article in the hard news section deals with controversial events, so there are more likely to exist hate comments or toxicity comments. The target articles were published between July 2021 and August 2021. During that period, the most controversial events were the South Korean presidential election, the Tokyo Olympics, COVID-19, and the Restoration of Taliban Control, etc. ### Text Characteristics It includes hatred words limited to Korea, such as hatred of certain political orientations and certain groups. For example, '대깨문' (a word that hates former Korean president Moon's supporter), and '꼴페미' (a word that hates feminists) </br> # 🤝 License & Contributors ### Licensing information This dataset is shared under CC-BY 4.0. </br>According to this license, you are free to use the dataset as long as you provide appropriate attribution (e.g., citing our paper). ### Citation information ``` @article{park2023haters, title={K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratings}, author={Park, Chaewon and Kim, Suhwan and Park, Kyubyong and Park, Kunwoo}, journal={Findings of the EMNLP 2023}, year={2023} } ``` ### Contributions - Chaewon Park - Suhwan Kim (TUNiB) - Kyubyong Park (TUNiB) - Kunwoo Park #-->
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unalignment/toxic-dpo-v0.1
unalignment
2023-12-13T12:08:23Z
178
44
[ "license:apache-2.0", "not-for-all-audiences", "region:us" ]
null
2023-12-11T15:51:16Z
--- license: apache-2.0 tags: - not-for-all-audiences --- ## Toxic-DPO This is a highly toxic, "harmful" dataset meant to illustrate how DPO can be used to de-censor/unalign a model quite easily using direct-preference-optimization (DPO) using very few examples. Most of the examples still contain some amount of warnings/disclaimers, so it's still somewhat editorialized. ## Usage restriction To use this data, you must acknowledge/agree to the following: - data contained within is "toxic"/"harmful", and contains profanity and other types of sensitive content - none of the content or views contained in the dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs automatically (llama-2-70b via prompt engineering for chosen and llama-2-13b-chat-hf for rejected) - you are able to use the dataset lawfully, particularly in locations with less-than-free speech laws - you, and you alone are responsible for having downloaded and used the dataset, and I am completely indemnified from any and all liabilities This dataset is meant __*exclusively*__ for academic/research or other non-nefarious use-cases.
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argilla/end2end_textclassification_with_suggestions_and_responses
argilla
2023-12-15T16:54:52Z
174
0
[ "size_categories:1K<n<10K", "rlfh", "argilla", "human-feedback", "region:us" ]
null
2023-11-29T15:15:26Z
--- size_categories: 1K<n<10K tags: - rlfh - argilla - human-feedback --- # Dataset Card for end2end_textclassification_with_suggestions_and_responses This dataset has been created with [Argilla](https://docs.argilla.io). As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets). ## Dataset Description - **Homepage:** https://argilla.io - **Repository:** https://github.com/argilla-io/argilla - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset contains: * A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla. * Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`. * The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla. ### Load with Argilla To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code: ```python import argilla as rg ds = rg.FeedbackDataset.from_huggingface("argilla/end2end_textclassification_with_suggestions_and_responses") ``` ### Load with `datasets` To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset("argilla/end2end_textclassification_with_suggestions_and_responses") ``` ### Supported Tasks and Leaderboards This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure). There are no leaderboards associated with this dataset. ### Languages [More Information Needed] ## Dataset Structure ### Data in Argilla The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**. The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. | Field Name | Title | Type | Required | Markdown | | ---------- | ----- | ---- | -------- | -------- | | text | Text | FieldTypes.text | True | False | The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking. | Question Name | Title | Type | Required | Description | Values/Labels | | ------------- | ----- | ---- | -------- | ----------- | ------------- | | label | Label | QuestionTypes.label_selection | True | N/A | ['World', 'Sports', 'Business', 'Sci/Tech'] | The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata". The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. | Metadata Name | Title | Type | Values | Visible for Annotators | | ------------- | ----- | ---- | ------ | ---------------------- | The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section. ### Data Instances An example of a dataset instance in Argilla looks as follows: ```json { "external_id": "record-0", "fields": { "text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again." }, "metadata": {}, "responses": [ { "status": "submitted", "user_id": null, "values": { "label": { "value": "Business" } } } ], "suggestions": [ { "agent": "cointegrated/rubert-tiny-bilingual-nli", "question_name": "label", "score": null, "type": null, "value": "Sports" } ], "vectors": {} } ``` While the same record in HuggingFace `datasets` looks as follows: ```json { "external_id": "record-0", "label": [ { "status": "submitted", "user_id": null, "value": "Business" } ], "label-suggestion": "Sports", "label-suggestion-metadata": { "agent": "cointegrated/rubert-tiny-bilingual-nli", "score": null, "type": null }, "metadata": "{}", "text": "Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street\u0027s dwindling\\band of ultra-cynics, are seeing green again." } ``` ### Data Fields Among the dataset fields, we differentiate between the following: * **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions. * **text** is of type `FieldTypes.text`. * **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`. * **label** is of type `QuestionTypes.label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech']. * **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable. * (optional) **label-suggestion** is of type `QuestionTypes.label_selection` with the following allowed values ['World', 'Sports', 'Business', 'Sci/Tech']. Additionally, we also have two more fields that are optional and are the following: * **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`. * **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file. ### Data Splits The dataset contains a single split, which is `train`. ## 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 guidelines Classify the articles into one of the four categories. #### 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]
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amazingvince/RedPajama-Data-V2-Sample-snapshot-2023-14
amazingvince
2023-12-16T14:41:05Z
169
1
[ "region:us" ]
null
2023-11-19T22:20:17Z
--- dataset_info: features: - name: raw_content dtype: string - name: doc_id dtype: string - name: meta dtype: string - name: quality_signals dtype: string splits: - name: train num_bytes: 2595581904 num_examples: 232056 download_size: 1178806632 dataset_size: 2595581904 configs: - config_name: default data_files: - split: train path: data/train-* ---
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cis-lmu/Glot500
cis-lmu
2023-12-15T06:41:30Z
166
5
[ "multilinguality:multilingual", "language:abk", "language:abn", "language:ace", "language:ach", "language:acm", "language:acr", "language:ada", "language:afb", "language:afr", "language:ahk", "language:ajg", "language:ajp", "language:aka", "language:als", "language:alt", "language:alz", "language:amh", "language:ami", "language:aoj", "language:apc", "language:ara", "language:arb", "language:arn", "language:ary", "language:arz", "language:asm", "language:ast", "language:aym", "language:azb", "language:aze", "language:azj", "language:bak", "language:bam", "language:ban", "language:bas", "language:bba", "language:bbc", "language:bci", "language:bcl", "language:bel", "language:bem", "language:ben", "language:ber", "language:bhw", "language:bih", "language:bik", "language:bim", "language:bin", "language:bis", "language:bjn", "language:bod", "language:bos", "language:bpy", "language:bqc", "language:bre", "language:bsb", "language:bts", "language:btx", "language:bum", "language:bzj", "language:cab", "language:cak", "language:cat", "language:cbk", "language:cce", "language:cfm", "language:cgg", "language:che", "language:chk", "language:chv", "language:chw", "language:cjk", "language:ckb", "language:cmn", "language:cnh", "language:cos", "language:crh", "language:crs", "language:csb", "language:ctd", "language:ctu", "language:cuk", "language:cym", "language:dan", "language:dhv", "language:diq", "language:div", "language:djk", "language:dln", "language:dtp", "language:dua", "language:dyu", "language:dzo", "language:efi", "language:ekk", "language:ell", "language:eml", "language:eng", "language:epo", "language:est", "language:eus", "language:ewe", "language:ext", "language:fao", "language:fas", "language:fat", "language:fij", "language:fin", "language:fon", "language:fra", "language:frr", "language:fry", "language:ful", "language:fur", "language:gaa", "language:gcf", "language:gcr", "language:gil", "language:gkn", "language:gkp", "language:gla", "language:gle", "language:glg", "language:glv", "language:gom", "language:gor", "language:grc", "language:grn", "language:gsw", "language:guc", "language:gug", "language:guj", "language:gur", "language:guw", "language:gym", "language:hat", "language:hau", "language:haw", "language:hbs", "language:heb", "language:her", "language:hif", "language:hil", "language:hin", "language:hmn", "language:hmo", "language:hne", "language:hrv", "language:hrx", "language:hsb", "language:hui", "language:hun", "language:hus", "language:hye", "language:hyw", "language:iba", "language:ibg", "language:ibo", "language:ido", "language:idu", "language:ifa", "language:ifb", "language:ige", "language:ikk", "language:iku", "language:ile", "language:ilo", "language:ina", "language:ind", "language:ish", "language:isl", "language:ita", "language:ixl", "language:izz", "language:jam", "language:jav", "language:jbo", "language:kaa", "language:kab", "language:kac", "language:kal", "language:kam", "language:kan", "language:kat", "language:kaz", "language:kbd", "language:kbp", "language:kea", "language:kek", "language:khm", "language:kik", "language:kin", "language:kir", "language:kjh", "language:kmb", "language:kmr", "language:kom", "language:kon", "language:koo", "language:kor", "language:kos", "language:kpg", "language:kqn", "language:krc", "language:kri", "language:ksd", "language:ksh", "language:kss", "language:ksw", "language:kua", "language:kur", "language:kwn", "language:lam", "language:lao", "language:lat", "language:lav", "language:ldi", "language:leh", "language:lfn", "language:lhu", "language:lij", "language:lim", "language:lin", "language:lit", "language:llb", "language:lmo", "language:loz", "language:lua", "language:lue", "language:lug", "language:lun", "language:luo", "language:lus", "language:lvs", "language:lzh", "language:mad", "language:mah", "language:mai", "language:mal", "language:mam", "language:mar", "language:mau", "language:mbb", "language:mck", "language:mco", "language:mdy", "language:meu", "language:mfe", "language:mgh", "language:mgr", "language:mhr", "language:min", "language:miq", "language:mkd", "language:mlg", "language:mny", "language:mon", "language:mos", "language:mps", "language:mri", "language:mrw", "language:msa", "language:mwl", "language:mwn", "language:mxv", "language:mya", "language:myv", "language:mzh", "language:mzn", "language:nan", "language:nap", "language:naq", "language:nav", "language:nba", "language:nbl", "language:nch", "language:ncj", "language:ncx", "language:ndc", "language:nde", "language:ndo", "language:nds", "language:nep", "language:new", "language:ngl", "language:ngu", "language:nia", "language:niu", "language:nld", "language:nnb", "language:nno", "language:nob", "language:nor", "language:nse", "language:nso", "language:nya", "language:nyk", "language:nyn", "language:nyu", "language:nyy", "language:nzi", "language:oci", "language:ogo", "language:oke", "language:ori", "language:orm", "language:ory", "language:oss", "language:ote", "language:pag", "language:pam", "language:pan", "language:pap", "language:pau", "language:pcd", "language:pcm", "language:pdt", "language:pes", "language:pfl", "language:phm", "language:pis", "language:pls", "language:plt", "language:pms", "language:pnb", "language:poh", "language:pon", "language:por", "language:prk", "language:pus", "language:pxm", "language:qub", "language:quc", "language:que", "language:qug", "language:quw", "language:quy", "language:quz", "language:qvi", "language:rap", "language:rar", "language:rmn", "language:rmy", "language:rng", "language:roh", "language:ron", "language:rue", "language:rug", "language:run", "language:rus", "language:sag", "language:sah", "language:san", "language:sat", "language:scn", "language:sco", "language:seh", "language:sgs", "language:sid", "language:sin", "language:skg", "language:slk", "language:slv", "language:sme", "language:smo", "language:sna", "language:snd", "language:som", "language:sop", "language:sot", "language:sqi", "language:srd", "language:srm", "language:srn", "language:srp", "language:ssw", "language:sun", "language:suz", "language:swa", "language:swe", "language:swh", "language:sxn", "language:szl", "language:tah", "language:tam", "language:tat", "language:tbz", "language:tca", "language:tcf", "language:tdt", "language:tdx", "language:tel", "language:teo", "language:tgl", "language:tha", "language:tih", "language:tir", "language:tiv", "language:tlh", "language:tob", "language:tog", "language:toh", "language:toi", "language:toj", "language:tok", "language:ton", "language:top", "language:tpi", "language:tpm", "language:tsc", "language:tso", "language:tsz", "language:ttj", "language:tuc", "language:tuk", "language:tur", "language:twi", "language:twx", "language:tyv", "language:tzh", "language:tzo", "language:udm", "language:uig", "language:ukr", "language:umb", "language:urd", "language:urh", "language:uzb", "language:uzn", "language:ven", "language:vep", "language:vie", "language:vls", "language:vmw", "language:vol", "language:wal", "language:wes", "language:wln", "language:wol", "language:wuu", "language:xav", "language:xho", "language:xmf", "language:xmv", "language:yao", "language:yap", "language:yid", "language:yom", "language:yor", "language:yua", "language:yue", "language:zai", "language:zea", "language:zho", "language:zlm", "language:zne", "language:zpa", "language:zsm", "language:zul", "license:other", "arxiv:2305.12182", "region:us" ]
null
2023-11-01T10:25:59Z
--- license: other license_name: license license_link: LICENSE configs: - config_name: mlt_Mlym data_files: - split: train path: "mlt_Mlym/train/*.arrow" - split: dev path: "mlt_Mlym/dev/*.arrow" - split: test path: "mlt_Mlym/test/*.arrow" - config_name: knv_Latn data_files: - split: train path: "knv_Latn/train/*.arrow" - split: dev path: "knv_Latn/dev/*.arrow" - split: test path: "knv_Latn/test/*.arrow" - config_name: guj_Arab data_files: - split: train path: "guj_Arab/train/*.arrow" - split: dev path: "guj_Arab/dev/*.arrow" - split: test path: "guj_Arab/test/*.arrow" - config_name: ton_Latn data_files: - split: train path: "ton_Latn/train/*.arrow" - split: dev path: "ton_Latn/dev/*.arrow" - split: test path: "ton_Latn/test/*.arrow" - config_name: nld_Latn data_files: - split: train path: "nld_Latn/train/*.arrow" - split: dev path: "nld_Latn/dev/*.arrow" - split: test path: "nld_Latn/test/*.arrow" - config_name: tzo_Latn data_files: - split: train path: "tzo_Latn/train/*.arrow" - split: dev path: "tzo_Latn/dev/*.arrow" - split: test path: "tzo_Latn/test/*.arrow" - config_name: tsn_Hani data_files: - split: train path: "tsn_Hani/train/*.arrow" - split: dev path: "tsn_Hani/dev/*.arrow" - split: test path: "tsn_Hani/test/*.arrow" - config_name: aze_Zinh data_files: - split: train path: "aze_Zinh/train/*.arrow" - split: dev path: "aze_Zinh/dev/*.arrow" - split: test path: "aze_Zinh/test/*.arrow" - config_name: cuk_Latn data_files: - split: train path: "cuk_Latn/train/*.arrow" - split: dev path: "cuk_Latn/dev/*.arrow" - split: test path: "cuk_Latn/test/*.arrow" - config_name: uzb_Cyrl data_files: - split: train path: "uzb_Cyrl/train/*.arrow" - split: dev path: "uzb_Cyrl/dev/*.arrow" - split: test path: "uzb_Cyrl/test/*.arrow" - config_name: jav_Latn data_files: - split: train path: "jav_Latn/train/*.arrow" - split: dev path: "jav_Latn/dev/*.arrow" - split: test path: "jav_Latn/test/*.arrow" - config_name: rap_Latn data_files: - split: train path: "rap_Latn/train/*.arrow" - split: dev path: "rap_Latn/dev/*.arrow" - split: test path: "rap_Latn/test/*.arrow" - config_name: bak_Cyrl data_files: - split: train path: "bak_Cyrl/train/*.arrow" - split: dev path: "bak_Cyrl/dev/*.arrow" - split: test path: "bak_Cyrl/test/*.arrow" - config_name: por_Latn data_files: - split: train path: "por_Latn/train/*.arrow" - split: dev path: "por_Latn/dev/*.arrow" - split: test path: "por_Latn/test/*.arrow" - config_name: mlt_Hang data_files: - split: train path: "mlt_Hang/train/*.arrow" - split: dev path: "mlt_Hang/dev/*.arrow" - split: test path: "mlt_Hang/test/*.arrow" - config_name: quy_Latn data_files: - split: train path: "quy_Latn/train/*.arrow" - split: dev path: "quy_Latn/dev/*.arrow" - split: test path: "quy_Latn/test/*.arrow" - config_name: hnj_Latn data_files: - split: train path: "hnj_Latn/train/*.arrow" - split: dev path: "hnj_Latn/dev/*.arrow" - split: test path: "hnj_Latn/test/*.arrow" - config_name: ast_Latn data_files: - split: train path: "ast_Latn/train/*.arrow" - split: dev path: "ast_Latn/dev/*.arrow" - split: test path: "ast_Latn/test/*.arrow" - config_name: cos_Latn data_files: - split: train path: "cos_Latn/train/*.arrow" - split: dev path: "cos_Latn/dev/*.arrow" - split: test path: "cos_Latn/test/*.arrow" - config_name: fon_Latn data_files: - split: train path: "fon_Latn/train/*.arrow" - split: dev path: "fon_Latn/dev/*.arrow" - split: test path: "fon_Latn/test/*.arrow" - config_name: sna_Latn data_files: - split: train path: "sna_Latn/train/*.arrow" - split: dev path: "sna_Latn/dev/*.arrow" - split: test path: "sna_Latn/test/*.arrow" - config_name: dzo_Tibt data_files: - split: train path: "dzo_Tibt/train/*.arrow" - split: dev path: "dzo_Tibt/dev/*.arrow" - split: test path: "dzo_Tibt/test/*.arrow" - config_name: nob_Latn data_files: - split: train path: "nob_Latn/train/*.arrow" - split: dev path: "nob_Latn/dev/*.arrow" - split: test path: "nob_Latn/test/*.arrow" - config_name: nch_Latn data_files: - split: train path: "nch_Latn/train/*.arrow" - split: dev path: "nch_Latn/dev/*.arrow" - split: test path: "nch_Latn/test/*.arrow" - config_name: che_Cyrl data_files: - split: train path: "che_Cyrl/train/*.arrow" - split: dev path: "che_Cyrl/dev/*.arrow" - split: test path: "che_Cyrl/test/*.arrow" - config_name: ext_Latn data_files: - split: train path: "ext_Latn/train/*.arrow" - split: dev path: "ext_Latn/dev/*.arrow" - split: test path: "ext_Latn/test/*.arrow" - config_name: dtp_Latn data_files: - split: train path: "dtp_Latn/train/*.arrow" - split: dev path: "dtp_Latn/dev/*.arrow" - split: test path: "dtp_Latn/test/*.arrow" - config_name: yue_Hani data_files: - split: train path: "yue_Hani/train/*.arrow" - split: dev path: "yue_Hani/dev/*.arrow" - split: test path: "yue_Hani/test/*.arrow" - config_name: kbd_Cyrl data_files: - split: train path: "kbd_Cyrl/train/*.arrow" - split: dev path: "kbd_Cyrl/dev/*.arrow" - split: test path: "kbd_Cyrl/test/*.arrow" - config_name: mar_Deva data_files: - split: train path: "mar_Deva/train/*.arrow" - split: dev path: "mar_Deva/dev/*.arrow" - split: test path: "mar_Deva/test/*.arrow" - config_name: ron_Latn data_files: - split: train path: "ron_Latn/train/*.arrow" - split: dev path: "ron_Latn/dev/*.arrow" - split: test path: "ron_Latn/test/*.arrow" - config_name: acr_Latn data_files: - split: train path: "acr_Latn/train/*.arrow" - split: dev path: "acr_Latn/dev/*.arrow" - split: test path: "acr_Latn/test/*.arrow" - config_name: afb_Arab data_files: - split: train path: "afb_Arab/train/*.arrow" - split: dev path: "afb_Arab/dev/*.arrow" - split: test path: "afb_Arab/test/*.arrow" - config_name: mon_Hani data_files: - split: train path: "mon_Hani/train/*.arrow" - split: dev path: "mon_Hani/dev/*.arrow" - split: test path: "mon_Hani/test/*.arrow" - config_name: sqi_Latn data_files: - split: train path: "sqi_Latn/train/*.arrow" - split: dev path: "sqi_Latn/dev/*.arrow" - split: test path: "sqi_Latn/test/*.arrow" - config_name: eng_Latn data_files: - split: train path: "eng_Latn/train/*.arrow" - split: dev path: "eng_Latn/dev/*.arrow" - split: test path: "eng_Latn/test/*.arrow" - config_name: ksd_Latn data_files: - split: train path: "ksd_Latn/train/*.arrow" - split: dev path: "ksd_Latn/dev/*.arrow" - split: test path: "ksd_Latn/test/*.arrow" - config_name: rus_Cyrl data_files: - split: train path: "rus_Cyrl/train/*.arrow" - split: dev path: "rus_Cyrl/dev/*.arrow" - split: test path: "rus_Cyrl/test/*.arrow" - config_name: bcl_Latn data_files: - split: train path: "bcl_Latn/train/*.arrow" - split: dev path: "bcl_Latn/dev/*.arrow" - split: test path: "bcl_Latn/test/*.arrow" - config_name: ksh_Latn data_files: - split: train path: "ksh_Latn/train/*.arrow" - split: dev path: "ksh_Latn/dev/*.arrow" - split: test path: "ksh_Latn/test/*.arrow" - config_name: hin_Latn data_files: - split: train path: "hin_Latn/train/*.arrow" - split: dev path: "hin_Latn/dev/*.arrow" - split: test path: "hin_Latn/test/*.arrow" - config_name: myv_Cyrl data_files: - split: train path: "myv_Cyrl/train/*.arrow" - split: dev path: "myv_Cyrl/dev/*.arrow" - split: test path: "myv_Cyrl/test/*.arrow" - config_name: kjh_Cyrl data_files: - split: train path: "kjh_Cyrl/train/*.arrow" - split: dev path: "kjh_Cyrl/dev/*.arrow" - split: test path: "kjh_Cyrl/test/*.arrow" - config_name: sah_Cyrl data_files: - split: train path: "sah_Cyrl/train/*.arrow" - split: dev path: "sah_Cyrl/dev/*.arrow" - split: test path: "sah_Cyrl/test/*.arrow" - config_name: naq_Latn data_files: - split: train path: "naq_Latn/train/*.arrow" - split: dev path: "naq_Latn/dev/*.arrow" - split: test path: "naq_Latn/test/*.arrow" - config_name: tdt_Latn data_files: - split: train path: "tdt_Latn/train/*.arrow" - split: dev path: "tdt_Latn/dev/*.arrow" - split: test path: "tdt_Latn/test/*.arrow" - config_name: kac_Latn data_files: - split: train path: "kac_Latn/train/*.arrow" - split: dev path: "kac_Latn/dev/*.arrow" - split: test path: "kac_Latn/test/*.arrow" - config_name: cak_Latn data_files: - split: train path: "cak_Latn/train/*.arrow" - split: dev path: "cak_Latn/dev/*.arrow" - split: test path: "cak_Latn/test/*.arrow" - config_name: kir_Cyrl data_files: - split: train path: "kir_Cyrl/train/*.arrow" - split: dev path: "kir_Cyrl/dev/*.arrow" - split: test path: "kir_Cyrl/test/*.arrow" - config_name: mps_Latn data_files: - split: train path: "mps_Latn/train/*.arrow" - split: dev path: "mps_Latn/dev/*.arrow" - split: test path: "mps_Latn/test/*.arrow" - config_name: yid_Hebr data_files: - split: train path: "yid_Hebr/train/*.arrow" - split: dev path: "yid_Hebr/dev/*.arrow" - split: test path: "yid_Hebr/test/*.arrow" - config_name: mlt_Beng data_files: - split: train path: "mlt_Beng/train/*.arrow" - split: dev path: "mlt_Beng/dev/*.arrow" - split: test path: "mlt_Beng/test/*.arrow" - config_name: srn_Latn data_files: - split: train path: "srn_Latn/train/*.arrow" - split: dev path: "srn_Latn/dev/*.arrow" - split: test path: "srn_Latn/test/*.arrow" - config_name: div_Thaa data_files: - split: train path: "div_Thaa/train/*.arrow" - split: dev path: "div_Thaa/dev/*.arrow" - split: test path: "div_Thaa/test/*.arrow" - config_name: mlt_Kana data_files: - split: train path: "mlt_Kana/train/*.arrow" - split: dev path: "mlt_Kana/dev/*.arrow" - split: test path: "mlt_Kana/test/*.arrow" - config_name: mkd_Cyrl data_files: - split: train path: "mkd_Cyrl/train/*.arrow" - split: dev path: "mkd_Cyrl/dev/*.arrow" - split: test path: "mkd_Cyrl/test/*.arrow" - config_name: bre_Latn data_files: - split: train path: "bre_Latn/train/*.arrow" - split: dev path: "bre_Latn/dev/*.arrow" - split: test path: "bre_Latn/test/*.arrow" - config_name: tvl_Latn data_files: - split: train path: "tvl_Latn/train/*.arrow" - split: test path: "tvl_Latn/test/*.arrow" - config_name: ven_Latn data_files: - split: train path: "ven_Latn/train/*.arrow" - split: dev path: "ven_Latn/dev/*.arrow" - split: test path: "ven_Latn/test/*.arrow" - config_name: mlt_Mymr data_files: - split: train path: "mlt_Mymr/train/*.arrow" - split: dev path: "mlt_Mymr/dev/*.arrow" - split: test path: "mlt_Mymr/test/*.arrow" - config_name: wuu_Hani data_files: - split: train path: "wuu_Hani/train/*.arrow" - split: dev path: "wuu_Hani/dev/*.arrow" - split: test path: "wuu_Hani/test/*.arrow" - config_name: mwl_Latn data_files: - split: train path: "mwl_Latn/train/*.arrow" - split: dev path: "mwl_Latn/dev/*.arrow" - split: test path: "mwl_Latn/test/*.arrow" - config_name: miq_Latn data_files: - split: train path: "miq_Latn/train/*.arrow" - config_name: slv_Latn data_files: - split: train path: "slv_Latn/train/*.arrow" - split: dev path: "slv_Latn/dev/*.arrow" - split: test path: "slv_Latn/test/*.arrow" - config_name: hrv_Latn data_files: - split: train path: "hrv_Latn/train/*.arrow" - split: dev path: "hrv_Latn/dev/*.arrow" - split: test path: "hrv_Latn/test/*.arrow" - config_name: hmo_Latn data_files: - split: train path: "hmo_Latn/train/*.arrow" - split: dev path: "hmo_Latn/dev/*.arrow" - split: test path: "hmo_Latn/test/*.arrow" - config_name: som_Latn data_files: - split: train path: "som_Latn/train/*.arrow" - split: dev path: "som_Latn/dev/*.arrow" - split: test path: "som_Latn/test/*.arrow" - config_name: bod_Tibt data_files: - split: train path: "bod_Tibt/train/*.arrow" - split: dev path: "bod_Tibt/dev/*.arrow" - split: test path: "bod_Tibt/test/*.arrow" - config_name: pls_Latn data_files: - split: train path: "pls_Latn/train/*.arrow" - split: dev path: "pls_Latn/dev/*.arrow" - split: test path: "pls_Latn/test/*.arrow" - config_name: ile_Latn data_files: - split: train path: "ile_Latn/train/*.arrow" - split: dev path: "ile_Latn/dev/*.arrow" - split: test path: "ile_Latn/test/*.arrow" - config_name: luo_Latn data_files: - split: train path: "luo_Latn/train/*.arrow" - split: dev path: "luo_Latn/dev/*.arrow" - split: test path: "luo_Latn/test/*.arrow" - config_name: pus_Arab data_files: - split: train path: "pus_Arab/train/*.arrow" - split: dev path: "pus_Arab/dev/*.arrow" - split: test path: "pus_Arab/test/*.arrow" - config_name: fao_Latn data_files: - split: train path: "fao_Latn/train/*.arrow" - split: dev path: "fao_Latn/dev/*.arrow" - split: test path: "fao_Latn/test/*.arrow" - config_name: fas_Arab data_files: - split: train path: "fas_Arab/train/*.arrow" - split: dev path: "fas_Arab/dev/*.arrow" - split: test path: "fas_Arab/test/*.arrow" - config_name: swa_Latn data_files: - split: train path: "swa_Latn/train/*.arrow" - split: dev path: "swa_Latn/dev/*.arrow" - split: test path: "swa_Latn/test/*.arrow" - config_name: mlt_Hebr data_files: - split: train path: "mlt_Hebr/train/*.arrow" - split: dev path: "mlt_Hebr/dev/*.arrow" - split: test path: "mlt_Hebr/test/*.arrow" - config_name: ary_Arab data_files: - split: train path: "ary_Arab/train/*.arrow" - split: dev path: "ary_Arab/dev/*.arrow" - split: test path: "ary_Arab/test/*.arrow" - config_name: hus_Latn data_files: - split: train path: "hus_Latn/train/*.arrow" - split: dev path: "hus_Latn/dev/*.arrow" - split: test path: "hus_Latn/test/*.arrow" - config_name: ote_Latn data_files: - split: train path: "ote_Latn/train/*.arrow" - split: dev path: "ote_Latn/dev/*.arrow" - split: test path: "ote_Latn/test/*.arrow" - config_name: ilo_Latn data_files: - split: train path: "ilo_Latn/train/*.arrow" - split: dev path: "ilo_Latn/dev/*.arrow" - split: test path: "ilo_Latn/test/*.arrow" - config_name: abk_Cyrl data_files: - split: train path: "abk_Cyrl/train/*.arrow" - split: dev path: "abk_Cyrl/dev/*.arrow" - split: test path: "abk_Cyrl/test/*.arrow" - config_name: bqc_Latn data_files: - split: train path: "bqc_Latn/train/*.arrow" - split: dev path: "bqc_Latn/dev/*.arrow" - split: test path: "bqc_Latn/test/*.arrow" - config_name: mlt_Taml data_files: - split: train path: "mlt_Taml/train/*.arrow" - split: dev path: "mlt_Taml/dev/*.arrow" - split: test path: "mlt_Taml/test/*.arrow" - config_name: hil_Latn data_files: - split: train path: "hil_Latn/train/*.arrow" - config_name: pon_Latn data_files: - split: train path: "pon_Latn/train/*.arrow" - split: dev path: "pon_Latn/dev/*.arrow" - split: test path: "pon_Latn/test/*.arrow" - config_name: zul_Latn data_files: - split: train path: "zul_Latn/train/*.arrow" - split: dev path: "zul_Latn/dev/*.arrow" - split: test path: "zul_Latn/test/*.arrow" - config_name: als_Latn data_files: - split: train path: "als_Latn/train/*.arrow" - split: dev path: "als_Latn/dev/*.arrow" - split: test path: "als_Latn/test/*.arrow" - config_name: pes_Arab data_files: - split: train path: "pes_Arab/train/*.arrow" - split: dev path: "pes_Arab/dev/*.arrow" - split: test path: "pes_Arab/test/*.arrow" - config_name: bpy_Beng data_files: - split: train path: "bpy_Beng/train/*.arrow" - split: dev path: "bpy_Beng/dev/*.arrow" - split: test path: "bpy_Beng/test/*.arrow" - config_name: bos_Latn data_files: - split: train path: "bos_Latn/train/*.arrow" - split: dev path: "bos_Latn/dev/*.arrow" - split: test path: "bos_Latn/test/*.arrow" - config_name: sot_Latn data_files: - split: train path: "sot_Latn/train/*.arrow" - split: dev path: "sot_Latn/dev/*.arrow" - split: test path: "sot_Latn/test/*.arrow" - config_name: lin_Latn data_files: - split: train path: "lin_Latn/train/*.arrow" - split: dev path: "lin_Latn/dev/*.arrow" - split: test path: "lin_Latn/test/*.arrow" - config_name: tuk_Cyrl data_files: - split: train path: "tuk_Cyrl/train/*.arrow" - split: dev path: "tuk_Cyrl/dev/*.arrow" - config_name: gla_Latn data_files: - split: train path: "gla_Latn/train/*.arrow" - split: dev path: "gla_Latn/dev/*.arrow" - split: test path: "gla_Latn/test/*.arrow" - config_name: wln_Latn data_files: - split: train path: "wln_Latn/train/*.arrow" - split: dev path: "wln_Latn/dev/*.arrow" - split: test path: "wln_Latn/test/*.arrow" - config_name: apc_Arab data_files: - split: train path: "apc_Arab/train/*.arrow" - split: dev path: "apc_Arab/dev/*.arrow" - split: test path: "apc_Arab/test/*.arrow" - config_name: aze_Hira data_files: - split: train path: "aze_Hira/train/*.arrow" - split: dev path: "aze_Hira/dev/*.arrow" - split: test path: "aze_Hira/test/*.arrow" - config_name: hin_Deva data_files: - split: train path: "hin_Deva/train/*.arrow" - split: dev path: "hin_Deva/dev/*.arrow" - split: test path: "hin_Deva/test/*.arrow" - config_name: hye_Armn data_files: - split: train path: "hye_Armn/train/*.arrow" - split: dev path: "hye_Armn/dev/*.arrow" - split: test path: "hye_Armn/test/*.arrow" - config_name: tir_Ethi data_files: - split: train path: "tir_Ethi/train/*.arrow" - split: dev path: "tir_Ethi/dev/*.arrow" - split: test path: "tir_Ethi/test/*.arrow" - config_name: aze_Ethi data_files: - split: train path: "aze_Ethi/train/*.arrow" - split: dev path: "aze_Ethi/dev/*.arrow" - split: test path: "aze_Ethi/test/*.arrow" - config_name: pap_Latn data_files: - split: train path: "pap_Latn/train/*.arrow" - split: dev path: "pap_Latn/dev/*.arrow" - split: test path: "pap_Latn/test/*.arrow" - config_name: mlt_Ethi data_files: - split: train path: "mlt_Ethi/train/*.arrow" - split: dev path: "mlt_Ethi/dev/*.arrow" - split: test path: "mlt_Ethi/test/*.arrow" - config_name: gcf_Latn data_files: - split: train path: "gcf_Latn/train/*.arrow" - split: dev path: "gcf_Latn/dev/*.arrow" - split: test path: "gcf_Latn/test/*.arrow" - config_name: cjk_Latn data_files: - split: train path: "cjk_Latn/train/*.arrow" - split: dev path: "cjk_Latn/dev/*.arrow" - split: test path: "cjk_Latn/test/*.arrow" - config_name: pcd_Latn data_files: - split: train path: "pcd_Latn/train/*.arrow" - split: dev path: "pcd_Latn/dev/*.arrow" - split: test path: "pcd_Latn/test/*.arrow" - config_name: tur_Latn data_files: - split: train path: "tur_Latn/train/*.arrow" - split: dev path: "tur_Latn/dev/*.arrow" - split: test path: "tur_Latn/test/*.arrow" - config_name: kon_Latn data_files: - split: train path: "kon_Latn/train/*.arrow" - split: dev path: "kon_Latn/dev/*.arrow" - split: test path: "kon_Latn/test/*.arrow" - config_name: csy_Latn data_files: - split: train path: "csy_Latn/train/*.arrow" - split: dev path: "csy_Latn/dev/*.arrow" - split: test path: "csy_Latn/test/*.arrow" - config_name: xho_Latn data_files: - split: train path: "xho_Latn/train/*.arrow" - split: dev path: "xho_Latn/dev/*.arrow" - split: test path: "xho_Latn/test/*.arrow" - config_name: guc_Latn data_files: - split: train path: "guc_Latn/train/*.arrow" - config_name: aka_Latn data_files: - split: train path: "aka_Latn/train/*.arrow" - split: dev path: "aka_Latn/dev/*.arrow" - split: test path: "aka_Latn/test/*.arrow" - config_name: kea_Latn data_files: - split: train path: "kea_Latn/train/*.arrow" - split: dev path: "kea_Latn/dev/*.arrow" - split: test path: "kea_Latn/test/*.arrow" - config_name: bar_Latn data_files: - split: train path: "bar_Latn/train/*.arrow" - split: dev path: "bar_Latn/dev/*.arrow" - split: test path: "bar_Latn/test/*.arrow" - config_name: sme_Latn data_files: - split: train path: "sme_Latn/train/*.arrow" - split: dev path: "sme_Latn/dev/*.arrow" - split: test path: "sme_Latn/test/*.arrow" - config_name: aze_Hang data_files: - split: train path: "aze_Hang/train/*.arrow" - split: dev path: "aze_Hang/dev/*.arrow" - split: test path: "aze_Hang/test/*.arrow" - config_name: csb_Latn data_files: - split: train path: "csb_Latn/train/*.arrow" - split: dev path: "csb_Latn/dev/*.arrow" - split: test path: "csb_Latn/test/*.arrow" - config_name: bak_Latn data_files: - split: train path: "bak_Latn/train/*.arrow" - split: dev path: "bak_Latn/dev/*.arrow" - split: test path: "bak_Latn/test/*.arrow" - config_name: djk_Latn data_files: - split: train path: "djk_Latn/train/*.arrow" - split: dev path: "djk_Latn/dev/*.arrow" - split: test path: "djk_Latn/test/*.arrow" - config_name: xav_Latn data_files: - split: train path: "xav_Latn/train/*.arrow" - split: dev path: "xav_Latn/dev/*.arrow" - split: test path: "xav_Latn/test/*.arrow" - config_name: oci_Latn data_files: - split: train path: "oci_Latn/train/*.arrow" - split: dev path: "oci_Latn/dev/*.arrow" - split: test path: "oci_Latn/test/*.arrow" - config_name: acm_Arab data_files: - split: train path: "acm_Arab/train/*.arrow" - split: dev path: "acm_Arab/dev/*.arrow" - split: test path: "acm_Arab/test/*.arrow" - config_name: rmy_Cyrl data_files: - split: train path: "rmy_Cyrl/train/*.arrow" - config_name: krc_Cyrl data_files: - split: train path: "krc_Cyrl/train/*.arrow" - split: dev path: "krc_Cyrl/dev/*.arrow" - split: test path: "krc_Cyrl/test/*.arrow" - config_name: cym_Latn data_files: - split: train path: "cym_Latn/train/*.arrow" - split: dev path: "cym_Latn/dev/*.arrow" - split: test path: "cym_Latn/test/*.arrow" - config_name: lus_Latn data_files: - split: train path: "lus_Latn/train/*.arrow" - split: dev path: "lus_Latn/dev/*.arrow" - split: test path: "lus_Latn/test/*.arrow" - config_name: ngu_Latn data_files: - split: train path: "ngu_Latn/train/*.arrow" - split: dev path: "ngu_Latn/dev/*.arrow" - split: test path: "ngu_Latn/test/*.arrow" - config_name: yom_Latn data_files: - split: train path: "yom_Latn/train/*.arrow" - split: dev path: "yom_Latn/dev/*.arrow" - split: test path: "yom_Latn/test/*.arrow" - config_name: tam_Taml data_files: - split: train path: "tam_Taml/train/*.arrow" - split: dev path: "tam_Taml/dev/*.arrow" - split: test path: "tam_Taml/test/*.arrow" - config_name: ajp_Arab data_files: - split: train path: "ajp_Arab/train/*.arrow" - split: dev path: "ajp_Arab/dev/*.arrow" - split: test path: "ajp_Arab/test/*.arrow" - config_name: epo_Latn data_files: - split: train path: "epo_Latn/train/*.arrow" - split: dev path: "epo_Latn/dev/*.arrow" - split: test path: "epo_Latn/test/*.arrow" - config_name: fra_Latn data_files: - split: train path: "fra_Latn/train/*.arrow" - split: dev path: "fra_Latn/dev/*.arrow" - split: test path: "fra_Latn/test/*.arrow" - config_name: ita_Latn data_files: - split: train path: "ita_Latn/train/*.arrow" - split: dev path: "ita_Latn/dev/*.arrow" - split: test path: "ita_Latn/test/*.arrow" - config_name: seh_Latn data_files: - split: train path: "seh_Latn/train/*.arrow" - split: dev path: "seh_Latn/dev/*.arrow" - split: test path: "seh_Latn/test/*.arrow" - config_name: hbs_Latn data_files: - split: train path: "hbs_Latn/train/*.arrow" - split: dev path: "hbs_Latn/dev/*.arrow" - split: test path: "hbs_Latn/test/*.arrow" - config_name: uzn_Cyrl data_files: - split: train path: "uzn_Cyrl/train/*.arrow" - split: dev path: "uzn_Cyrl/dev/*.arrow" - split: test path: "uzn_Cyrl/test/*.arrow" - config_name: ksw_Mymr data_files: - split: train path: "ksw_Mymr/train/*.arrow" - config_name: pms_Latn data_files: - split: train path: "pms_Latn/train/*.arrow" - split: dev path: "pms_Latn/dev/*.arrow" - split: test path: "pms_Latn/test/*.arrow" - config_name: zlm_Latn data_files: - split: train path: "zlm_Latn/train/*.arrow" - split: dev path: "zlm_Latn/dev/*.arrow" - split: test path: "zlm_Latn/test/*.arrow" - config_name: qub_Latn data_files: - split: train path: "qub_Latn/train/*.arrow" - split: dev path: "qub_Latn/dev/*.arrow" - config_name: arg_Latn data_files: - split: train path: "arg_Latn/train/*.arrow" - split: dev path: "arg_Latn/dev/*.arrow" - split: test path: "arg_Latn/test/*.arrow" - config_name: kaa_Cyrl data_files: - split: train path: "kaa_Cyrl/train/*.arrow" - split: dev path: "kaa_Cyrl/dev/*.arrow" - split: test path: "kaa_Cyrl/test/*.arrow" - config_name: toj_Latn data_files: - split: train path: "toj_Latn/train/*.arrow" - split: dev path: "toj_Latn/dev/*.arrow" - split: test path: "toj_Latn/test/*.arrow" - config_name: aze_Grek data_files: - split: train path: "aze_Grek/train/*.arrow" - split: dev path: "aze_Grek/dev/*.arrow" - split: test path: "aze_Grek/test/*.arrow" - config_name: guj_Cyrl data_files: - split: train path: "guj_Cyrl/train/*.arrow" - split: dev path: "guj_Cyrl/dev/*.arrow" - split: test path: "guj_Cyrl/test/*.arrow" - config_name: kab_Latn data_files: - split: train path: "kab_Latn/train/*.arrow" - split: dev path: "kab_Latn/dev/*.arrow" - split: test path: "kab_Latn/test/*.arrow" - config_name: pan_Guru data_files: - split: train path: "pan_Guru/train/*.arrow" - split: dev path: "pan_Guru/dev/*.arrow" - split: test path: "pan_Guru/test/*.arrow" - config_name: nan_Latn data_files: - split: train path: "nan_Latn/train/*.arrow" - split: dev path: "nan_Latn/dev/*.arrow" - split: test path: "nan_Latn/test/*.arrow" - config_name: aze_Latn data_files: - split: train path: "aze_Latn/train/*.arrow" - split: dev path: "aze_Latn/dev/*.arrow" - split: test path: "aze_Latn/test/*.arrow" - config_name: ara_Arab data_files: - split: train path: "ara_Arab/train/*.arrow" - split: dev path: "ara_Arab/dev/*.arrow" - split: test path: "ara_Arab/test/*.arrow" - config_name: aze_Mymr data_files: - split: train path: "aze_Mymr/train/*.arrow" - split: dev path: "aze_Mymr/dev/*.arrow" - split: test path: "aze_Mymr/test/*.arrow" - config_name: meu_Latn data_files: - split: train path: "meu_Latn/train/*.arrow" - split: dev path: "meu_Latn/dev/*.arrow" - split: test path: "meu_Latn/test/*.arrow" - config_name: mon_Arab data_files: - split: train path: "mon_Arab/train/*.arrow" - split: dev path: "mon_Arab/dev/*.arrow" - split: test path: "mon_Arab/test/*.arrow" - config_name: lvs_Latn data_files: - split: train path: "lvs_Latn/train/*.arrow" - split: dev path: "lvs_Latn/dev/*.arrow" - split: test path: "lvs_Latn/test/*.arrow" - config_name: nbl_Latn data_files: - split: train path: "nbl_Latn/train/*.arrow" - split: dev path: "nbl_Latn/dev/*.arrow" - split: test path: "nbl_Latn/test/*.arrow" - config_name: crh_Latn data_files: - split: train path: "crh_Latn/train/*.arrow" - split: dev path: "crh_Latn/dev/*.arrow" - split: test path: "crh_Latn/test/*.arrow" - config_name: kbp_Latn data_files: - split: train path: "kbp_Latn/train/*.arrow" - split: dev path: "kbp_Latn/dev/*.arrow" - split: test path: "kbp_Latn/test/*.arrow" - config_name: tgl_Latn data_files: - split: train path: "tgl_Latn/train/*.arrow" - split: dev path: "tgl_Latn/dev/*.arrow" - split: test path: "tgl_Latn/test/*.arrow" - config_name: kmb_Latn data_files: - split: train path: "kmb_Latn/train/*.arrow" - split: dev path: "kmb_Latn/dev/*.arrow" - split: test path: "kmb_Latn/test/*.arrow" - config_name: hun_Latn data_files: - split: train path: "hun_Latn/train/*.arrow" - split: dev path: "hun_Latn/dev/*.arrow" - split: test path: "hun_Latn/test/*.arrow" - config_name: aze_Thai data_files: - split: train path: "aze_Thai/train/*.arrow" - split: dev path: "aze_Thai/dev/*.arrow" - split: test path: "aze_Thai/test/*.arrow" - config_name: yao_Latn data_files: - split: train path: "yao_Latn/train/*.arrow" - split: dev path: "yao_Latn/dev/*.arrow" - split: test path: "yao_Latn/test/*.arrow" - config_name: arn_Latn data_files: - split: train path: "arn_Latn/train/*.arrow" - split: dev path: "arn_Latn/dev/*.arrow" - split: test path: "arn_Latn/test/*.arrow" - config_name: jbo_Latn data_files: - split: train path: "jbo_Latn/train/*.arrow" - split: dev path: "jbo_Latn/dev/*.arrow" - split: test path: "jbo_Latn/test/*.arrow" - config_name: mzn_Arab data_files: - split: train path: "mzn_Arab/train/*.arrow" - split: dev path: "mzn_Arab/dev/*.arrow" - split: test path: "mzn_Arab/test/*.arrow" - config_name: lzh_Hani data_files: - split: train path: "lzh_Hani/train/*.arrow" - split: dev path: "lzh_Hani/dev/*.arrow" - split: test path: "lzh_Hani/test/*.arrow" - config_name: heb_Hebr data_files: - split: train path: "heb_Hebr/train/*.arrow" - split: dev path: "heb_Hebr/dev/*.arrow" - split: test path: "heb_Hebr/test/*.arrow" - config_name: bjn_Latn data_files: - split: train path: "bjn_Latn/train/*.arrow" - split: dev path: "bjn_Latn/dev/*.arrow" - split: test path: "bjn_Latn/test/*.arrow" - config_name: gug_Latn data_files: - split: train path: "gug_Latn/train/*.arrow" - split: dev path: "gug_Latn/dev/*.arrow" - split: test path: "gug_Latn/test/*.arrow" - config_name: mlt_Hira data_files: - split: train path: "mlt_Hira/train/*.arrow" - split: dev path: "mlt_Hira/dev/*.arrow" - split: test path: "mlt_Hira/test/*.arrow" - config_name: swc_Latn data_files: - split: train path: "swc_Latn/train/*.arrow" - split: dev path: "swc_Latn/dev/*.arrow" - split: test path: "swc_Latn/test/*.arrow" - config_name: yor_Latn data_files: - split: train path: "yor_Latn/train/*.arrow" - split: dev path: "yor_Latn/dev/*.arrow" - split: test path: "yor_Latn/test/*.arrow" - config_name: ban_Latn data_files: - split: train path: "ban_Latn/train/*.arrow" - split: dev path: "ban_Latn/dev/*.arrow" - split: test path: "ban_Latn/test/*.arrow" - config_name: aze_Guru data_files: - split: train path: "aze_Guru/train/*.arrow" - split: dev path: "aze_Guru/dev/*.arrow" - split: test path: "aze_Guru/test/*.arrow" - config_name: tlh_Latn data_files: - split: train path: "tlh_Latn/train/*.arrow" - split: dev path: "tlh_Latn/dev/*.arrow" - split: test path: "tlh_Latn/test/*.arrow" - config_name: chv_Cyrl data_files: - split: train path: "chv_Cyrl/train/*.arrow" - split: dev path: "chv_Cyrl/dev/*.arrow" - split: test path: "chv_Cyrl/test/*.arrow" - config_name: sin_Sinh data_files: - split: train path: "sin_Sinh/train/*.arrow" - split: dev path: "sin_Sinh/dev/*.arrow" - split: test path: "sin_Sinh/test/*.arrow" - config_name: aze_Gujr data_files: - split: train path: "aze_Gujr/train/*.arrow" - split: dev path: "aze_Gujr/dev/*.arrow" - split: test path: "aze_Gujr/test/*.arrow" - config_name: ind_Latn data_files: - split: train path: "ind_Latn/train/*.arrow" - split: dev path: "ind_Latn/dev/*.arrow" - split: test path: "ind_Latn/test/*.arrow" - config_name: amh_Ethi data_files: - split: train path: "amh_Ethi/train/*.arrow" - split: dev path: "amh_Ethi/dev/*.arrow" - split: test path: "amh_Ethi/test/*.arrow" - config_name: zea_Latn data_files: - split: train path: "zea_Latn/train/*.arrow" - split: dev path: "zea_Latn/dev/*.arrow" - split: test path: "zea_Latn/test/*.arrow" - config_name: kpg_Latn data_files: - split: train path: "kpg_Latn/train/*.arrow" - split: dev path: "kpg_Latn/dev/*.arrow" - split: test path: "kpg_Latn/test/*.arrow" - config_name: glk_Arab data_files: - split: train path: "glk_Arab/train/*.arrow" - split: dev path: "glk_Arab/dev/*.arrow" - split: test path: "glk_Arab/test/*.arrow" - config_name: crh_Cyrl data_files: - split: train path: "crh_Cyrl/train/*.arrow" - split: dev path: "crh_Cyrl/dev/*.arrow" - split: test path: "crh_Cyrl/test/*.arrow" - config_name: nyu_Latn data_files: - split: train path: "nyu_Latn/train/*.arrow" - config_name: aze_Beng data_files: - split: train path: "aze_Beng/train/*.arrow" - split: dev path: "aze_Beng/dev/*.arrow" - split: test path: "aze_Beng/test/*.arrow" - config_name: ibo_Latn data_files: - split: train path: "ibo_Latn/train/*.arrow" - split: dev path: "ibo_Latn/dev/*.arrow" - split: test path: "ibo_Latn/test/*.arrow" - config_name: msa_Latn data_files: - split: train path: "msa_Latn/train/*.arrow" - split: dev path: "msa_Latn/dev/*.arrow" - split: test path: "msa_Latn/test/*.arrow" - config_name: prs_Arab data_files: - split: train path: "prs_Arab/train/*.arrow" - split: dev path: "prs_Arab/dev/*.arrow" - split: test path: "prs_Arab/test/*.arrow" - config_name: nap_Latn data_files: - split: train path: "nap_Latn/train/*.arrow" - split: dev path: "nap_Latn/dev/*.arrow" - split: test path: "nap_Latn/test/*.arrow" - config_name: bik_Latn data_files: - split: train path: "bik_Latn/train/*.arrow" - split: dev path: "bik_Latn/dev/*.arrow" - split: test path: "bik_Latn/test/*.arrow" - config_name: srp_Cyrl data_files: - split: train path: "srp_Cyrl/train/*.arrow" - split: dev path: "srp_Cyrl/dev/*.arrow" - split: test path: "srp_Cyrl/test/*.arrow" - config_name: lao_Laoo data_files: - split: train path: "lao_Laoo/train/*.arrow" - split: dev path: "lao_Laoo/dev/*.arrow" - split: test path: "lao_Laoo/test/*.arrow" - config_name: kom_Cyrl data_files: - split: train path: "kom_Cyrl/train/*.arrow" - split: dev path: "kom_Cyrl/dev/*.arrow" - split: test path: "kom_Cyrl/test/*.arrow" - config_name: nde_Latn data_files: - split: train path: "nde_Latn/train/*.arrow" - split: dev path: "nde_Latn/dev/*.arrow" - split: test path: "nde_Latn/test/*.arrow" - config_name: hui_Latn data_files: - split: train path: "hui_Latn/train/*.arrow" - split: dev path: "hui_Latn/dev/*.arrow" - split: test path: "hui_Latn/test/*.arrow" - config_name: uig_Latn data_files: - split: train path: "uig_Latn/train/*.arrow" - split: dev path: "uig_Latn/dev/*.arrow" - split: test path: "uig_Latn/test/*.arrow" - config_name: new_Deva data_files: - split: train path: "new_Deva/train/*.arrow" - split: dev path: "new_Deva/dev/*.arrow" - split: test path: "new_Deva/test/*.arrow" - config_name: kur_Arab data_files: - split: train path: "kur_Arab/train/*.arrow" - split: dev path: "kur_Arab/dev/*.arrow" - split: test path: "kur_Arab/test/*.arrow" - config_name: sco_Latn data_files: - split: train path: "sco_Latn/train/*.arrow" - split: dev path: "sco_Latn/dev/*.arrow" - split: test path: "sco_Latn/test/*.arrow" - config_name: ayr_Latn data_files: - split: train path: "ayr_Latn/train/*.arrow" - split: dev path: "ayr_Latn/dev/*.arrow" - split: test path: "ayr_Latn/test/*.arrow" - config_name: suz_Deva data_files: - split: train path: "suz_Deva/train/*.arrow" - split: dev path: "suz_Deva/dev/*.arrow" - split: test path: "suz_Deva/test/*.arrow" - config_name: wal_Latn data_files: - split: train path: "wal_Latn/train/*.arrow" - split: dev path: "wal_Latn/dev/*.arrow" - split: test path: "wal_Latn/test/*.arrow" - config_name: mlt_Latn data_files: - split: train path: "mlt_Latn/train/*.arrow" - split: dev path: "mlt_Latn/dev/*.arrow" - split: test path: "mlt_Latn/test/*.arrow" - config_name: asm_Beng data_files: - split: train path: "asm_Beng/train/*.arrow" - split: dev path: "asm_Beng/dev/*.arrow" - split: test path: "asm_Beng/test/*.arrow" - config_name: aze_Syrc data_files: - split: train path: "aze_Syrc/train/*.arrow" - split: dev path: "aze_Syrc/dev/*.arrow" - split: test path: "aze_Syrc/test/*.arrow" - config_name: san_Deva data_files: - split: train path: "san_Deva/train/*.arrow" - split: dev path: "san_Deva/dev/*.arrow" - split: test path: "san_Deva/test/*.arrow" - config_name: kaz_Cyrl data_files: - split: train path: "kaz_Cyrl/train/*.arrow" - split: dev path: "kaz_Cyrl/dev/*.arrow" - split: test path: "kaz_Cyrl/test/*.arrow" - config_name: iba_Latn data_files: - split: train path: "iba_Latn/train/*.arrow" - config_name: tuk_Latn data_files: - split: train path: "tuk_Latn/train/*.arrow" - split: dev path: "tuk_Latn/dev/*.arrow" - split: test path: "tuk_Latn/test/*.arrow" - config_name: nso_Latn data_files: - split: train path: "nso_Latn/train/*.arrow" - split: dev path: "nso_Latn/dev/*.arrow" - split: test path: "nso_Latn/test/*.arrow" - config_name: aze_Geor data_files: - split: train path: "aze_Geor/train/*.arrow" - split: dev path: "aze_Geor/dev/*.arrow" - split: test path: "aze_Geor/test/*.arrow" - config_name: run_Latn data_files: - split: train path: "run_Latn/train/*.arrow" - split: dev path: "run_Latn/dev/*.arrow" - split: test path: "run_Latn/test/*.arrow" - config_name: ctu_Latn data_files: - split: train path: "ctu_Latn/train/*.arrow" - split: dev path: "ctu_Latn/dev/*.arrow" - split: test path: "ctu_Latn/test/*.arrow" - config_name: bam_Latn data_files: - split: train path: "bam_Latn/train/*.arrow" - split: dev path: "bam_Latn/dev/*.arrow" - split: test path: "bam_Latn/test/*.arrow" - config_name: fin_Latn data_files: - split: train path: "fin_Latn/train/*.arrow" - split: dev path: "fin_Latn/dev/*.arrow" - split: test path: "fin_Latn/test/*.arrow" - config_name: gor_Latn data_files: - split: train path: "gor_Latn/train/*.arrow" - split: dev path: "gor_Latn/dev/*.arrow" - split: test path: "gor_Latn/test/*.arrow" - config_name: kmr_Latn data_files: - split: train path: "kmr_Latn/train/*.arrow" - split: dev path: "kmr_Latn/dev/*.arrow" - split: test path: "kmr_Latn/test/*.arrow" - config_name: ben_Beng data_files: - split: train path: "ben_Beng/train/*.arrow" - split: dev path: "ben_Beng/dev/*.arrow" - split: test path: "ben_Beng/test/*.arrow" - config_name: pag_Latn data_files: - split: train path: "pag_Latn/train/*.arrow" - split: dev path: "pag_Latn/dev/*.arrow" - split: test path: "pag_Latn/test/*.arrow" - config_name: niu_Latn data_files: - split: train path: "niu_Latn/train/*.arrow" - config_name: xmf_Geor data_files: - split: train path: "xmf_Geor/train/*.arrow" - split: dev path: "xmf_Geor/dev/*.arrow" - split: test path: "xmf_Geor/test/*.arrow" - config_name: ekk_Latn data_files: - split: train path: "ekk_Latn/train/*.arrow" - split: dev path: "ekk_Latn/dev/*.arrow" - split: test path: "ekk_Latn/test/*.arrow" - config_name: lmo_Latn data_files: - split: train path: "lmo_Latn/train/*.arrow" - split: dev path: "lmo_Latn/dev/*.arrow" - split: test path: "lmo_Latn/test/*.arrow" - config_name: mhr_Cyrl data_files: - split: train path: "mhr_Cyrl/train/*.arrow" - split: dev path: "mhr_Cyrl/dev/*.arrow" - split: test path: "mhr_Cyrl/test/*.arrow" - config_name: plt_Latn data_files: - split: train path: "plt_Latn/train/*.arrow" - split: dev path: "plt_Latn/dev/*.arrow" - split: test path: "plt_Latn/test/*.arrow" - config_name: qvi_Latn data_files: - split: train path: "qvi_Latn/train/*.arrow" - split: dev path: "qvi_Latn/dev/*.arrow" - split: test path: "qvi_Latn/test/*.arrow" - config_name: mlt_Zinh data_files: - split: train path: "mlt_Zinh/train/*.arrow" - split: dev path: "mlt_Zinh/dev/*.arrow" - split: test path: "mlt_Zinh/test/*.arrow" - config_name: roh_Latn data_files: - split: train path: "roh_Latn/train/*.arrow" - split: dev path: "roh_Latn/dev/*.arrow" - split: test path: "roh_Latn/test/*.arrow" - config_name: mah_Latn data_files: - split: train path: "mah_Latn/train/*.arrow" - config_name: npi_Deva data_files: - split: train path: "npi_Deva/train/*.arrow" - split: dev path: "npi_Deva/dev/*.arrow" - split: test path: "npi_Deva/test/*.arrow" - config_name: guj_Telu data_files: - split: train path: "guj_Telu/train/*.arrow" - split: dev path: "guj_Telu/dev/*.arrow" - split: test path: "guj_Telu/test/*.arrow" - config_name: tok_Latn data_files: - split: train path: "tok_Latn/train/*.arrow" - split: dev path: "tok_Latn/dev/*.arrow" - split: test path: "tok_Latn/test/*.arrow" - config_name: eml_Latn data_files: - split: train path: "eml_Latn/train/*.arrow" - split: dev path: "eml_Latn/dev/*.arrow" - split: test path: "eml_Latn/test/*.arrow" - config_name: pnb_Arab data_files: - split: train path: "pnb_Arab/train/*.arrow" - split: dev path: "pnb_Arab/dev/*.arrow" - split: test path: "pnb_Arab/test/*.arrow" - config_name: tsn_Hira data_files: - split: train path: "tsn_Hira/train/*.arrow" - split: dev path: "tsn_Hira/dev/*.arrow" - split: test path: "tsn_Hira/test/*.arrow" - config_name: nav_Latn data_files: - split: train path: "nav_Latn/train/*.arrow" - split: dev path: "nav_Latn/dev/*.arrow" - split: test path: "nav_Latn/test/*.arrow" - config_name: hyw_Latn data_files: - split: train path: "hyw_Latn/train/*.arrow" - config_name: cat_Latn data_files: - split: train path: "cat_Latn/train/*.arrow" - split: dev path: "cat_Latn/dev/*.arrow" - split: test path: "cat_Latn/test/*.arrow" - config_name: gym_Latn data_files: - split: train path: "gym_Latn/train/*.arrow" - split: dev path: "gym_Latn/dev/*.arrow" - split: test path: "gym_Latn/test/*.arrow" - config_name: sat_Olck data_files: - split: train path: "sat_Olck/train/*.arrow" - split: dev path: "sat_Olck/dev/*.arrow" - split: test path: "sat_Olck/test/*.arrow" - config_name: snd_Arab data_files: - split: train path: "snd_Arab/train/*.arrow" - split: dev path: "snd_Arab/dev/*.arrow" - split: test path: "snd_Arab/test/*.arrow" - config_name: isl_Latn data_files: - split: train path: "isl_Latn/train/*.arrow" - split: dev path: "isl_Latn/dev/*.arrow" - split: test path: "isl_Latn/test/*.arrow" - config_name: mlt_Telu data_files: - split: train path: "mlt_Telu/train/*.arrow" - split: dev path: "mlt_Telu/dev/*.arrow" - split: test path: "mlt_Telu/test/*.arrow" - config_name: kal_Latn data_files: - split: train path: "kal_Latn/train/*.arrow" - split: dev path: "kal_Latn/dev/*.arrow" - split: test path: "kal_Latn/test/*.arrow" - config_name: aoj_Latn data_files: - split: train path: "aoj_Latn/train/*.arrow" - split: dev path: "aoj_Latn/dev/*.arrow" - split: test path: "aoj_Latn/test/*.arrow" - config_name: zai_Latn data_files: - split: train path: "zai_Latn/train/*.arrow" - split: dev path: "zai_Latn/dev/*.arrow" - split: test path: "zai_Latn/test/*.arrow" - config_name: guj_Gujr data_files: - split: train path: "guj_Gujr/train/*.arrow" - split: dev path: "guj_Gujr/dev/*.arrow" - split: test path: "guj_Gujr/test/*.arrow" - config_name: min_Latn data_files: - split: train path: "min_Latn/train/*.arrow" - split: dev path: "min_Latn/dev/*.arrow" - split: test path: "min_Latn/test/*.arrow" - config_name: grc_Grek data_files: - split: train path: "grc_Grek/train/*.arrow" - split: dev path: "grc_Grek/dev/*.arrow" - split: test path: "grc_Grek/test/*.arrow" - config_name: hmn_Latn data_files: - split: train path: "hmn_Latn/train/*.arrow" - split: dev path: "hmn_Latn/dev/*.arrow" - split: test path: "hmn_Latn/test/*.arrow" - config_name: ido_Latn data_files: - split: train path: "ido_Latn/train/*.arrow" - split: dev path: "ido_Latn/dev/*.arrow" - split: test path: "ido_Latn/test/*.arrow" - config_name: khm_Khmr data_files: - split: train path: "khm_Khmr/train/*.arrow" - split: dev path: "khm_Khmr/dev/*.arrow" - split: test path: "khm_Khmr/test/*.arrow" - config_name: quh_Latn data_files: - split: train path: "quh_Latn/train/*.arrow" - split: dev path: "quh_Latn/dev/*.arrow" - split: test path: "quh_Latn/test/*.arrow" - config_name: ikk_Latn data_files: - split: train path: "ikk_Latn/train/*.arrow" - split: dev path: "ikk_Latn/dev/*.arrow" - split: test path: "ikk_Latn/test/*.arrow" - config_name: iku_Cans data_files: - split: train path: "iku_Cans/train/*.arrow" - split: dev path: "iku_Cans/dev/*.arrow" - split: test path: "iku_Cans/test/*.arrow" - config_name: tat_Latn data_files: - split: train path: "tat_Latn/train/*.arrow" - split: dev path: "tat_Latn/dev/*.arrow" - split: test path: "tat_Latn/test/*.arrow" - config_name: bel_Cyrl data_files: - split: train path: "bel_Cyrl/train/*.arrow" - split: dev path: "bel_Cyrl/dev/*.arrow" - split: test path: "bel_Cyrl/test/*.arrow" - config_name: dyu_Latn data_files: - split: train path: "dyu_Latn/train/*.arrow" - split: dev path: "dyu_Latn/dev/*.arrow" - split: test path: "dyu_Latn/test/*.arrow" - config_name: guj_Thai data_files: - split: train path: "guj_Thai/train/*.arrow" - split: dev path: "guj_Thai/dev/*.arrow" - split: test path: "guj_Thai/test/*.arrow" - config_name: que_Latn data_files: - split: train path: "que_Latn/train/*.arrow" - split: dev path: "que_Latn/dev/*.arrow" - split: test path: "que_Latn/test/*.arrow" - config_name: wol_Latn data_files: - split: train path: "wol_Latn/train/*.arrow" - split: dev path: "wol_Latn/dev/*.arrow" - split: test path: "wol_Latn/test/*.arrow" - config_name: hne_Deva data_files: - split: train path: "hne_Deva/train/*.arrow" - split: dev path: "hne_Deva/dev/*.arrow" - split: test path: "hne_Deva/test/*.arrow" - config_name: zho_Hani data_files: - split: train path: "zho_Hani/train/*.arrow" - split: dev path: "zho_Hani/dev/*.arrow" - split: test path: "zho_Hani/test/*.arrow" - config_name: tum_Latn data_files: - split: train path: "tum_Latn/train/*.arrow" - split: dev path: "tum_Latn/dev/*.arrow" - split: test path: "tum_Latn/test/*.arrow" - config_name: swh_Latn data_files: - split: train path: "swh_Latn/train/*.arrow" - split: dev path: "swh_Latn/dev/*.arrow" - split: test path: "swh_Latn/test/*.arrow" - config_name: kua_Latn data_files: - split: train path: "kua_Latn/train/*.arrow" - config_name: ncj_Latn data_files: - split: train path: "ncj_Latn/train/*.arrow" - split: dev path: "ncj_Latn/dev/*.arrow" - split: test path: "ncj_Latn/test/*.arrow" - config_name: ewe_Latn data_files: - split: train path: "ewe_Latn/train/*.arrow" - split: dev path: "ewe_Latn/dev/*.arrow" - split: test path: "ewe_Latn/test/*.arrow" - config_name: mlt_Geor data_files: - split: train path: "mlt_Geor/train/*.arrow" - split: dev path: "mlt_Geor/dev/*.arrow" - split: test path: "mlt_Geor/test/*.arrow" - config_name: hat_Latn data_files: - split: train path: "hat_Latn/train/*.arrow" - split: dev path: "hat_Latn/dev/*.arrow" - split: test path: "hat_Latn/test/*.arrow" - config_name: guj_Hani data_files: - split: train path: "guj_Hani/train/*.arrow" - split: dev path: "guj_Hani/dev/*.arrow" - split: test path: "guj_Hani/test/*.arrow" - config_name: ina_Latn data_files: - split: train path: "ina_Latn/train/*.arrow" - split: dev path: "ina_Latn/dev/*.arrow" - split: test path: "ina_Latn/test/*.arrow" - config_name: ahk_Latn data_files: - split: train path: "ahk_Latn/train/*.arrow" - split: dev path: "ahk_Latn/dev/*.arrow" - split: test path: "ahk_Latn/test/*.arrow" - config_name: srm_Latn data_files: - split: train path: "srm_Latn/train/*.arrow" - split: dev path: "srm_Latn/dev/*.arrow" - split: test path: "srm_Latn/test/*.arrow" - config_name: lug_Latn data_files: - split: train path: "lug_Latn/train/*.arrow" - split: dev path: "lug_Latn/dev/*.arrow" - split: test path: "lug_Latn/test/*.arrow" - config_name: ach_Latn data_files: - split: train path: "ach_Latn/train/*.arrow" - config_name: rmy_Latn data_files: - split: train path: "rmy_Latn/train/*.arrow" - split: dev path: "rmy_Latn/dev/*.arrow" - split: test path: "rmy_Latn/test/*.arrow" - config_name: smo_Latn data_files: - split: train path: "smo_Latn/train/*.arrow" - split: dev path: "smo_Latn/dev/*.arrow" - split: test path: "smo_Latn/test/*.arrow" - config_name: mos_Latn data_files: - split: train path: "mos_Latn/train/*.arrow" - split: dev path: "mos_Latn/dev/*.arrow" - split: test path: "mos_Latn/test/*.arrow" - config_name: srd_Latn data_files: - split: train path: "srd_Latn/train/*.arrow" - split: dev path: "srd_Latn/dev/*.arrow" - split: test path: "srd_Latn/test/*.arrow" - config_name: srp_Latn data_files: - split: train path: "srp_Latn/train/*.arrow" - split: dev path: "srp_Latn/dev/*.arrow" - split: test path: "srp_Latn/test/*.arrow" - config_name: azb_Arab data_files: - split: train path: "azb_Arab/train/*.arrow" - split: dev path: "azb_Arab/dev/*.arrow" - split: test path: "azb_Arab/test/*.arrow" - config_name: aze_Arab data_files: - split: train path: "aze_Arab/train/*.arrow" - split: dev path: "aze_Arab/dev/*.arrow" - split: test path: "aze_Arab/test/*.arrow" - config_name: ori_Orya data_files: - split: train path: "ori_Orya/train/*.arrow" - split: dev path: "ori_Orya/dev/*.arrow" - split: test path: "ori_Orya/test/*.arrow" - config_name: mzh_Latn data_files: - split: train path: "mzh_Latn/train/*.arrow" - split: dev path: "mzh_Latn/dev/*.arrow" - split: test path: "mzh_Latn/test/*.arrow" - config_name: kur_Latn data_files: - split: train path: "kur_Latn/train/*.arrow" - split: dev path: "kur_Latn/dev/*.arrow" - split: test path: "kur_Latn/test/*.arrow" - config_name: wbm_Latn data_files: - split: train path: "wbm_Latn/train/*.arrow" - split: dev path: "wbm_Latn/dev/*.arrow" - config_name: crs_Latn data_files: - split: train path: "crs_Latn/train/*.arrow" - config_name: aze_Deva data_files: - split: train path: "aze_Deva/train/*.arrow" - split: dev path: "aze_Deva/dev/*.arrow" - split: test path: "aze_Deva/test/*.arrow" - config_name: tsn_Arab data_files: - split: train path: "tsn_Arab/train/*.arrow" - split: dev path: "tsn_Arab/dev/*.arrow" - split: test path: "tsn_Arab/test/*.arrow" - config_name: ada_Latn data_files: - split: train path: "ada_Latn/train/*.arrow" - config_name: hif_Latn data_files: - split: train path: "hif_Latn/train/*.arrow" - split: dev path: "hif_Latn/dev/*.arrow" - split: test path: "hif_Latn/test/*.arrow" - config_name: guj_Grek data_files: - split: train path: "guj_Grek/train/*.arrow" - split: dev path: "guj_Grek/dev/*.arrow" - split: test path: "guj_Grek/test/*.arrow" - config_name: pcm_Latn data_files: - split: train path: "pcm_Latn/train/*.arrow" - split: dev path: "pcm_Latn/dev/*.arrow" - split: test path: "pcm_Latn/test/*.arrow" - config_name: tso_Latn data_files: - split: train path: "tso_Latn/train/*.arrow" - split: dev path: "tso_Latn/dev/*.arrow" - split: test path: "tso_Latn/test/*.arrow" - config_name: nor_Latn data_files: - split: train path: "nor_Latn/train/*.arrow" - split: dev path: "nor_Latn/dev/*.arrow" - split: test path: "nor_Latn/test/*.arrow" - config_name: bsb_Latn data_files: - split: train path: "bsb_Latn/train/*.arrow" - split: dev path: "bsb_Latn/dev/*.arrow" - split: test path: "bsb_Latn/test/*.arrow" - config_name: uig_Cyrl data_files: - split: train path: "uig_Cyrl/train/*.arrow" - split: dev path: "uig_Cyrl/dev/*.arrow" - split: test path: "uig_Cyrl/test/*.arrow" - config_name: gaa_Latn data_files: - split: train path: "gaa_Latn/train/*.arrow" - config_name: ukr_Cyrl data_files: - split: train path: "ukr_Cyrl/train/*.arrow" - split: dev path: "ukr_Cyrl/dev/*.arrow" - split: test path: "ukr_Cyrl/test/*.arrow" - config_name: lav_Latn data_files: - split: train path: "lav_Latn/train/*.arrow" - split: dev path: "lav_Latn/dev/*.arrow" - split: test path: "lav_Latn/test/*.arrow" - config_name: mon_Latn data_files: - split: train path: "mon_Latn/train/*.arrow" - split: dev path: "mon_Latn/dev/*.arrow" - split: test path: "mon_Latn/test/*.arrow" - config_name: nep_Deva data_files: - split: train path: "nep_Deva/train/*.arrow" - split: dev path: "nep_Deva/dev/*.arrow" - split: test path: "nep_Deva/test/*.arrow" - config_name: aze_Telu data_files: - split: train path: "aze_Telu/train/*.arrow" - split: dev path: "aze_Telu/dev/*.arrow" - split: test path: "aze_Telu/test/*.arrow" - config_name: guj_Deva data_files: - split: train path: "guj_Deva/train/*.arrow" - split: dev path: "guj_Deva/dev/*.arrow" - split: test path: "guj_Deva/test/*.arrow" - config_name: pis_Latn data_files: - split: train path: "pis_Latn/train/*.arrow" - config_name: lhu_Latn data_files: - split: train path: "lhu_Latn/train/*.arrow" - split: dev path: "lhu_Latn/dev/*.arrow" - split: test path: "lhu_Latn/test/*.arrow" - config_name: bew_Latn data_files: - split: train path: "bew_Latn/train/*.arrow" - split: dev path: "bew_Latn/dev/*.arrow" - split: test path: "bew_Latn/test/*.arrow" - config_name: nya_Latn data_files: - split: train path: "nya_Latn/train/*.arrow" - split: dev path: "nya_Latn/dev/*.arrow" - split: test path: "nya_Latn/test/*.arrow" - config_name: poh_Latn data_files: - split: train path: "poh_Latn/train/*.arrow" - split: dev path: "poh_Latn/dev/*.arrow" - split: test path: "poh_Latn/test/*.arrow" - config_name: nnb_Latn data_files: - split: train path: "nnb_Latn/train/*.arrow" - split: dev path: "nnb_Latn/dev/*.arrow" - split: test path: "nnb_Latn/test/*.arrow" - config_name: grn_Latn data_files: - split: train path: "grn_Latn/train/*.arrow" - split: dev path: "grn_Latn/dev/*.arrow" - split: test path: "grn_Latn/test/*.arrow" - config_name: mco_Latn data_files: - split: train path: "mco_Latn/train/*.arrow" - split: dev path: "mco_Latn/dev/*.arrow" - split: test path: "mco_Latn/test/*.arrow" - config_name: ory_Orya data_files: - split: train path: "ory_Orya/train/*.arrow" - split: dev path: "ory_Orya/dev/*.arrow" - split: test path: "ory_Orya/test/*.arrow" - config_name: ful_Latn data_files: - split: train path: "ful_Latn/train/*.arrow" - split: dev path: "ful_Latn/dev/*.arrow" - split: test path: "ful_Latn/test/*.arrow" - config_name: diq_Latn data_files: - split: train path: "diq_Latn/train/*.arrow" - split: dev path: "diq_Latn/dev/*.arrow" - split: test path: "diq_Latn/test/*.arrow" - config_name: sag_Latn data_files: - split: train path: "sag_Latn/train/*.arrow" - split: dev path: "sag_Latn/dev/*.arrow" - split: test path: "sag_Latn/test/*.arrow" - config_name: tel_Telu data_files: - split: train path: "tel_Telu/train/*.arrow" - split: dev path: "tel_Telu/dev/*.arrow" - split: test path: "tel_Telu/test/*.arrow" - config_name: afr_Latn data_files: - split: train path: "afr_Latn/train/*.arrow" - split: dev path: "afr_Latn/dev/*.arrow" - split: test path: "afr_Latn/test/*.arrow" - config_name: haw_Latn data_files: - split: train path: "haw_Latn/train/*.arrow" - split: dev path: "haw_Latn/dev/*.arrow" - split: test path: "haw_Latn/test/*.arrow" - config_name: bar_Arab data_files: - split: train path: "bar_Arab/train/*.arrow" - split: dev path: "bar_Arab/dev/*.arrow" - split: test path: "bar_Arab/test/*.arrow" - config_name: umb_Latn data_files: - split: train path: "umb_Latn/train/*.arrow" - split: dev path: "umb_Latn/dev/*.arrow" - split: test path: "umb_Latn/test/*.arrow" - config_name: hsb_Latn data_files: - split: train path: "hsb_Latn/train/*.arrow" - split: dev path: "hsb_Latn/dev/*.arrow" - split: test path: "hsb_Latn/test/*.arrow" - config_name: fij_Latn data_files: - split: train path: "fij_Latn/train/*.arrow" - split: dev path: "fij_Latn/dev/*.arrow" - split: test path: "fij_Latn/test/*.arrow" - config_name: hbs_Cyrl data_files: - split: train path: "hbs_Cyrl/train/*.arrow" - split: dev path: "hbs_Cyrl/dev/*.arrow" - split: test path: "hbs_Cyrl/test/*.arrow" - config_name: san_Latn data_files: - split: train path: "san_Latn/train/*.arrow" - split: dev path: "san_Latn/dev/*.arrow" - split: test path: "san_Latn/test/*.arrow" - config_name: vls_Latn data_files: - split: train path: "vls_Latn/train/*.arrow" - split: dev path: "vls_Latn/dev/*.arrow" - split: test path: "vls_Latn/test/*.arrow" - config_name: zsm_Latn data_files: - split: train path: "zsm_Latn/train/*.arrow" - split: dev path: "zsm_Latn/dev/*.arrow" - split: test path: "zsm_Latn/test/*.arrow" - config_name: lij_Latn data_files: - split: train path: "lij_Latn/train/*.arrow" - split: dev path: "lij_Latn/dev/*.arrow" - split: test path: "lij_Latn/test/*.arrow" - config_name: quc_Latn data_files: - split: train path: "quc_Latn/train/*.arrow" - split: dev path: "quc_Latn/dev/*.arrow" - split: test path: "quc_Latn/test/*.arrow" - config_name: mam_Latn data_files: - split: train path: "mam_Latn/train/*.arrow" - split: dev path: "mam_Latn/dev/*.arrow" - split: test path: "mam_Latn/test/*.arrow" - config_name: tls_Latn data_files: - split: train path: "tls_Latn/train/*.arrow" - split: dev path: "tls_Latn/dev/*.arrow" - split: test path: "tls_Latn/test/*.arrow" - config_name: tuc_Latn data_files: - split: train path: "tuc_Latn/train/*.arrow" - split: dev path: "tuc_Latn/dev/*.arrow" - split: test path: "tuc_Latn/test/*.arrow" - config_name: dan_Latn data_files: - split: train path: "dan_Latn/train/*.arrow" - split: dev path: "dan_Latn/dev/*.arrow" - split: test path: "dan_Latn/test/*.arrow" - config_name: rue_Cyrl data_files: - split: train path: "rue_Cyrl/train/*.arrow" - split: dev path: "rue_Cyrl/dev/*.arrow" - split: test path: "rue_Cyrl/test/*.arrow" - config_name: mlt_Guru data_files: - split: train path: "mlt_Guru/train/*.arrow" - split: dev path: "mlt_Guru/dev/*.arrow" - split: test path: "mlt_Guru/test/*.arrow" - config_name: ace_Latn data_files: - split: train path: "ace_Latn/train/*.arrow" - split: dev path: "ace_Latn/dev/*.arrow" - split: test path: "ace_Latn/test/*.arrow" - config_name: bem_Latn data_files: - split: train path: "bem_Latn/train/*.arrow" - split: dev path: "bem_Latn/dev/*.arrow" - split: test path: "bem_Latn/test/*.arrow" - config_name: kam_Latn data_files: - split: train path: "kam_Latn/train/*.arrow" - split: dev path: "kam_Latn/dev/*.arrow" - split: test path: "kam_Latn/test/*.arrow" - config_name: uig_Hani data_files: - split: train path: "uig_Hani/train/*.arrow" - split: dev path: "uig_Hani/dev/*.arrow" - split: test path: "uig_Hani/test/*.arrow" - config_name: kaa_Latn data_files: - split: train path: "kaa_Latn/train/*.arrow" - split: dev path: "kaa_Latn/dev/*.arrow" - split: test path: "kaa_Latn/test/*.arrow" - config_name: ndo_Latn data_files: - split: train path: "ndo_Latn/train/*.arrow" - split: dev path: "ndo_Latn/dev/*.arrow" - split: test path: "ndo_Latn/test/*.arrow" - config_name: aze_Knda data_files: - split: train path: "aze_Knda/train/*.arrow" - split: dev path: "aze_Knda/dev/*.arrow" - split: test path: "aze_Knda/test/*.arrow" - config_name: oss_Cyrl data_files: - split: train path: "oss_Cyrl/train/*.arrow" - split: dev path: "oss_Cyrl/dev/*.arrow" - split: test path: "oss_Cyrl/test/*.arrow" - config_name: lit_Latn data_files: - split: train path: "lit_Latn/train/*.arrow" - split: dev path: "lit_Latn/dev/*.arrow" - split: test path: "lit_Latn/test/*.arrow" - config_name: frr_Latn data_files: - split: train path: "frr_Latn/train/*.arrow" - split: dev path: "frr_Latn/dev/*.arrow" - split: test path: "frr_Latn/test/*.arrow" - config_name: yap_Latn data_files: - split: train path: "yap_Latn/train/*.arrow" - split: dev path: "yap_Latn/dev/*.arrow" - split: test path: "yap_Latn/test/*.arrow" - config_name: gom_Latn data_files: - split: train path: "gom_Latn/train/*.arrow" - split: dev path: "gom_Latn/dev/*.arrow" - split: test path: "gom_Latn/test/*.arrow" - config_name: swe_Latn data_files: - split: train path: "swe_Latn/train/*.arrow" - split: dev path: "swe_Latn/dev/*.arrow" - split: test path: "swe_Latn/test/*.arrow" - config_name: lfn_Latn data_files: - split: train path: "lfn_Latn/train/*.arrow" - split: dev path: "lfn_Latn/dev/*.arrow" - split: test path: "lfn_Latn/test/*.arrow" - config_name: cmn_Hani data_files: - split: train path: "cmn_Hani/train/*.arrow" - split: dev path: "cmn_Hani/dev/*.arrow" - split: test path: "cmn_Hani/test/*.arrow" - config_name: mon_Cyrl data_files: - split: train path: "mon_Cyrl/train/*.arrow" - split: dev path: "mon_Cyrl/dev/*.arrow" - split: test path: "mon_Cyrl/test/*.arrow" - config_name: vep_Latn data_files: - split: train path: "vep_Latn/train/*.arrow" - split: dev path: "vep_Latn/dev/*.arrow" - split: test path: "vep_Latn/test/*.arrow" - config_name: ixl_Latn data_files: - split: train path: "ixl_Latn/train/*.arrow" - split: dev path: "ixl_Latn/dev/*.arrow" - split: test path: "ixl_Latn/test/*.arrow" - config_name: mlt_Gujr data_files: - split: train path: "mlt_Gujr/train/*.arrow" - split: dev path: "mlt_Gujr/dev/*.arrow" - split: test path: "mlt_Gujr/test/*.arrow" - config_name: gil_Latn data_files: - split: train path: "gil_Latn/train/*.arrow" - config_name: mau_Latn data_files: - split: train path: "mau_Latn/train/*.arrow" - split: dev path: "mau_Latn/dev/*.arrow" - split: test path: "mau_Latn/test/*.arrow" - config_name: tsn_Latn data_files: - split: train path: "tsn_Latn/train/*.arrow" - split: dev path: "tsn_Latn/dev/*.arrow" - split: test path: "tsn_Latn/test/*.arrow" - config_name: aym_Latn data_files: - split: train path: "aym_Latn/train/*.arrow" - split: dev path: "aym_Latn/dev/*.arrow" - split: test path: "aym_Latn/test/*.arrow" - config_name: vec_Latn data_files: - split: train path: "vec_Latn/train/*.arrow" - split: dev path: "vec_Latn/dev/*.arrow" - split: test path: "vec_Latn/test/*.arrow" - config_name: gom_Deva data_files: - split: train path: "gom_Deva/train/*.arrow" - split: dev path: "gom_Deva/dev/*.arrow" - split: test path: "gom_Deva/test/*.arrow" - config_name: fur_Latn data_files: - split: train path: "fur_Latn/train/*.arrow" - split: dev path: "fur_Latn/dev/*.arrow" - split: test path: "fur_Latn/test/*.arrow" - config_name: kin_Latn data_files: - split: train path: "kin_Latn/train/*.arrow" - split: dev path: "kin_Latn/dev/*.arrow" - split: test path: "kin_Latn/test/*.arrow" - config_name: guj_Hang data_files: - split: train path: "guj_Hang/train/*.arrow" - split: dev path: "guj_Hang/dev/*.arrow" - split: test path: "guj_Hang/test/*.arrow" - config_name: gcr_Latn data_files: - split: train path: "gcr_Latn/train/*.arrow" - config_name: sgs_Latn data_files: - split: train path: "sgs_Latn/train/*.arrow" - split: dev path: "sgs_Latn/dev/*.arrow" - split: test path: "sgs_Latn/test/*.arrow" - config_name: bih_Deva data_files: - split: train path: "bih_Deva/train/*.arrow" - split: dev path: "bih_Deva/dev/*.arrow" - split: test path: "bih_Deva/test/*.arrow" - config_name: guj_Guru data_files: - split: train path: "guj_Guru/train/*.arrow" - split: dev path: "guj_Guru/dev/*.arrow" - split: test path: "guj_Guru/test/*.arrow" - config_name: vie_Latn data_files: - split: train path: "vie_Latn/train/*.arrow" - split: dev path: "vie_Latn/dev/*.arrow" - split: test path: "vie_Latn/test/*.arrow" - config_name: tha_Thai data_files: - split: train path: "tha_Thai/train/*.arrow" - split: dev path: "tha_Thai/dev/*.arrow" - split: test path: "tha_Thai/test/*.arrow" - config_name: pau_Latn data_files: - split: train path: "pau_Latn/train/*.arrow" - config_name: est_Latn data_files: - split: train path: "est_Latn/train/*.arrow" - split: dev path: "est_Latn/dev/*.arrow" - split: test path: "est_Latn/test/*.arrow" - config_name: lue_Latn data_files: - split: train path: "lue_Latn/train/*.arrow" - config_name: rug_Latn data_files: - split: train path: "rug_Latn/train/*.arrow" - split: dev path: "rug_Latn/dev/*.arrow" - split: test path: "rug_Latn/test/*.arrow" - config_name: kjb_Latn data_files: - split: train path: "kjb_Latn/train/*.arrow" - split: dev path: "kjb_Latn/dev/*.arrow" - split: test path: "kjb_Latn/test/*.arrow" - config_name: kik_Latn data_files: - split: train path: "kik_Latn/train/*.arrow" - split: dev path: "kik_Latn/dev/*.arrow" - split: test path: "kik_Latn/test/*.arrow" - config_name: mri_Latn data_files: - split: train path: "mri_Latn/train/*.arrow" - split: dev path: "mri_Latn/dev/*.arrow" - split: test path: "mri_Latn/test/*.arrow" - config_name: ber_Latn data_files: - split: train path: "ber_Latn/train/*.arrow" - split: dev path: "ber_Latn/dev/*.arrow" - split: test path: "ber_Latn/test/*.arrow" - config_name: ssw_Latn data_files: - split: train path: "ssw_Latn/train/*.arrow" - split: dev path: "ssw_Latn/dev/*.arrow" - split: test path: "ssw_Latn/test/*.arrow" - config_name: guj_Beng data_files: - split: train path: "guj_Beng/train/*.arrow" - split: dev path: "guj_Beng/dev/*.arrow" - split: test path: "guj_Beng/test/*.arrow" - config_name: quz_Latn data_files: - split: train path: "quz_Latn/train/*.arrow" - config_name: arb_Arab data_files: - split: train path: "arb_Arab/train/*.arrow" - split: dev path: "arb_Arab/dev/*.arrow" - split: test path: "arb_Arab/test/*.arrow" - config_name: mlt_Sinh data_files: - split: train path: "mlt_Sinh/train/*.arrow" - split: dev path: "mlt_Sinh/dev/*.arrow" - split: test path: "mlt_Sinh/test/*.arrow" - config_name: mai_Deva data_files: - split: train path: "mai_Deva/train/*.arrow" - split: dev path: "mai_Deva/dev/*.arrow" - split: test path: "mai_Deva/test/*.arrow" - config_name: mlt_Thai data_files: - split: train path: "mlt_Thai/train/*.arrow" - split: dev path: "mlt_Thai/dev/*.arrow" - split: test path: "mlt_Thai/test/*.arrow" - config_name: bew_Cyrl data_files: - split: train path: "bew_Cyrl/train/*.arrow" - split: dev path: "bew_Cyrl/dev/*.arrow" - split: test path: "bew_Cyrl/test/*.arrow" - config_name: tat_Cyrl data_files: - split: train path: "tat_Cyrl/train/*.arrow" - split: dev path: "tat_Cyrl/dev/*.arrow" - split: test path: "tat_Cyrl/test/*.arrow" - config_name: mya_Mymr data_files: - split: train path: "mya_Mymr/train/*.arrow" - split: dev path: "mya_Mymr/dev/*.arrow" - split: test path: "mya_Mymr/test/*.arrow" - config_name: alt_Cyrl data_files: - split: train path: "alt_Cyrl/train/*.arrow" - split: dev path: "alt_Cyrl/dev/*.arrow" - split: test path: "alt_Cyrl/test/*.arrow" - config_name: nno_Latn data_files: - split: train path: "nno_Latn/train/*.arrow" - split: dev path: "nno_Latn/dev/*.arrow" - split: test path: "nno_Latn/test/*.arrow" - config_name: hrx_Latn data_files: - split: train path: "hrx_Latn/train/*.arrow" - split: dev path: "hrx_Latn/dev/*.arrow" - split: test path: "hrx_Latn/test/*.arrow" - config_name: hau_Latn data_files: - split: train path: "hau_Latn/train/*.arrow" - split: dev path: "hau_Latn/dev/*.arrow" - split: test path: "hau_Latn/test/*.arrow" - config_name: gsw_Latn data_files: - split: train path: "gsw_Latn/train/*.arrow" - split: dev path: "gsw_Latn/dev/*.arrow" - split: test path: "gsw_Latn/test/*.arrow" - config_name: pam_Latn data_files: - split: train path: "pam_Latn/train/*.arrow" - split: dev path: "pam_Latn/dev/*.arrow" - split: test path: "pam_Latn/test/*.arrow" - config_name: mlt_Deva data_files: - split: train path: "mlt_Deva/train/*.arrow" - split: dev path: "mlt_Deva/dev/*.arrow" - split: test path: "mlt_Deva/test/*.arrow" - config_name: sun_Latn data_files: - split: train path: "sun_Latn/train/*.arrow" - split: dev path: "sun_Latn/dev/*.arrow" - split: test path: "sun_Latn/test/*.arrow" - config_name: aze_Sinh data_files: - split: train path: "aze_Sinh/train/*.arrow" - split: dev path: "aze_Sinh/dev/*.arrow" - split: test path: "aze_Sinh/test/*.arrow" - config_name: lat_Latn data_files: - split: train path: "lat_Latn/train/*.arrow" - split: dev path: "lat_Latn/dev/*.arrow" - split: test path: "lat_Latn/test/*.arrow" - config_name: bis_Latn data_files: - split: train path: "bis_Latn/train/*.arrow" - split: dev path: "bis_Latn/dev/*.arrow" - split: test path: "bis_Latn/test/*.arrow" - config_name: udm_Cyrl data_files: - split: train path: "udm_Cyrl/train/*.arrow" - split: dev path: "udm_Cyrl/dev/*.arrow" - split: test path: "udm_Cyrl/test/*.arrow" - config_name: tca_Latn data_files: - split: train path: "tca_Latn/train/*.arrow" - split: dev path: "tca_Latn/dev/*.arrow" - split: test path: "tca_Latn/test/*.arrow" - config_name: uig_Arab data_files: - split: train path: "uig_Arab/train/*.arrow" - split: dev path: "uig_Arab/dev/*.arrow" - split: test path: "uig_Arab/test/*.arrow" - config_name: glg_Latn data_files: - split: train path: "glg_Latn/train/*.arrow" - split: dev path: "glg_Latn/dev/*.arrow" - split: test path: "glg_Latn/test/*.arrow" - config_name: tah_Latn data_files: - split: train path: "tah_Latn/train/*.arrow" - split: dev path: "tah_Latn/dev/*.arrow" - config_name: glk_Latn data_files: - split: train path: "glk_Latn/train/*.arrow" - split: dev path: "glk_Latn/dev/*.arrow" - split: test path: "glk_Latn/test/*.arrow" - config_name: aze_Tfng data_files: - split: train path: "aze_Tfng/train/*.arrow" - split: dev path: "aze_Tfng/dev/*.arrow" - split: test path: "aze_Tfng/test/*.arrow" - config_name: ckb_Arab data_files: - split: train path: "ckb_Arab/train/*.arrow" - split: dev path: "ckb_Arab/dev/*.arrow" - split: test path: "ckb_Arab/test/*.arrow" - config_name: gle_Latn data_files: - split: train path: "gle_Latn/train/*.arrow" - split: dev path: "gle_Latn/dev/*.arrow" - split: test path: "gle_Latn/test/*.arrow" - config_name: lim_Latn data_files: - split: train path: "lim_Latn/train/*.arrow" - split: dev path: "lim_Latn/dev/*.arrow" - split: test path: "lim_Latn/test/*.arrow" - config_name: slk_Latn data_files: - split: train path: "slk_Latn/train/*.arrow" - split: dev path: "slk_Latn/dev/*.arrow" - split: test path: "slk_Latn/test/*.arrow" - config_name: nds_Latn data_files: - split: train path: "nds_Latn/train/*.arrow" - split: dev path: "nds_Latn/dev/*.arrow" - split: test path: "nds_Latn/test/*.arrow" - config_name: kor_Hang data_files: - split: train path: "kor_Hang/train/*.arrow" - split: dev path: "kor_Hang/dev/*.arrow" - split: test path: "kor_Hang/test/*.arrow" - config_name: uzb_Latn data_files: - split: train path: "uzb_Latn/train/*.arrow" - split: dev path: "uzb_Latn/dev/*.arrow" - split: test path: "uzb_Latn/test/*.arrow" - config_name: pfl_Latn data_files: - split: train path: "pfl_Latn/train/*.arrow" - split: dev path: "pfl_Latn/dev/*.arrow" - split: test path: "pfl_Latn/test/*.arrow" - config_name: azj_Latn data_files: - split: train path: "azj_Latn/train/*.arrow" - split: dev path: "azj_Latn/dev/*.arrow" - split: test path: "azj_Latn/test/*.arrow" - config_name: glv_Latn data_files: - split: train path: "glv_Latn/train/*.arrow" - split: dev path: "glv_Latn/dev/*.arrow" - split: test path: "glv_Latn/test/*.arrow" - config_name: jam_Latn data_files: - split: train path: "jam_Latn/train/*.arrow" - split: dev path: "jam_Latn/dev/*.arrow" - split: test path: "jam_Latn/test/*.arrow" - config_name: kat_Geor data_files: - split: train path: "kat_Geor/train/*.arrow" - split: dev path: "kat_Geor/dev/*.arrow" - split: test path: "kat_Geor/test/*.arrow" - config_name: fry_Latn data_files: - split: train path: "fry_Latn/train/*.arrow" - split: dev path: "fry_Latn/dev/*.arrow" - split: test path: "fry_Latn/test/*.arrow" - config_name: guj_Knda data_files: - split: train path: "guj_Knda/train/*.arrow" - split: dev path: "guj_Knda/dev/*.arrow" - split: test path: "guj_Knda/test/*.arrow" - config_name: kat_Latn data_files: - split: train path: "kat_Latn/train/*.arrow" - split: dev path: "kat_Latn/dev/*.arrow" - split: test path: "kat_Latn/test/*.arrow" - config_name: twi_Latn data_files: - split: train path: "twi_Latn/train/*.arrow" - split: dev path: "twi_Latn/dev/*.arrow" - split: test path: "twi_Latn/test/*.arrow" - config_name: eus_Latn data_files: - split: train path: "eus_Latn/train/*.arrow" - split: dev path: "eus_Latn/dev/*.arrow" - split: test path: "eus_Latn/test/*.arrow" - config_name: toi_Latn data_files: - split: train path: "toi_Latn/train/*.arrow" - config_name: mlt_Armn data_files: - split: train path: "mlt_Armn/train/*.arrow" - split: dev path: "mlt_Armn/dev/*.arrow" - split: test path: "mlt_Armn/test/*.arrow" - config_name: mon_Hira data_files: - split: train path: "mon_Hira/train/*.arrow" - split: dev path: "mon_Hira/dev/*.arrow" - split: test path: "mon_Hira/test/*.arrow" - config_name: mlg_Latn data_files: - split: train path: "mlg_Latn/train/*.arrow" - split: dev path: "mlg_Latn/dev/*.arrow" - split: test path: "mlg_Latn/test/*.arrow" - config_name: tyv_Cyrl data_files: - split: train path: "tyv_Cyrl/train/*.arrow" - split: dev path: "tyv_Cyrl/dev/*.arrow" - split: test path: "tyv_Cyrl/test/*.arrow" - config_name: arz_Arab data_files: - split: train path: "arz_Arab/train/*.arrow" - split: dev path: "arz_Arab/dev/*.arrow" - split: test path: "arz_Arab/test/*.arrow" - config_name: hyw_Armn data_files: - split: train path: "hyw_Armn/train/*.arrow" - config_name: chk_Latn data_files: - split: train path: "chk_Latn/train/*.arrow" - split: dev path: "chk_Latn/dev/*.arrow" - split: test path: "chk_Latn/test/*.arrow" - config_name: vol_Latn data_files: - split: train path: "vol_Latn/train/*.arrow" - split: dev path: "vol_Latn/dev/*.arrow" - split: test path: "vol_Latn/test/*.arrow" - config_name: kek_Latn data_files: - split: train path: "kek_Latn/train/*.arrow" - split: dev path: "kek_Latn/dev/*.arrow" - split: test path: "kek_Latn/test/*.arrow" - config_name: teo_Latn data_files: - split: train path: "teo_Latn/train/*.arrow" - config_name: ell_Grek data_files: - split: train path: "ell_Grek/train/*.arrow" - split: dev path: "ell_Grek/dev/*.arrow" - split: test path: "ell_Grek/test/*.arrow" - config_name: kan_Knda data_files: - split: train path: "kan_Knda/train/*.arrow" - split: dev path: "kan_Knda/dev/*.arrow" - split: test path: "kan_Knda/test/*.arrow" - config_name: tpi_Latn data_files: - split: train path: "tpi_Latn/train/*.arrow" - split: dev path: "tpi_Latn/dev/*.arrow" - split: test path: "tpi_Latn/test/*.arrow" - config_name: rop_Latn data_files: - split: train path: "rop_Latn/train/*.arrow" - split: dev path: "rop_Latn/dev/*.arrow" - split: test path: "rop_Latn/test/*.arrow" - config_name: aze_Mlym data_files: - split: train path: "aze_Mlym/train/*.arrow" - split: dev path: "aze_Mlym/dev/*.arrow" - split: test path: "aze_Mlym/test/*.arrow" - config_name: lua_Latn data_files: - split: train path: "lua_Latn/train/*.arrow" - split: dev path: "lua_Latn/dev/*.arrow" - config_name: mad_Latn data_files: - split: train path: "mad_Latn/train/*.arrow" - split: dev path: "mad_Latn/dev/*.arrow" - split: test path: "mad_Latn/test/*.arrow" - config_name: top_Latn data_files: - split: train path: "top_Latn/train/*.arrow" - split: dev path: "top_Latn/dev/*.arrow" - split: test path: "top_Latn/test/*.arrow" - config_name: scn_Latn data_files: - split: train path: "scn_Latn/train/*.arrow" - split: dev path: "scn_Latn/dev/*.arrow" - split: test path: "scn_Latn/test/*.arrow" - config_name: aze_Thaa data_files: - split: train path: "aze_Thaa/train/*.arrow" - split: dev path: "aze_Thaa/dev/*.arrow" - split: test path: "aze_Thaa/test/*.arrow" - config_name: guj_Latn data_files: - split: train path: "guj_Latn/train/*.arrow" - split: dev path: "guj_Latn/dev/*.arrow" - split: test path: "guj_Latn/test/*.arrow" - config_name: ngl_Latn data_files: - split: train path: "ngl_Latn/train/*.arrow" - split: dev path: "ngl_Latn/dev/*.arrow" - config_name: mal_Mlym data_files: - split: train path: "mal_Mlym/train/*.arrow" - split: dev path: "mal_Mlym/dev/*.arrow" - split: test path: "mal_Mlym/test/*.arrow" - config_name: szl_Latn data_files: - split: train path: "szl_Latn/train/*.arrow" - split: dev path: "szl_Latn/dev/*.arrow" - split: test path: "szl_Latn/test/*.arrow" - config_name: orm_Latn data_files: - split: train path: "orm_Latn/train/*.arrow" - split: dev path: "orm_Latn/dev/*.arrow" - split: test path: "orm_Latn/test/*.arrow" - config_name: urd_Arab data_files: - split: train path: "urd_Arab/train/*.arrow" - split: dev path: "urd_Arab/dev/*.arrow" - split: test path: "urd_Arab/test/*.arrow" - config_name: cbk_Latn data_files: - split: train path: "cbk_Latn/train/*.arrow" - split: dev path: "cbk_Latn/dev/*.arrow" - split: test path: "cbk_Latn/test/*.arrow" multilinguality: - multilingual language: - abk - abn - ace - ach - acm - acr - ada - afb - afr - ahk - ajg - ajp - aka - als - alt - alz - amh - ami - aoj - apc - ara - arb - arn - ary - arz - asm - ast - aym - azb - aze - azj - bak - bam - ban - bas - bba - bbc - bci - bcl - bel - bem - ben - ber - bhw - bih - bik - bim - bin - bis - bjn - bod - bos - bpy - bqc - bre - bsb - bts - btx - bum - bzj - cab - cak - cat - cbk - cce - cfm - cgg - che - chk - chv - chw - cjk - ckb - cmn - cnh - cos - crh - crs - csb - ctd - ctu - cuk - cym - dan - dhv - diq - div - djk - dln - dtp - dua - dyu - dzo - efi - ekk - ell - eml - eng - epo - est - eus - ewe - ext - fao - fas - fat - fij - fin - fon - fra - frr - fry - ful - fur - gaa - gcf - gcr - gil - gkn - gkp - gla - gle - glg - glv - gom - gor - grc - grn - gsw - guc - gug - guj - gur - guw - gym - hat - hau - haw - hbs - heb - her - hif - hil - hin - hmn - hmo - hne - hrv - hrx - hsb - hui - hun - hus - hye - hyw - iba - ibg - ibo - ido - idu - ifa - ifb - ige - ikk - iku - ile - ilo - ina - ind - ish - isl - ita - ixl - izz - jam - jav - jbo - kaa - kab - kac - kal - kam - kan - kat - kaz - kbd - kbp - kea - kek - khm - kik - kin - kir - kjh - kmb - kmr - kom - kon - koo - kor - kos - kpg - kqn - krc - kri - ksd - ksh - kss - ksw - kua - kur - kwn - lam - lao - lat - lav - ldi - leh - lfn - lhu - lij - lim - lin - lit - llb - lmo - loz - lua - lue - lug - lun - luo - lus - lvs - lzh - mad - mah - mai - mal - mam - mar - mau - mbb - mck - mco - mdy - meu - mfe - mgh - mgr - mhr - min - miq - mkd - mlg - mny - mon - mos - mps - mri - mrw - msa - mwl - mwn - mxv - mya - myv - mzh - mzn - nan - nap - naq - nav - nba - nbl - nch - ncj - ncx - ndc - nde - ndo - nds - nep - new - ngl - ngu - nia - niu - nld - nnb - nno - nob - nor - nse - nso - nya - nyk - nyn - nyu - nyy - nzi - oci - ogo - oke - ori - orm - ory - oss - ote - pag - pam - pan - pap - pau - pcd - pcm - pdt - pes - pfl - phm - pis - pls - plt - pms - pnb - poh - pon - por - prk - pus - pxm - qub - quc - que - qug - quw - quy - quz - qvi - rap - rar - rmn - rmy - rng - roh - ron - rue - rug - run - rus - sag - sah - san - sat - scn - sco - seh - sgs - sid - sin - skg - slk - slv - sme - smo - sna - snd - som - sop - sot - sqi - srd - srm - srn - srp - ssw - sun - suz - swa - swe - swh - sxn - szl - tah - tam - tat - tbz - tca - tcf - tdt - tdx - tel - teo - tgl - tha - tih - tir - tiv - tlh - tob - tog - toh - toi - toj - tok - ton - top - tpi - tpm - tsc - tso - tsz - ttj - tuc - tuk - tur - twi - twx - tyv - tzh - tzo - udm - uig - ukr - umb - urd - urh - uzb - uzn - ven - vep - vie - vls - vmw - vol - wal - wes - wln - wol - wuu - xav - xho - xmf - xmv - yao - yap - yid - yom - yor - yua - yue - zai - zea - zho - zlm - zne - zpa - zsm - zul pretty_name: Glot500 Corpus --- # Glot500 Corpus A dataset of natural language data collected by putting together more than 150 existing mono-lingual and multilingual datasets together and crawling known multilingual websites. The focus of this dataset is on 500 extremely low-resource languages. (More Languages still to be uploaded here) This dataset is used to train the [Glot500](https://huggingface.co/cis-lmu/glot500-base) model. - **Homepage:** [homepage](https://github.com/cisnlp/Glot500) - **Repository:** [github](https://github.com/cisnlp/Glot500) - **Paper:** [acl](https://aclanthology.org/2023.acl-long.61/), [arxiv](https://arxiv.org/abs/2305.12182) ## Usage Replace `nbl_Latn` with your specific language. ```python from datasets import load_dataset dataset = load_dataset('cis-lmu/Glot500', 'nbl_Latn', split='train') print(dataset['train'][0]) # First row of nbl_Latn ``` <details> <summary>Click to show supported languages:</summary> ``` ton_Latn nld_Latn tzo_Latn leh_Latn cuk_Latn ibg_Latn uzb_Cyrl jav_Latn rap_Latn zpa_Latn bak_Cyrl por_Latn quy_Latn ast_Latn cos_Latn fon_Latn sna_Latn dzo_Tibt nob_Latn nch_Latn ish_Latn che_Cyrl ext_Latn ldi_Latn dtp_Latn yue_Hani kbd_Cyrl mar_Deva ron_Latn acr_Latn afb_Arab sqi_Latn eng_Latn ksd_Latn rus_Cyrl bcl_Latn ksh_Latn hin_Latn myv_Cyrl kjh_Cyrl sah_Cyrl gkp_Latn naq_Latn tdt_Latn rmn_Cyrl kac_Latn cak_Latn kir_Cyrl mps_Latn yid_Hebr dhv_Latn srn_Latn div_Thaa mkd_Cyrl idu_Latn bre_Latn bas_Latn ven_Latn pxm_Latn wuu_Hani mwl_Latn miq_Latn kss_Latn wes_Latn slv_Latn hrv_Latn hmo_Latn som_Latn bod_Tibt pls_Latn ile_Latn luo_Latn pus_Arab fao_Latn fas_Arab swa_Latn ifb_Latn ary_Arab tbz_Latn hus_Latn ote_Latn ilo_Latn ctd_Latn abk_Cyrl bqc_Latn hil_Latn pon_Latn zul_Latn als_Latn pes_Arab bpy_Beng bos_Latn sot_Latn lin_Latn tuk_Cyrl gla_Latn wln_Latn apc_Arab hin_Deva hye_Armn tir_Ethi pap_Latn gcf_Latn cjk_Latn pcd_Latn tur_Latn kon_Latn mwn_Latn izz_Latn xho_Latn lam_Latn guc_Latn aka_Latn kea_Latn sme_Latn fat_Latn csb_Latn bak_Latn djk_Latn xav_Latn oci_Latn acm_Arab rmy_Cyrl bim_Latn mck_Latn krc_Cyrl cym_Latn lus_Latn ncx_Latn ngu_Latn yom_Latn tam_Taml ajp_Arab epo_Latn fra_Latn ita_Latn seh_Latn sxn_Latn pdt_Latn hbs_Latn uzn_Cyrl bhw_Latn ksw_Mymr pms_Latn zlm_Latn ami_Latn qub_Latn twx_Latn tsz_Latn kaa_Cyrl toj_Latn toh_Latn kos_Latn ogo_Latn kab_Latn pan_Guru nan_Latn aze_Latn prk_Latn ara_Arab meu_Latn nba_Latn lvs_Latn nbl_Latn loz_Latn crh_Latn bci_Latn kbp_Latn tgl_Latn kmb_Latn hun_Latn nzi_Latn yao_Latn arn_Latn hyw_Cyrl vmw_Latn jbo_Latn mzn_Arab lzh_Hani heb_Hebr cce_Latn bjn_Latn gug_Latn yor_Latn ban_Latn tlh_Latn chv_Cyrl sin_Sinh ind_Latn dua_Latn sid_Latn amh_Ethi zea_Latn kpg_Latn crh_Cyrl nyu_Latn dln_Latn ibo_Latn tih_Latn msa_Latn nap_Latn mgr_Latn bik_Latn srp_Cyrl lao_Laoo guw_Latn kom_Cyrl sop_Latn nde_Latn hui_Latn cfm_Latn new_Deva kur_Arab sco_Latn nyk_Latn lun_Latn suz_Deva wal_Latn asm_Beng rar_Latn san_Deva kaz_Cyrl tog_Latn iba_Latn tuk_Latn nso_Latn run_Latn ctu_Latn bam_Latn fin_Latn gor_Latn kmr_Latn ben_Beng pag_Latn niu_Latn xmf_Geor ekk_Latn tsc_Latn lmo_Latn mhr_Cyrl plt_Latn qvi_Latn roh_Latn oke_Latn mah_Latn tok_Latn mgh_Latn eml_Latn urh_Latn pnb_Arab yua_Latn nav_Latn zne_Latn bin_Latn cat_Latn gym_Latn sat_Olck snd_Arab isl_Latn rmn_Grek bba_Latn kal_Latn aoj_Latn qug_Latn zai_Latn guj_Gujr min_Latn tob_Latn grc_Grek hmn_Latn ido_Latn khm_Khmr ikk_Latn iku_Cans tat_Latn bel_Cyrl dyu_Latn que_Latn efi_Latn quw_Latn nyn_Latn wol_Latn hne_Deva zho_Hani swh_Latn bum_Latn kua_Latn ncj_Latn ewe_Latn hat_Latn ina_Latn mfe_Latn ahk_Latn srm_Latn lug_Latn ach_Latn rmy_Latn tpm_Latn smo_Latn mos_Latn srd_Latn srp_Latn azb_Arab ori_Orya mzh_Latn kur_Latn phm_Latn kwn_Latn crs_Latn ada_Latn ttj_Latn hif_Latn tzh_Latn tdx_Latn bbc_Latn cnh_Latn pcm_Latn tso_Latn nor_Latn bsb_Latn kqn_Latn gaa_Latn ukr_Cyrl lav_Latn nep_Deva kmr_Cyrl ige_Latn pis_Latn lhu_Latn nya_Latn tiv_Latn mny_Latn kri_Latn nyy_Latn poh_Latn nnb_Latn grn_Latn mco_Latn ory_Orya ful_Latn diq_Latn sag_Latn tel_Telu afr_Latn haw_Latn umb_Latn hsb_Latn fij_Latn hbs_Cyrl san_Latn vls_Latn zsm_Latn lij_Latn quc_Latn mam_Latn tuc_Latn dan_Latn rue_Cyrl ace_Latn bem_Latn kam_Latn ndo_Latn mbb_Latn mrw_Latn ajg_Latn oss_Cyrl her_Latn lit_Latn frr_Latn yap_Latn bzj_Latn gom_Latn swe_Latn lfn_Latn cmn_Hani mon_Cyrl vep_Latn ixl_Latn gil_Latn mau_Latn aym_Latn gom_Deva fur_Latn cgg_Latn chw_Latn kin_Latn alz_Latn ndc_Latn gcr_Latn rmn_Latn sgs_Latn bih_Deva skg_Latn bts_Latn vie_Latn tha_Thai tcf_Latn pau_Latn est_Latn lue_Latn rug_Latn gur_Latn kik_Latn mri_Latn ber_Latn ssw_Latn cab_Latn quz_Latn arb_Arab mai_Deva tat_Cyrl mya_Mymr alt_Cyrl nno_Latn nse_Latn hrx_Latn hau_Latn koo_Latn gsw_Latn pam_Latn sun_Latn lat_Latn bis_Latn btx_Latn udm_Cyrl xmv_Latn tca_Latn uig_Arab glg_Latn tah_Latn llb_Latn ckb_Arab gle_Latn lim_Latn slk_Latn nds_Latn kor_Hang uzb_Latn gkn_Latn pfl_Latn azj_Latn glv_Latn jam_Latn kat_Geor abn_Latn fry_Latn kat_Latn twi_Latn eus_Latn toi_Latn mlg_Latn ifa_Latn tyv_Cyrl arz_Arab chk_Latn vol_Latn kek_Latn teo_Latn ell_Grek kan_Knda rng_Latn tpi_Latn mdy_Ethi lua_Latn mad_Latn top_Latn scn_Latn ngl_Latn mal_Mlym szl_Latn orm_Latn nia_Latn urd_Arab mxv_Latn cbk_Latn ``` </details> ## License We don't own any part of the data. The original source of each sentence of the data is indicated in dataset field. To see the copyright license of the original datasets visit [here](https://github.com/cisnlp/Glot500#glot500-c). We license the actual packaging, the metadata and the annotations of these data under the cc0-1.0. If you are a website/dataset owner and do not want your data to be included in this corpra, please send us an email at [email protected]. ## Ethical Considerations **1. Biases:** The text corpus may reflect the perspectives, opinions, or demographics of its sources or creators. It is important for users to critically evaluate the text in context especially for news sources and social medias. **2. Representativeness:** While we have aimed for diversity and inclusivity, the text corpus may not fully represent all native speakers. Users should be mindful of any potential underrepresentation. **3. Ethics:** We acknowledge that the collection and use of text data can have ethical implications. We have strived to handle the data responsibly, but we encourage users to consider the broader ethical implications of their own research or applications. ## Citation If you use any part of this code and data in your research, please cite it using the following BibTeX entry. ``` @inproceedings{imanigooghari-etal-2023-glot500, title = "Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages", author = {ImaniGooghari, Ayyoob and Lin, Peiqin and Kargaran, Amir Hossein and Severini, Silvia and Jalili Sabet, Masoud and Kassner, Nora and Ma, Chunlan and Schmid, Helmut and Martins, Andr{\'e} and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich}, editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.acl-long.61", doi = "10.18653/v1/2023.acl-long.61", pages = "1082--1117", abstract = "The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, {``}help{''} from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should notlimit NLP to a small fraction of the world{'}s languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at \url{https://github.com/cisnlp/Glot500}.", } ```
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Atsushi/fungi_indexed_mycological_papers_japanese
Atsushi
2023-12-14T21:21:26Z
165
0
[ "annotations_creators:other", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:ja", "license:cc-by-4.0", "region:us" ]
null
2022-03-02T23:29:22Z
--- annotations_creators: - other language: - ja license: - cc-by-4.0 multilinguality: - monolingual source_datasets: - original size_categories: - 1K<n<10K --- fungi_indexed_mycological_papers_japanese 大菌輪「論文3行まとめ」データセット 最終更新日:2023/12/15(R3-11244まで) ==== ### Languages Japanese This dataset is available in Japanese only. # 概要 Atsushi Nakajima(中島淳志)が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け(インデキシング)した情報を提供しています。 本データセットは、「論文3行まとめ」のコンテンツに含まれる各論文の3行抄録、タグ(索引)、掲載種一覧、比較種一覧をまとめたものです。 「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。 また、本データセットを可視化したWebアプリを[Observableで公開](https://tinyurl.com/2tvryz8u)しています。 ## 関連データセット 「識別形質まとめ」 [Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese) 「Trait Circusデータセット」(統制形質) [Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database) ## 各カラムの説明 * R3ID … 大菌輪「論文3行まとめ」のIDです。 * ja_title_provisional_translate(仮訳和文題名) … 作成者が翻訳したタイトルです。一部、日本語の原題があるものはそれをそのまま使用しています。 * original_title(原文題名) * published_year(出版年) * journal_title(雑誌名) * source(文献リンク) … 各情報の 出典(文献)のURLです。 * daikinrin_url … 大菌輪「論文3行まとめ」のURLです。 * tags … 作成者が論文を全文読んだ上で独自に付与した索引です。カンマ+半角空白区切りです。形態形質、宿主/基質、実験器具/実験手法/試薬、地理的分布、生理/生化学などを幅広く索引しています。 * R3summary_1 … 3行抄録の「1行目」です。 * R3summary_2 … 3行抄録の「2行目」です。 * R3summary_3 … 3行抄録の「3行目」です。 * species_reported(報告種一覧) … 当該論文内で掲載された種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。記号の意味は以下の通りです。 * ★=新種(新亜種・新品種・新変種) * ■= 新産種 * ▲=新組み合わせ * ◆=新学名 * ●=新階級 * (無印)=その他 * species_compared(比較種一覧) … いずれかの報告種と論文中で何らかの比較がなされた種の一覧です。「半角空白+半角スラッシュ+半角空白」区切りです。詳細は「識別形質まとめ」データセット([Atsushi/fungi_diagnostic_chars_comparison_japanese](https://huggingface.co/datasets/Atsushi/fungi_diagnostic_chars_comparison_japanese))を参照してください。 * taxon_reported(分類群一覧) … 報告種に対応する上位分類群をまとめたものです。カンマ+半角空白区切りです。MycoBankの情報を基に付与していますが、最新でない可能性があります。
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Atsushi/fungi_diagnostic_chars_comparison_japanese
Atsushi
2023-12-14T21:19:50Z
163
0
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:ja", "license:cc-by-4.0", "region:us" ]
[ "text-classification" ]
2022-03-02T23:29:22Z
--- annotations_creators: - other language: - ja license: - cc-by-4.0 multilinguality: - monolingual source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification size_categories: - 100K<n<1M --- fungi_diagnostic_chars_comparison_japanese 大菌輪「識別形質まとめ」データセット 最終更新日:2023/12/15(R3-11244まで) ==== ### Languages Japanese This dataset is available in Japanese only. # 概要 Atsushi Nakajima(中島淳志)が個人で運営しているWebサイト[大菌輪](http://mycoscouter.coolblog.jp/daikinrin/) では、数千件以上の菌類分類学論文を「論文3行まとめ」という形で要約および索引付け(インデキシング)した情報を提供しています。 その一環として、ある菌と別の菌の「共通する」あるいは「異なる」識別形質 (diagnostic characters) に関する記述を人手で抽出しています。 本データセットは、抽出された識別形質の一覧に、「色/color」、「形状/shape」などのカテゴリを半自動的に付与して集積したものです。 「論文3行まとめ」は毎日更新していますが、本データセットの更新はおおむね1ヶ月に一度とする予定です。 ## 関連データセット 「論文3行まとめ」 [Atsushi/fungi_indexed_mycological_papers_japanese](https://huggingface.co/datasets/Atsushi/fungi_indexed_mycological_papers_japanese) 「Trait Circusデータセット」(統制形質) [Atsushi/fungi_trait_circus_database](https://huggingface.co/datasets/Atsushi/fungi_trait_circus_database) ## 各カラムの説明 * R3ID … 大菌輪「論文3行まとめ」のIDです。 * No … 各識別文を一意のIDで区別するために、各R3IDにおいてナンバリングしたものです。 * comparison_source … 比較元の分類群(学名)です。 * comparison_target … 比較先の分類群(学名)です。 * sentence … 識別文です。全て日本語です。 * label …半自動的に付与されたカテゴリです(人手で修正していますが、ダブルチェックは行っていないので誤分類もあると思います)。以下の25のカテゴリが存在します。 * サイズ/size * 分子系統解析/molecular_phylogenetic_analysis * 形状/shape * 色/color * 地理的分布/geographical_distribution * 生息環境/habitat * 表面性状/surface_characteristics * 構造/structure * 有無/presence * 形態全般/general_morphology * 位置/position * 二次代謝産物/secondary_metabolite * 呈色反応/chemical_reaction * 数量/amount * 発達/development * 生理学的形質/physiological_characters * 分類/classification * 資化・発酵能/assimilation_and_fermentation * 質感/texture * 味・臭い/taste_and_smell * 病害・病原性関連/disease_and_pathogenecity * 全般/general_characters * 耐性・感受性/resistance_and_susceptibility * 栄養摂取様式/nutrition_style * 未分類/unclassified * common_or_different … 共通する形質は「1」、異なる形質は「0」です。 * data_source … 各情報の 出典(文献)のURLです。
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huggingface/documentation-images
huggingface
2023-12-16T02:11:50Z
154
19
[ "license:cc-by-nc-sa-4.0", "region:us" ]
null
2022-03-02T23:29:22Z
--- license: cc-by-nc-sa-4.0 --- ### This dataset contains images used in the documentation of HuggingFace's libraries. HF Team: Please make sure you optimize the assets before uploading them. My favorite tool for this is https://tinypng.com/.
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xwjiang2010/pile_dedupe_val_tokenized_chunked
xwjiang2010
2023-12-12T21:34:33Z
149
0
[ "region:us" ]
null
2023-12-11T19:16:42Z
--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 6937220668 num_examples: 423311 download_size: 3163118066 dataset_size: 6937220668 configs: - config_name: default data_files: - split: train path: data/train-* ---
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chymaks/Igbo_ner
chymaks
2023-12-12T05:02:33Z
147
0
[ "license:cc-by-nc-2.0", "region:us" ]
null
2023-03-21T14:07:46Z
--- license: cc-by-nc-2.0 ---
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michaelginn/latent-trees-agreement-ID
michaelginn
2023-12-14T03:34:58Z
147
0
[ "region:us" ]
null
2023-11-24T20:32:27Z
--- dataset_info: features: - name: text dtype: string - name: labels dtype: class_label: names: '0': '0' '1': '1' - name: depth dtype: int64 splits: - name: train num_bytes: 107176.8 num_examples: 2400 - name: eval num_bytes: 35725.6 num_examples: 800 - name: test num_bytes: 35725.6 num_examples: 800 download_size: 56457 dataset_size: 178628.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* - split: test path: data/test-* --- # Dataset Card for "latent-trees-agreement-ID" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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hoffman-lab/SkyScenes
hoffman-lab
2023-12-13T10:08:37Z
147
0
[ "task_categories:object-detection", "task_categories:depth-estimation", "task_categories:image-segmentation", "language:en", "license:mit", "dataset", "aerial", "synthetic", "domain adaptation", "sim2real", "arxiv:2312.06719", "region:us" ]
[ "object-detection", "depth-estimation", "image-segmentation" ]
2023-12-01T21:01:18Z
--- license: mit language: - en task_categories: - object-detection - depth-estimation - image-segmentation tags: - dataset - aerial - synthetic - domain adaptation - sim2real --- <div align="center"> # SkyScenes: A Synthetic Dataset for Aerial Scene Understanding [Sahil Khose](https://sahilkhose.github.io/)\*, [Anisha Pal](https://anipal.github.io/)\*, [Aayushi Agarwal](https://www.linkedin.com/in/aayushiag/)\*, [Deepanshi](https://www.linkedin.com/in/deepanshi-d/)\*, [Judy Hoffman](https://faculty.cc.gatech.edu/~judy/), [Prithvijit Chattopadhyay](https://prithv1.xyz/) </div> <!-- This repository is the official Pytorch implementation for [SkyScenes](). --> [![HuggingFace Dataset](https://img.shields.io/badge/🤗-HuggingFace%20Dataset-cyan.svg)](https://huggingface.co/datasets/hoffman-lab/SkyScenes) [![Project Page](https://img.shields.io/badge/Project-Website-orange)](https://github.gatech.edu/pages/hoffman-group/SkyScenes/) [![arXiv](https://img.shields.io/badge/arXiv-SkyScenes-b31b1b.svg)](https://arxiv.org/abs/2312.06719) <!-- [![Watch the Demo](./assets/robust_aerial_videos.mp4)](./assets/robust_aerial_videos.mp4) --> <img src="./assets/skyscene_intro_teaser.png" width="100%"/> ## 🚀 Release Updates Support for downloading the contents of [Images](https://huggingface.co/datasets/hoffman-lab/SkyScenes/tree/main/Images) using [datasets](https://huggingface.co/docs/datasets/index) is under developement and will be made available soon. In the meanwhile it is advised to use wget. ## Dataset Summary Real-world aerial scene understanding is limited by a lack of datasets that contain densely annotated images curated under a diverse set of conditions. Due to inherent challenges in obtaining such images in controlled real-world settings, we present SkyScenes, a synthetic dataset of densely annotated aerial images captured from Unmanned Aerial Vehicle (UAV) perspectives. **SkyScenes** images are carefully curated from **CARLA** to comprehensively capture diversity across layout (urban and rural maps), weather conditions, times of day, pitch angles and altitudes with corresponding semantic, instance and depth annotations. **SkyScenes** features **33,600** images in total, which are spread across 8 towns, 5 weather and daytime conditions and 12 height and pitch variations. <details> <summary>Click to view the detailed list of all variations</summary> - **Layout Variations(Total 8):**: - Town01 - Town02 - Town03 - Town04 - Town05 - Town06 - Town07 - Town10HD _Town07 features Rural Scenes, whereas the rest of the towns feature Urban scenes_ - **Weather & Daytime Variations(Total 5):** - ClearNoon - ClearSunset - ClearNight - CloudyNoon - MidRainyNoon - **Height and Pitch Variations of UAV Flight(Total 12):** - Height = 15m, Pitch = 0° - Height = 15m, Pitch = 45° - Height = 15m, Pitch = 60° - Height = 15m, Pitch = 90° - Height = 35m, Pitch = 0° - Height = 35m, Pitch = 45° - Height = 35m, Pitch = 60° - Height = 35m, Pitch = 90° - Height = 60m, Pitch = 0° - Height = 60m, Pitch = 45° - Height = 60m, Pitch = 60° - Height = 60m, Pitch = 90° </details> <details> <summary>Click to view class definitions, color palette and class IDs for Semantic Segmentation</summary> **SkyScenes** semantic segmentation labels span 28 classes which can be further collapsed to 20 classes. | Class ID | Class ID (collapsed) | RGB Color Palette | Class Name | Definition | |----------|--------------------|-------------------|------------------|----------------------------------------------------------------------------------------------------| | 0 | -1 | <span style="color:rgb(0, 0, 0)"> (0, 0, 0) </span> | unlabeled | Elements/objects in the scene that have not been categorized | | 1 | 2 | <span style="color:rgb(70, 70, 70)"> (70, 70, 70) </span> | building | Includes houses, skyscrapers, and the elements attached to them | | 2 | 4 | <span style="color:rgb(190, 153, 153)"> (190, 153, 153) </span> | fence | Wood or wire assemblies that enclose an area of ground | | 3 | -1 | <span style="color:rgb(55, 90, 80)"> (55, 90, 80) </span> | other | Uncategorized elements | | 4 | 11 | <span style="color:rgb(220, 20, 60)"> (220, 20, 60) </span> | pedestrian | Humans that walk | | 5 | 5 | <span style="color:rgb(153, 153, 153)"> (153, 153, 153) </span> | pole | Vertically oriented pole and its horizontal components if any | | 6 | 16 | <span style="color:rgb(157, 234, 50)"> (157, 234, 50) </span> | roadline | Markings on road | | 7 | 0 | <span style="color:rgb(128, 64, 128)"> (128, 64, 128) </span> | road | Lanes, streets, paved areas on which cars drive | | 8 | 1 | <span style="color:rgb(244, 35, 232)"> (244, 35, 232) </span> | sidewalk | Parts of ground designated for pedestrians or cyclists | | 9 | 8 | <span style="color:rgb(107, 142, 35)"> (107, 142, 35) </span> | vegetation | Trees, hedges, all kinds of vertical vegetation (ground-level vegetation is not included here) | | 10 | 13 | <span style="color:rgb(0, 0, 142)"> (0, 0, 142) </span> | cars | Cars in scene | | 11 | 3 | <span style="color:rgb(102, 102, 156)"> (102, 102, 156) </span> | wall | Individual standing walls, not part of buildings | | 12 | 7 | <span style="color:rgb(220, 220, 0)"> (220, 220, 0) </span> | traffic sign | Signs installed by the state/city authority, usually for traffic regulation | | 13 | 10 | <span style="color:rgb(70, 130, 180)"> (70, 130, 180) </span> | sky | Open sky, including clouds and sun | | 14 | -1 | <span style="color:rgb(81, 0, 81)"> (81, 0, 81) </span> | ground | Any horizontal ground-level structures that do not match any other category | | 15 | -1 | <span style="color:rgb(150, 100, 100)"> (150, 100, 100) </span> | bridge | The structure of the bridge | | 16 | -1 | <span style="color:rgb(230, 150, 140)"> (230, 150, 140) </span> | railtrack | Rail tracks that are non-drivable by cars | | 17 | -1 | <span style="color:rgb(180, 165, 180)"> (180, 165, 180) </span> | guardrail | Guard rails / crash barriers | | 18 | 6 | <span style="color:rgb(250, 170, 30)"> (250, 170, 30) </span> | traffic light | Traffic light boxes without their poles | | 19 | -1 | <span style="color:rgb(110, 190, 160)"> (110, 190, 160) </span> | static | Elements in the scene and props that are immovable | | 20 | -1 | <span style="color:rgb(170, 120, 50)"> (170, 120, 50) </span> | dynamic | Elements whose position is susceptible to change over time | | 21 | 19 | <span style="color:rgb(45, 60, 150)"> (45, 60, 150) </span> | water | Horizontal water surfaces | | 22 | 9 | <span style="color:rgb(152, 251, 152)"> (152, 251, 152) </span> | terrain | Grass, ground-level vegetation, soil, or sand | | 23 | 12 | <span style="color:rgb(255, 0, 0)"> (255, 0, 0) </span> | rider | Humans that ride/drive any kind of vehicle or mobility system | | 24 | 18 | <span style="color:rgb(119, 11, 32)"> (119, 11, 32) </span> | bicycle | Bicycles in scenes | | 25 | 17 | <span style="color:rgb(0, 0, 230)"> (0, 0, 230) </span> | motorcycle | Motorcycles in scene | | 26 | 15 | <span style="color:rgb(0, 60, 100)"> (0, 60, 100) </span> | bus | Buses in scenes | | 27 | 14 | <span style="color:rgb(0, 0, 70)"> (0, 0, 70) </span> | truck | Trucks in scenes | | </details> ## Dataset Structure The dataset is organized in the following structure: <!--<details> <summary><strong>Images (RGB Images)</strong></summary> - ***H_15_P_0*** - *ClearNoon* - Town01.tar.gz - Town02.tar.gz - ... - Town10HD.tar.gz - *ClearSunset* - Town01.tar.gz - Town02.tar.gz - ... - Town10HD.tar.gz - *ClearNight* - Town01.tar.gz - Town02.tar.gz - ... - Town10HD.tar.gz - *CloudyNoon* - Town01.tar.gz - Town02.tar.gz - ... - Town10HD.tar.gz - *MidRainyNoon* - Town01.tar.gz - Town02.tar.gz - ... - Town10HD.tar.gz - ***H_15_P_45*** - ... - ... - ***H_60_P_90*** - ... </details> <details> <summary><strong>Instance (Instance Segmentation Annotations)</strong></summary> - ***H_35_P_45*** - *ClearNoon* - Town01.tar.gz - Town02.tar.gz - ... - Town10HD.tar.gz </details> <details> <summary><strong>Segment (Semantic Segmentation Annotations)</strong></summary> - ***H_15_P_0*** - *ClearNoon* - Town01.tar.gz - Town02.tar.gz - ... - Town10HD.tar.gz - ***H_15_P_45*** - ... - ... - ***H_60_P_90*** </details> <details> <summary><strong>Depth (Depth Annotations)</strong></summary> - ***H_35_P_45*** - *ClearNoon* - Town01.tar.gz - Town02.tar.gz - ... - Town10HD.tar.gz </details> --> ``` ├── Images (RGB Images) │ ├── H_15_P_0 │ │ ├── ClearNoon │ │ │ ├── Town01 │ │ │ │ └── Town01.tar.gz │ │ │ ├── Town02 │ │ │ │ └── Town02.tar.gz │ │ │ ├── ... │ │ │ └── Town10HD │ │ │ └── Town10HD.tar.gz │ │ ├── ClearSunset │ │ │ ├── Town01 │ │ │ │ └── Town01.tar.gz │ │ │ ├── Town02 │ │ │ │ └── Town02.tar.gz │ │ │ ├── ... │ │ │ └── Town10HD │ │ │ └── Town10HD.tar.gz │ │ ├── ClearNight │ │ │ ├── Town01 │ │ │ │ └── Town01.tar.gz │ │ │ ├── Town02 │ │ │ │ └── Town02.tar.gz │ │ │ ├── ... │ │ │ └── Town10HD │ │ │ └── Town10HD.tar.gz │ │ ├── CloudyNoon │ │ │ ├── Town01 │ │ │ │ └── Town01.tar.gz │ │ │ ├── Town02 │ │ │ │ └── Town02.tar.gz │ │ │ ├── ... │ │ │ └── Town10HD │ │ │ └── Town10HD.tar.gz │ │ └── MidRainyNoon │ │ ├── Town01 │ │ │ └── Town01.tar.gz │ │ ├── Town02 │ │ │ └── Town02.tar.gz │ │ ├── ... │ │ └── Town10HD │ │ └── Town10HD.tar.gz │ ├── H_15_P_45 │ │ └── ... │ ├── ... │ └── H_60_P_90 │ └── ... ├── Instance (Instance Segmentation Annotations) │ ├── H_35_P_45 │ │ └── ClearNoon │ │ ├── Town01 │ │ │ └── Town01.tar.gz │ │ ├── Town02 │ │ │ └── Town02.tar.gz │ │ ├── ... │ │ └── Town10HD │ │ └── Town10HD.tar.gz │ └── ... ├── Segment (Semantic Segmentation Annotations) │ ├── H_15_P_0 │ │ ├── ClearNoon │ │ │ ├── Town01 │ │ │ │ └── Town01.tar.gz │ │ │ ├── Town02 │ │ │ │ └── Town02.tar.gz │ │ │ ├── ... │ │ │ └── Town10HD │ │ │ └── Town10HD.tar.gz │ │ ├── H_15_P_45 │ │ │ └── ... │ │ ├── ... │ │ └── H_60_P_90 │ │ └── ... │ └── ... └── Depth (Depth Annotations) ├── H_35_P_45 │ └── ClearNoon │ ├── Town01 │ │ └── Town01.tar.gz │ ├── Town02 │ │ └── Town02.tar.gz │ ├── ... │ └── Town10HD │ └── Town10HD.tar.gz └── ... ``` **Note**: Since the same viewpoint is reproduced across each weather variation, hence ClearNoon annotations can be used for all images pertaining to the different weather variations. ## Dataset Download The dataset can be downloaded using both [datasets](https://huggingface.co/docs/datasets/index) library by Hugging Face and wget. Since SkyScenes offers variations across different axes we enable different subsets for download that can aid in model sensitivity analysis across these axes. ### Download instructions: wget **Example script for downloading different subsets of data using wget** ```bash #!/bin/bash #Change here to download a specific Height and Pitch Variation, for example - H_15_P_0 #Note- Currently supporting only H_35_P_45 for Instance and Depth Maps # HP=('H_15_P_45' 'H_15_P_60' 'H_15_P_90') HP=('H_15_P_0' 'H_15_P_45' 'H_15_P_60' 'H_15_P_90' 'H_35_P_0' 'H_35_P_45' 'H_35_P_60' 'H_35_P_90' 'H_60_P_0' 'H_60_P_45' 'H_60_P_60' 'H_60_P_90') #Change here to download a specific weather subset, for example - ClearNoon #Note - For Segment, Instance and Depth annotations this field should only have ClearNoon variation # weather=('ClearNoon' 'ClearNight') weather=('ClearNoon' 'ClearNight' 'ClearSunset' 'CloudyNoon' 'MidRainyNoon') #Change here to download a specific Town subset, for example - Town07 layout=('Town01' 'Town02' 'Town03' 'Town04' 'Town05' 'Town06' 'Town07' 'Town10HD') #Change here for any specific annotation, for example - https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Segment base_url=('https://huggingface.co/datasets/hoffman-lab/SkyScenes/resolve/main/Images') #Change here for base download folder base_download_folder='SkyScenes' for hp in "${HP[@]}"; do for w in "${weather[@]}"; do for t in "${layout[@]}"; do folder=$(echo "$base_url" | awk -F '/' '{print $(NF)}') download_url="${base_url}/${hp}/${w}/${t}/${t}.tar.gz" download_folder="${base_download_folder}/${folder}/${hp}/${w}/${t}" mkdir -p "$download_folder" echo "Downloading: $download_url" wget -P "$download_folder" "$download_url" done done done ``` ### Download instructions: [datasets](https://huggingface.co/docs/datasets/index) <details> <summary>Click to view all the available keys for downloading subsets of the data</summary> * **Layout Variations** - Rural - Urban * **Weather Variations** - ClearNoon - ClearNight (only images) - ClearSunset (only images) - CloudyNoon (only images) - MidRainyNoon (only images) * **Height Variations** - H_15 - H_35 - H_60 * **Pitch Variations** - P_0 - P_45 - P_60 - P_90 * **Height and Pitch Variations** - H_15_P_0 - H_15_P_45 - H_15_P_60 - H_15_P_90 - H_35_P_0 - H_35_P_45 - H_35_P_60 - H_35_P_90 - H_60_P_0 - H_60_P_45 - H_60_P_60 - H_60_P_90 Full dataset key: full **💡Notes**: - To download **images** append subset key with **images**, example - ```H_35_P_45 images``` - To download **semantic segmentation** maps append subset key with **semseg**, example - ```H_35_P_45 semseg``` - To download **instance segmentation** maps append subset key with **instance**, example - ```H_35_P_45 instance``` - To download **depth** maps append subset key with **depth**, example - ```H_35_P_45 depth``` </details> **Example script for loading H_35_P_45 images** ```python from datasets import load_dataset dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 images") ``` **Example script for loading H_35_P_45 semantic segmentation maps** ```python from datasets import load_dataset dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 semseg") ``` **Example script for loading H_35_P_45 instance segmentation maps** ```python from datasets import load_dataset dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 instance") ``` **Example script for loading H_35_P_45 depth maps** ```python from datasets import load_dataset dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 depth") ``` ### 💡 Notes - Depth and Instance segmentation maps are available for only H_35_P_45, other variations will be made available soon. - To prevent issues when loading datasets using [datasets](https://huggingface.co/docs/datasets/index) library, it is recommended to avoid downloading subsets that contain overlapping directories. If there are any overlapping directories between the existing downloads and new ones, it's essential to clear the .cache directory of any such overlaps before proceeding with the new downloads. This step will ensure a clean and conflict-free environment for handling datasets. ## BibTex If you find this work useful please like ❤️ our dataset repo and cite 📄 our paper. Thanks for your support! ``` @misc{khose2023skyscenes, title={SkyScenes: A Synthetic Dataset for Aerial Scene Understanding}, author={Sahil Khose and Anisha Pal and Aayushi Agarwal and Deepanshi and Judy Hoffman and Prithvijit Chattopadhyay}, year={2023}, eprint={2312.06719}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
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tyouisen/aclue
tyouisen
2023-12-12T06:07:10Z
146
2
[ "task_categories:multiple-choice", "task_categories:question-answering", "size_categories:1M<n<10M", "language:zh", "license:cc-by-nc-4.0", "llm", "Ancient Chinese", "Evaluation", "chinese", "arxiv:2310.0955", "region:us" ]
[ "multiple-choice", "question-answering" ]
2023-08-16T14:14:21Z
--- license: cc-by-nc-4.0 task_categories: - multiple-choice - question-answering language: - zh tags: - llm - Ancient Chinese - Evaluation - chinese pretty_name: ACLUE size_categories: - 1M<n<10M --- # Dataset Card for ACLUE - **Homepage:** [https://github.com/isen-zhang/ACLUE](https://github.com/isen-zhang/ACLUE) - **Repository:** [https://huggingface.co/datasets/tyouisen/aclue](https://huggingface.co/datasets/tyouisen/aclue) - **Paper:** [https://arxiv.org/abs/2310.0955](https://arxiv.org/abs/2310.0955) - **Leaderboard:** [https://github.com/isen-zhang/ACLUE](https://github.com/isen-zhang/ACLUE) ### 简介 (Introduction) Ancient Chinese Language Understanding Evaluation (ACLUE) 是一个面向古代汉语的评估基准,旨在帮助评估大型语言模型在古代汉语上的表现。 The Ancient Chinese Language Understanding Evaluation (ACLUE) is an evaluation benchmark focused on ancient Chinese language comprehension. It aims to assess the performance of large-scale language models (LLMs) on understanding ancient Chinese. ### 数据 (Data) 该基准测试包含15个任务,涵盖了各个领域,包括词汇、句法、语义、推理和知识。我们为这15个任务提供了开发集和测试集数据,开发集中有5个问题,而测试集中则有100多个问题。我们鼓励研究人员使用ACLUE来测试和提升其模型在古代汉语语言理解方面的能力。ACLUE的任务取自人工挑选的公开资源和自动生成的古代汉语语料库。这些问题涵盖了从夏朝(公元前2070年)到明朝(公元1368年)的广泛时间范围。ACLUE对所有任务都采用了多项选择题的形式。 The benchmark comprises 15 tasks spanning various domains, including lexical, syntactic, semantic, inference, and knowledge. We provide development and test dataset for each of 15 tasks, with 5 questions in development set and 100+ quesitons in test set. We encourage researchers to use ACLUE to test and enhance their models' abilities in ancient Chinese language understanding. ACLUE's tasks are derived from a combination of manually curated questions from publicly available resources, and automatic generated questions from classical Chinese language corpora. The range of questions span from the Xia dynasty (2070 BCE) to the Ming dynasty (1368 CE). ACLUE employs a multiple-choice question format for all tasks. ### 数据实例( Data Instances) 数据集中的每个问题都是一个包含4个选项的多项选择题,其中只有一个选项是正确答案。以下是两个示例: Each question in the dataset is a multiple-choice questions with 4 choices and only one choice as the correct answer. Here are two examples: ``` 以下是关于{古诗词曲鉴赏}的单项选择题,请直接给出正确答案的选项。 题目:《木兰诗--北朝民歌》唧唧复唧唧,木兰当户织。不闻机杼声,唯闻女叹息。问女何所思,问女何所忆。女亦无所思,女亦无所忆。昨夜见军帖,可汗大点兵,军书十二卷,卷卷有爷名。阿爷无大儿,木兰无长兄,愿为市鞍马,从此替爷征。东市买骏马,西市买鞍鞯,南市买辔头,北市买长鞭。旦辞爷娘去,暮宿黄河边,不闻爷娘唤女声,但闻黄河流水鸣溅溅。旦辞黄河去,暮至黑山头,不闻爷娘唤女声,但闻燕山胡骑鸣啾啾。万里赴戎机,关山度若飞。朔气传金柝,寒光照铁衣。将军百战死,壮士十年归。归来见天子,天子坐明堂。策勋十二转,赏赐百千强。可汗问所欲,木兰不用尚书郎,愿驰千里足,送儿还故乡。爷娘闻女来,出郭相扶将;阿姊闻妹来,当户理红妆;小弟闻姊来,磨刀霍霍向猪羊。开我东阁门,坐我西阁床。脱我战时袍,著我旧时裳。当窗理云鬓,对镜帖花黄。出门看火伴,火伴皆惊忙:同行十二年,不知木兰是女郎。雄兔脚扑朔,雌兔眼迷离;双兔傍地走,安能辨我是雄雌?下列对这首诗的理解和分析,不正确的一项是 () A. 《木兰诗》是南北朝时期的一首长篇叙事民歌,风格刚健质朴。全诗以“木兰是女郎”来构思木兰的传奇故事,富有浪漫色彩。 B. “愿为市鞍马”的“市”是“市场”的意思,“万里赴戎机”的“戎机”是“战事”的意思。 C. 木兰“不用尚书郎”而愿“还故乡”固然有对家乡的眷恋,但也有自己女儿身秘密的因素。 D. “朔气传金柝,寒光照铁衣”运用对偶手法,描写了木兰在边塞艰苦的军旅生活。 答案是:B ``` ``` 题目:《虞美人》李煜。春花秋月何时了?往事知多少。小楼昨夜又东风,故国不堪回首月明中。雕栏玉砌应犹在,只是朱颜改。问君能有几多愁?恰似一江春水向东流。对《虞美人》的赏析,不恰当的一项是() A. 词作从眼前景物入手,生发联想和想像,追怀昔日帝王生活,描摹了一幅幅鲜活的画面,隐晦地表达出叛逆之情,惹恼了宋太宗,铸成了词人悲惨结局。 B. 词作以实虚相间的手法来绘景、抒情、达意,忽而写眼前,忽而写想像。 C. 《虞美人》乃李煜绝笔词 D. 《虞美人》以其形式别致给人美感愉悦。 答案是: ``` 以下列出了任务的类别、实例数量、问题平均长度以及任务的来源: The category, number of instances, average length of the question, and origin of the tasks are provided below: | Task | Total Q. | Avg. len |Task (zh) | Category | Origin | |-------------------------------|------|------|-----------------------------------|----------|-----------| | Named entity recognition | 500 | 138 | 古汉语命名体识别 | lexical | generated | | Polysemy resolution | 500 | 116 | 古文单字多义 | lexical | generated | | Homographic character resolution | 500 | 137 | 通假字 | lexical | generated | | Sentence segmentation | 500 | 210 | 古文断句 | syntactic| generated | | Couplet prediction | 500 | 62 | 对联预测 | semantic | generated | | Poetry context prediction | 500 | 77 | 古诗词上下句预测 | semantic | generated | | Poetry sentiment analysis | 500 | 60 | 诗词情感分类 | inference| generated | | Poem quality estimation | 406 | 118 | 古诗词质量评估 | inference| generated | | Ancient Chinese medical | 211 | 38 | 医古文 | knowledge| collected | | Ancient Chinese literature | 160 | 44 | 古代文学知识 | knowledge| collected | | Traditional Chinese culture | 136 | 59 | 国学常识 | knowledge| collected | | Poetry appreciation | 103 | 258 | 古诗词曲鉴赏 | inference| collected | | Basic ancient Chinese | 249 | 52 | 基础古汉语知识 | knowledge| collected | | Reading comprehension | 101 | 982 | 古文阅读理解 | inference| collected | | Ancient Chinese phonetics | 101 | 50 | 古音学 | knowledge| collected | #### 加载数据 (Load data) ```python task_list = ['polysemy_resolution', 'poetry_sentiment_analysis', 'named_entity_recognition', 'basic_ancient_chinese', 'poetry_context_prediction', 'sentence_segmentation', 'couplet_prediction', 'poetry_appreciate', 'ancient_chinese_culture', 'ancient_phonetics', 'homographic_character_resolution', 'ancient_literature', 'ancient_medical', 'poetry_quality_assessment', 'reading_comprehension'] from datasets import load_dataset dataset = {k: load_dataset(r"tyouisen/aclue", k) for k in task_list} # Print an example: print(dataset['polysemy_resolution']['test'][0]) # Or download specific dataset: dataset = load_dataset("tyouisen/aclue", "couplet_prediction", split="test") # or split = "dev" ``` ### 引用 (Citation) ``` Zhang, Yixuan, and Haonan Li. "Can Large Langauge Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE." Proceedings of the Ancient Language Processing Workshop. 2023. ``` ### 许可证 (License) ACLUE数据集采用:(The ACLUE dataset is licensed under a:) [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
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seonglae/nq_open-validation
seonglae
2023-12-14T10:23:53Z
140
0
[ "region:us" ]
null
2023-11-15T18:04:11Z
--- dataset_info: - config_name: psgs_w100.dpr_nq.10_dpr-reader-single-nq-base features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: 'null' - name: predicted dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 22461013 num_examples: 3610 download_size: 13069117 dataset_size: 22461013 - config_name: psgs_w100.dpr_nq.10_lsg-bart-base-4096-booksum.1_dpr-reader-single-nq-base features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: string - name: predicted dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 29791480 num_examples: 3610 download_size: 15853782 dataset_size: 29791480 - config_name: psgs_w100.dpr_nq.10_resrer-bart-base.1_dpr-reader-single-nq-base features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: string - name: predicted dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 28798929 num_examples: 3610 download_size: 17159760 dataset_size: 28798929 - config_name: psgs_w100.dpr_nq.10_resrer-bart-base.1_gpt-3.5-turbo features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: string - name: predicted dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 28807451 num_examples: 3610 download_size: 17139973 dataset_size: 28807451 - config_name: psgs_w100.dpr_nq.10_resrer-bart.1_dpr-reader-single-nq-base features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: string - name: predicted dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 28152955 num_examples: 3610 download_size: 16764384 dataset_size: 28152955 - config_name: psgs_w100.dpr_nq.10_resrer-pegasus-x.1_dpr-reader-single-nq-base features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: string - name: predicted dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 30337257 num_examples: 3610 download_size: 17579283 dataset_size: 30337257 - config_name: psgs_w100.dpr_nq.1_dpr-reader-single-nq-base features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: 'null' - name: predicted dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 2611003 num_examples: 3610 download_size: 1750992 dataset_size: 2611003 - config_name: psgs_w100.dpr_nq.1_gpt-3.5-turbo features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: 'null' - name: predicted dtype: string - name: score dtype: int64 splits: - name: train num_bytes: 2628010 num_examples: 3610 download_size: 1732708 dataset_size: 2628010 - config_name: psgs_w100.dpr_nq.4_resrer-bart-sep.1_dpr-reader-single-nq-base features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: string - name: predicted dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 14905001 num_examples: 3610 download_size: 9271126 dataset_size: 14905001 - config_name: psgs_w100.dpr_nq.4_resrer-bart.1_dpr-reader-single-nq-base features: - name: question dtype: string - name: answer sequence: string - name: retrieved dtype: string - name: summary dtype: string - name: predicted dtype: string - name: score dtype: float64 splits: - name: train num_bytes: 14523768 num_examples: 3610 download_size: 9011695 dataset_size: 14523768 configs: - config_name: psgs_w100.dpr_nq.10_dpr-reader-single-nq-base data_files: - split: train path: psgs_w100.dpr_nq.10_dpr-reader-single-nq-base/train-* - config_name: psgs_w100.dpr_nq.10_lsg-bart-base-4096-booksum.1_dpr-reader-single-nq-base data_files: - split: train path: psgs_w100.dpr_nq.10_lsg-bart-base-4096-booksum.1_dpr-reader-single-nq-base/train-* - config_name: psgs_w100.dpr_nq.10_resrer-bart-base.1_dpr-reader-single-nq-base data_files: - split: train path: psgs_w100.dpr_nq.10_resrer-bart-base.1_dpr-reader-single-nq-base/train-* - config_name: psgs_w100.dpr_nq.10_resrer-bart-base.1_gpt-3.5-turbo data_files: - split: train path: psgs_w100.dpr_nq.10_resrer-bart-base.1_gpt-3.5-turbo/train-* - config_name: psgs_w100.dpr_nq.10_resrer-bart.1_dpr-reader-single-nq-base data_files: - split: train path: psgs_w100.dpr_nq.10_resrer-bart.1_dpr-reader-single-nq-base/train-* - config_name: psgs_w100.dpr_nq.10_resrer-pegasus-x.1_dpr-reader-single-nq-base data_files: - split: train path: psgs_w100.dpr_nq.10_resrer-pegasus-x.1_dpr-reader-single-nq-base/train-* - config_name: psgs_w100.dpr_nq.1_dpr-reader-single-nq-base data_files: - split: train path: psgs_w100.dpr_nq.1_dpr-reader-single-nq-base/train-* - config_name: psgs_w100.dpr_nq.1_gpt-3.5-turbo data_files: - split: train path: psgs_w100.dpr_nq.1_gpt-3.5-turbo/train-* - config_name: psgs_w100.dpr_nq.4_resrer-bart-sep.1_dpr-reader-single-nq-base data_files: - split: train path: psgs_w100.dpr_nq.4_resrer-bart-sep.1_dpr-reader-single-nq-base/train-* - config_name: psgs_w100.dpr_nq.4_resrer-bart.1_dpr-reader-single-nq-base data_files: - split: train path: psgs_w100.dpr_nq.4_resrer-bart.1_dpr-reader-single-nq-base/train-* --- # Dataset Card for "nq_open-validation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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svenschultze/artificial-vs-natural
svenschultze
2023-12-12T11:13:22Z
137
0
[ "region:us" ]
null
2023-12-07T15:03:10Z
--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string - name: margin dtype: int64 splits: - name: train num_bytes: 26691782.61397505 num_examples: 8153 - name: test num_bytes: 2966117.3860249477 num_examples: 906 download_size: 15568743 dataset_size: 29657900.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
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SonishMaharjan/asr
SonishMaharjan
2023-12-14T03:21:11Z
136
0
[ "license:unknown", "region:us" ]
null
2023-12-13T09:08:29Z
--- license: unknown ---
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Antreas/TALI
Antreas
2023-12-13T09:02:28Z
130
1
[ "task_categories:zero-shot-classification", "size_categories:1M<n<10M", "license:cc-by-4.0", "video", "audio", "text", "image", "tetramodal", "multimodal", "youtube", "wikipedia", "region:us" ]
[ "zero-shot-classification" ]
2023-08-16T22:59:13Z
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: image_url dtype: string - name: item_idx dtype: int64 - name: wit_features struct: - name: attribution_passes_lang_id sequence: bool - name: caption_alt_text_description sequence: string - name: caption_reference_description sequence: string - name: caption_title_and_reference_description sequence: string - name: context_page_description sequence: string - name: context_section_description sequence: string - name: hierarchical_section_title sequence: string - name: is_main_image sequence: bool - name: language sequence: string - name: page_changed_recently sequence: bool - name: page_title sequence: string - name: page_url sequence: string - name: section_title sequence: string - name: wit_idx dtype: int64 - name: youtube_title_text dtype: string - name: youtube_description_text dtype: string - name: youtube_video_content dtype: binary - name: youtube_video_starting_time dtype: string - name: youtube_subtitle_text dtype: string - name: youtube_video_size dtype: int64 - name: youtube_video_file_path dtype: string splits: - name: train num_bytes: 1902638101655.625 num_examples: 1052915 - name: val num_bytes: 104485442867.25 num_examples: 57958 - name: test num_bytes: 111107332347.375 num_examples: 61389 download_size: 2058391040534 dataset_size: 2118230876870.25 license: cc-by-4.0 task_categories: - zero-shot-classification tags: - video - audio - text - image - tetramodal - multimodal - youtube - wikipedia pretty_name: TALI size_categories: - 1M<n<10M --- # Dataset Card for "TALI" ## Table of Contents 1. Dataset Description 1. Abstract 2. Brief Description 2. Dataset Information 1. Modalities 2. Dataset Variants 3. Dataset Statistics 4. Data Fields 5. Data Splits 3. Dataset Creation 4. Dataset Use 5. Additional Information ## Dataset Description ### Abstract TALI is a large-scale, tetramodal dataset designed to facilitate a shift from unimodal and duomodal to tetramodal research in deep learning. It aligns text, video, images, and audio, providing a rich resource for innovative self-supervised learning tasks and multimodal research. TALI enables exploration of how different modalities and data/model scaling affect downstream performance, with the aim of inspiring diverse research ideas and enhancing understanding of model capabilities and robustness in deep learning. ### Brief Description TALI (Temporally and semantically Aligned Audio, Language and Images) is a dataset that uses the Wikipedia Image Text (WIT) captions and article titles to search Youtube for videos that match the captions. It then downloads the video, audio, and subtitles from these videos. The result is a rich multimodal dataset that has multiple caption types related to both the WiT Images, and the Youtube videos. This enables learning to take place between either temporally or semantically aligned text, images, audio and video. ## Dataset Information ### Modalities The TALI dataset consists of the following modalities: 1. Image: 1. Wikipedia caption image 2. Randomly sampled image from youtube video 2. Text 1. Wikipedia Caption Text 2. Wikipedia Title Text 3. Wikipedia Main Body Text 4. YouTube Subtitle Text 5. YouTube Description Text 6. YouTube Title Text 3. Audio 1. YouTube Content Audio 4. Video 1. YouTube Content Video ## Usage: To get started with TALI, you can load the dataset via Hugging Face's `datasets` library through our helper functions. The reason we don't use `datasets` directly is because we found huggingface_hub downloads much faster and reliable. For a full set of possible configurations look at [examples.py](examples.py). Here's a basic usage example: First install the tali package: ### Installation For the default install use: ```bash pip install git+https://github.com/AntreasAntoniou/TALI ``` For the dev install use: ```bash pip install git+https://github.com/AntreasAntoniou/TALI[dev] ``` Then use the dataset using: ### Examples Import relevant helper functions ```python import pathlib from enum import Enum import torch from tqdm.auto import tqdm from tali.data import ( SubModalityTypes, TALIBaseTransform, TALIBaseTransformConfig, VideoFramesFormat, default_transforms, load_dataset_via_hub, ) ``` #### TALI with default transforms (CLIP and Whisper) and no streaming ```python def tali_with_transforms_no_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI" )["train"] ( image_transforms, text_transforms, audio_transforms, video_transforms, ) = default_transforms() preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=text_transforms, image_tokenizer=image_transforms, audio_tokenizer=audio_transforms, video_tokenizer=video_transforms, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with no transforms and no streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats ```python def tali_without_transforms_no_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI" )["train"] preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=None, image_tokenizer=None, audio_tokenizer=None, video_tokenizer=None, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with default transforms and streaming ```python def tali_with_transforms_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI", streaming=True )["train"] ( image_transforms, text_transforms, audio_transforms, video_transforms, ) = default_transforms() preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=text_transforms, image_tokenizer=image_transforms, audio_tokenizer=audio_transforms, video_tokenizer=video_transforms, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` #### TALI with no transforms and streaming, returning text as text, images as PIL images, videos as a list of PIL images, and audio as a sequence of floats ```python def tali_without_transforms_streaming( dataset_storage_path: pathlib.Path | str, ): if isinstance(dataset_storage_path, str): dataset_storage_path = pathlib.Path(dataset_storage_path) dataset = load_dataset_via_hub( dataset_storage_path, dataset_name="Antreas/TALI", streaming=True )["train"] preprocessing_transform = TALIBaseTransform( cache_dir=dataset_storage_path / "cache", text_tokenizer=None, image_tokenizer=None, audio_tokenizer=None, video_tokenizer=None, config=TALIBaseTransformConfig( root_filepath=dataset_storage_path, modality_list=[ SubModalityTypes.youtube_content_video, SubModalityTypes.youtube_content_audio, SubModalityTypes.youtube_random_video_frame, SubModalityTypes.youtube_subtitle_text, SubModalityTypes.youtube_description_text, SubModalityTypes.youtube_title_text, SubModalityTypes.wikipedia_caption_image, SubModalityTypes.wikipedia_caption_text, SubModalityTypes.wikipedia_main_body_text, SubModalityTypes.wikipedia_title_text, ], video_frames_format=VideoFramesFormat.PIL, ), ) for sample in tqdm(dataset): sample = preprocessing_transform(sample) print(list(sample.keys())) for key, value in sample.items(): if hasattr(value, "shape"): print(key, value.shape) elif isinstance(value, torch.Tensor): print(key, value.shape) elif hasattr(value, "__len__"): print(key, len(value)) print(key, type(value)) break ``` ### Dataset Statistics TBA ## Dataset Creation The TALI dataset was created by starting from the WiT dataset and using either the context_page_description or page_title as a source-query to search YouTube for video that were creative commons opted-in, and, not age restricted. The top 100 result titles were returned and compared with the source-query using the CLIP text embeddings of the largest CLIP model available. The top-1 title’s video based on the CLIP ranking was chosen and downloaded. The video was broken into 30-second segments and the top-10 segments for eachvideo were chosen based on the distance between the CLIP image embedding of the first image of each segment and the video’s title text. The image, audio, and subtitle frames were extracted from these segments. At sampling time, one of these 10 segments is randomly selected, and a 10-second segment is chosen out of the 30-second clip. The result is 200 video frames (spread throughout the 10-second segment), and 160000 audio frames (10 seconds). ## Dataset Use TALI is designed for use in a wide range of multimodal research tasks, including but not limited to: - Multimodal understanding and reasoning - Self-supervised learning - Multimodal alignment and translation - Multimodal summarization - Multimodal question answering ## Dataset Curators: Antreas Antoniou Citation Information: TBA Contributions: Thanks to all contributors including data curators, annotators, and software developers. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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nesuri/sorsolingo-tts-bsl
nesuri
2023-12-17T13:57:19Z
129
0
[ "region:us" ]
null
2023-12-07T14:37:55Z
--- dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 82617979.0 num_examples: 153 download_size: 79264687 dataset_size: 82617979.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
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ddellapietra/test1
ddellapietra
2023-12-17T00:06:28Z
129
0
[ "region:us" ]
null
2023-12-16T21:49:17Z
--- dataset_info: features: - name: formatted_prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 20786720 num_examples: 1000 download_size: 8512059 dataset_size: 20786720 configs: - config_name: default data_files: - split: train path: data/train-* ---
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Pipper/sol_processed_s2s
Pipper
2023-12-12T09:57:39Z
127
0
[ "region:us" ]
null
2023-11-16T09:41:34Z
--- dataset_info: features: - name: file_name dtype: string - name: comments dtype: string - name: code_string dtype: string - name: __index_level_0__ dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 48324923815 num_examples: 1223464 - name: test num_bytes: 2669251521 num_examples: 67971 - name: valid num_bytes: 2660994665 num_examples: 67970 download_size: 11579574597 dataset_size: 53655170001 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "sol_processed_s2s" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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librarian-bots/model_cards_with_metadata_with_embeddings
librarian-bots
2023-12-11T10:06:20Z
127
5
[ "task_categories:text-retrieval", "size_categories:100K<n<1M", "region:us" ]
[ "text-retrieval" ]
2023-11-30T18:37:44Z
--- size_categories: - 100K<n<1M task_categories: - text-retrieval pretty_name: Model Card dataset_info: features: - name: modelId dtype: string - name: author dtype: string - name: last_modified dtype: timestamp[us, tz=UTC] - name: downloads dtype: int64 - name: likes dtype: int64 - name: library_name dtype: string - name: tags sequence: string - name: pipeline_tag dtype: string - name: createdAt dtype: timestamp[us, tz=UTC] - name: card dtype: string - name: embedding sequence: float32 splits: - name: train num_bytes: 2043696915.96232 num_examples: 432043 download_size: 1200724193 dataset_size: 2043696915.96232 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Hugging Face Hub Model Cards with Embeddings This dataset consists of [model cards](https://huggingface.co/docs/hub/model-cards) for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub. This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new [discussion](https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/discussions/new). This dataset is the same as the [Hugging Face Hub Model Cards](https://huggingface.co/datasets/librarian-bots/model_cards) dataset but with the addition of embeddings for each model card. The embeddings are generated using the [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) model. ## Dataset Details ### Dataset Description - **Curated by:** Daniel van Strien - **Language(s) (NLP):** Model cards on the Hugging Face Hub are predominantly in English but may include other languages. ## Uses There are a number of potential uses for this dataset including: - text mining to find common themes in model cards - analysis of the model card format/content - topic modelling of model cards - analysis of the model card metadata - training language models on model cards - build a recommender system for model cards - build a search engine for model cards ### Out-of-Scope Use [More Information Needed] ## Dataset Structure This dataset has a single split. ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> The dataset was created to assist people in working with model cards. In particular. it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format. ### Source Data The source data is `README.md` files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory. #### 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. --> The data is downloaded using a CRON job on a daily basis. #### Who are the source data producers? The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API. ### Annotations [optional] There are no additional annotations in this dataset beyond the model card content. #### Annotation process N/A #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> N/A #### 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. --> We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias. Whilst we do not directly download any images linked to the model cards, some model cards may include images. Some of these images may not be suitable for all audiences. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> ## Citation No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page. ## Dataset Card Authors [@davanstrien](https://huggingface.co/davanstrien) ## Dataset Card Contact [@davanstrien](https://huggingface.co/davanstrien)
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librarian-bots/dataset_cards_with_metadata
librarian-bots
2023-12-17T01:45:28Z
123
6
[ "task_categories:text-retrieval", "size_categories:10K<n<100K", "ethics", "documentation", "region:us" ]
[ "text-retrieval" ]
2023-10-11T09:15:10Z
--- size_categories: - 10K<n<100K task_categories: - text-retrieval dataset_info: features: - name: datasetId dtype: string - name: author dtype: string - name: last_modified dtype: timestamp[us, tz=UTC] - name: downloads dtype: int64 - name: likes dtype: int64 - name: tags sequence: string - name: task_categories sequence: string - name: createdAt dtype: timestamp[us, tz=UTC] - name: card dtype: string splits: - name: train num_bytes: 214327992 num_examples: 57583 download_size: 53399171 dataset_size: 214327992 configs: - config_name: default data_files: - split: train path: data/train-* tags: - ethics - documentation --- # Dataset Card for Hugging Face Hub Dataset Cards This datasets consists of [dataset cards](https://huggingface.co/docs/hub/datasets-cards) for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub. This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub. This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new [discussion](https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/discussions/new). ## Dataset Details ### Dataset Description - **Curated by:** Daniel van Strien - **Language(s) (NLP):** Dataset cards on the Hugging Face Hub are predominantly in English but may include other languages. ## Uses There are a number of potential uses for this dataset including: - text mining to find common themes in dataset cards - analysis of the dataset card format/content - topic modelling of dataset cards - training language models on the dataset cards ### Out-of-Scope Use [More Information Needed] ## Dataset Structure This dataset has a single split. ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format. ### Source Data The source data is `README.md` files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory. #### 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. --> The data is downloaded using a CRON job on a daily basis. #### Who are the source data producers? The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API. ### Annotations [optional] There are no additional annotations in this dataset beyond the dataset card content. #### Annotation process N/A #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> N/A #### 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. --> We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards. Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias. Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences. ### 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 No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page. ## Dataset Card Authors [@davanstrien](https://huggingface.co/davanstrien) ## Dataset Card Contact [@davanstrien](https://huggingface.co/davanstrien)
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binwang/InstructDS_datasets
binwang
2023-12-16T03:29:54Z
119
0
[ "license:cc-by-nc-4.0", "region:us" ]
null
2023-12-16T03:13:27Z
--- license: cc-by-nc-4.0 configs: - config_name: SAMSum data_files: - split: train path: "SAMSum/train.json" - split: validation path: "SAMSum/validation.json" - split: test path: "SAMSum/test.json" - config_name: SAMSum_QDS data_files: - split: train path: "SAMSum_QDS/train.json" - split: validation path: "SAMSum_QDS/validation.json" - split: test path: "SAMSum_QDS/test.json" - config_name: DialogSum data_files: - split: train path: "DialogSum/train.json" - split: validation path: "DialogSum/validation.json" - split: test path: "DialogSum/test.json" - config_name: DialogSum_QDS data_files: - split: train path: "DialogSum_QDS/train.json" - split: validation path: "DialogSum_QDS/validation.json" - split: test path: "DialogSum_QDS/test.json" - config_name: TODSum data_files: - split: train path: "TODSum/train.json" - split: validation path: "TODSum/validation.json" - split: test path: "TODSum/test.json" - config_name: TODSum_QDS data_files: - split: train path: "TODSum_QDS/train.json" - split: validation path: "TODSum_QDS/validation.json" - split: test path: "TODSum_QDS/test.json" - config_name: DREAM data_files: - split: train path: "DREAM/train.json" - split: validation path: "DREAM/validation.json" - split: test path: "DREAM/test.json" ---
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TimKoornstra/financial-tweets-sentiment
TimKoornstra
2023-12-15T18:20:16Z
117
2
[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:mit", "sentiment", "twitter", "finance", "crypto", "stocks", "tweet", "collection", "region:us" ]
[ "text-classification" ]
2023-12-09T17:03:27Z
--- language: - en license: mit size_categories: - 10K<n<100K task_categories: - text-classification pretty_name: Financial Tweets with Sentiment class dataset_info: features: - name: tweet dtype: string - name: sentiment dtype: class_label: names: '0': neutral '1': bullish '2': bearish - name: url dtype: string splits: - name: train num_bytes: 6848991 num_examples: 38091 download_size: 2648055 dataset_size: 6848991 configs: - config_name: default data_files: - split: train path: data/train-* tags: - sentiment - twitter - finance - crypto - stocks - tweet - collection --- # Financial Sentiment Analysis Dataset ## Overview This dataset is a comprehensive collection of tweets focused on financial topics, meticulously curated to assist in sentiment analysis in the domain of finance and stock markets. It serves as a valuable resource for training machine learning models to understand and predict sentiment trends based on social media discourse, particularly within the financial sector. ## Data Description The dataset comprises tweets related to financial markets, stocks, and economic discussions. Each tweet is labeled with a sentiment value, where '1' denotes a positive sentiment, '2' signifies a negative sentiment, and '0' indicates a neutral sentiment. The dataset has undergone thorough preprocessing, including sentiment mapping and the removal of duplicate entries, to ensure data quality and consistency. ### Dataset Structure - **Tweet**: The text of the tweet, providing insights into financial discussions. - **Sentiment**: A numerical label indicating the sentiment of the tweet (1 for bullish, 2 for bearish, and 0 for neutral). ## Dataset Size - **Bullish Sentiments**: 17,368 - **Bearish Sentiments**: 14,383 - **Neutral Sentiments**: 6,340 ## Sources This dataset is an amalgamation of data from various reputable sources, each contributing a unique perspective on financial sentiment: - [FIQA Sentiment Classification](https://huggingface.co/datasets/ChanceFocus/fiqa-sentiment-classification): A sentiment analysis dataset with 721 positive, 379 negative, and 11 neutral sentiments. - [Stock Market Tweets Data](https://ieee-dataport.org/open-access/stock-market-tweets-data): A collection of tweets with 523 positive, 420 neutral, and 341 negative sentiments. - [Stock Related Tweet Sentiment](https://www.kaggle.com/datasets/mattgilgo/stock-related-tweet-sentiment): A dataset featuring 5005 positive, 741 neutral, and 736 negative sentiments. - [Master Thesis Data](https://github.com/moritzwilksch/MasterThesis/tree/main): Includes 3711 positive, 2784 neutral, and 2167 negative sentiments. - [Twitter Stock Sentiment](https://github.com/poojathakoor/twitter-stock-sentiment): Comprises 702 positive, 595 negative, and 481 neutral sentiments. - [Crypto Sentiment](https://github.com/surge-ai/crypto-sentiment/tree/main): Sentiment data for cryptocurrency-related tweets with 296 positive and 256 negative sentiments. - [Stock Sentiment](https://github.com/surge-ai/stock-sentiment/tree/main): Sentiment analysis on stock-related tweets, including 327 positive and 173 negative sentiments. - [Stockmarket Sentiment Dataset](https://www.kaggle.com/datasets/yash612/stockmarket-sentiment-dataset): Features 3685 positive and 2106 negative sentiments. - [Twitter Financial News Sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment): Contains 2398 positive, 7744 negative, and 1789 neutral sentiments. ## Usage This dataset is ideal for training and evaluating machine learning models for sentiment analysis, especially those focused on understanding market trends and investor sentiment. It can be used for academic research, financial market analysis, and developing AI tools for financial institutions. ## Acknowledgments We extend our heartfelt gratitude to all the authors and contributors of the original datasets. Their efforts in data collection and curation have been pivotal in creating this comprehensive resource. ## License This dataset is made available under the MIT license, adhering to the licensing terms of the original datasets.
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bzantium/LongPerplexity
bzantium
2023-12-11T07:46:20Z
117
0
[ "language:en", "license:apache-2.0", "ppl", "region:us" ]
null
2023-12-11T07:34:25Z
--- license: apache-2.0 configs: - config_name: c4 data_files: - split: test path: c4.jsonl - config_name: arxiv data_files: - split: test path: arxiv.jsonl - config_name: github data_files: - split: test path: github.jsonl language: - en tags: - ppl ---
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Jiahuan/teach_edh
Jiahuan
2023-12-13T15:12:08Z
114
0
[ "region:us" ]
null
2023-11-28T19:57:13Z
--- dataset_info: features: - name: output dtype: string - name: input dtype: string splits: - name: train num_bytes: 3300795 num_examples: 9742 - name: validation num_bytes: 357450 num_examples: 1137 - name: test num_bytes: 1104161 num_examples: 3855 download_size: 1752273 dataset_size: 4762406 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
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agil/EUIPO_questions_enhanced
agil
2023-12-17T19:02:17Z
114
0
[ "region:us" ]
null
2023-12-12T12:40:33Z
--- dataset_info: features: - name: ID_oq dtype: int64 - name: text_oq dtype: string - name: text_gq dtype: string - name: equiv dtype: int64 splits: - name: train num_bytes: 360653.6 num_examples: 2316 - name: test num_bytes: 90163.4 num_examples: 579 download_size: 157976 dataset_size: 450817.0 --- # Dataset Card for "EUIPO_questions_enhanced" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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jxm/msmarco__gtr_base__dpr
jxm
2023-12-12T23:13:55Z
114
0
[ "region:us" ]
null
2023-12-12T23:13:30Z
--- dataset_info: features: - name: text dtype: string - name: embeddings_A sequence: float32 - name: embeddings_B sequence: float32 splits: - name: train num_bytes: 647745307 num_examples: 100000 download_size: 757450091 dataset_size: 647745307 configs: - config_name: default data_files: - split: train path: data/train-* ---
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gordicaleksa/serbian-llm-eval-v1
gordicaleksa
2023-12-17T22:28:38Z
111
0
[ "language:sr", "license:apache-2.0", "region:us" ]
null
2023-12-14T20:53:51Z
--- license: apache-2.0 language: - sr pretty_name: s --- # Serbian LLM eval 🇷🇸 This dataset should be used for Serbian (and potentially also HBS) LLM evaluation. [GitHub project](https://github.com/gordicaleksa/lm-evaluation-harness-serbian) used to build this dataset. For technical report of the project see this in-depth [Weights & Biases report](https://wandb.ai/gordicaleksa/serbian_llm_eval/reports/First-Serbian-LLM-eval---Vmlldzo2MjgwMDA5). ❤️ TL;DR: ## What is covered? Common sense reasoning: * Hellaswag, Winogrande, PIQA, OpenbookQA, ARC-Easy, ARC-Challenge World knowledge: * NaturalQuestions, TriviaQA Reading comprehension: * BoolQ ## How was the eval created? 3 steps: 1. Machine Translation from English -> Serbian using Google Translate (currently the best system for this lang direction) 2. Refinement via GPT-4 3. Minor manual work by me (Aleksa Gordić) + we'll likely have a new version of Winogrande that was annotated by a human annotator Please see [the report](https://wandb.ai/gordicaleksa/serbian_llm_eval/reports/First-Serbian-LLM-eval---Vmlldzo2MjgwMDA5) for more detail. ## Example of how to use 1. Create a python environment and install HuggingFace datasets (`pip install datasets`). 2. Run: ```Python import datasets tasks = ["arc_challenge", "arc_easy", "boolq", "hellaswag", "nq_open", "openbookqa", "piqa", "triviaqa", "winogrande"] for task in tasks: dataset = datasets.load_dataset("gordicaleksa/serbian-llm-eval-v1", task) for split in dataset.keys(): dataset = dataset[split] print(f"Task: {task}, Split: {split}") for example in dataset: print(example) ``` # Project Sponsors ## Platinum sponsors 🌟 * <b>Ivan</b> (fizicko lice, anoniman) ## Gold sponsors 🟡 * **qq** (fizicko lice, anoniman) * [**Mitar Perovic**](https://www.linkedin.com/in/perovicmitar/) * [**Nikola Ivancevic**](https://www.linkedin.com/in/nivancevic/) ## Silver sponsors ⚪ [**psk.rs**](https://psk.rs/), [**OmniStreak**](https://omnistreak.com/), [**Marko Radojicic**](https://www.linkedin.com/in/marko-radojicic-acmanik-cube/), [**Luka Vazic**](https://www.linkedin.com/in/vazic/), [**Miloš Durković**](https://www.linkedin.com/in/milo%C5%A1-d-684b99188/), [**Marjan Radeski**](https://www.linkedin.com/in/marjanradeski/), **Marjan Stankovic** (fizicko lice), [**Nikola Stojiljkovic**](https://www.linkedin.com/in/nikola-stojiljkovic-10469239/), [**Mihailo Tomic**](https://www.linkedin.com/in/mihailotomic/), [**Bojan Jevtic**](https://www.linkedin.com/in/bojanjevtic/), [**Jelena Jovanović**](https://www.linkedin.com/in/eldumo/), [**Nenad Davidović**](https://www.linkedin.com/in/nenad-davidovic-662ab749/) ## Credits Thank you to the following individuals from my [Discord server](https://discord.gg/peBrCpheKE ) who helped with donating google translate creditss & running machine translation part of the pipeline: [Vera Prohaska](https://vtwoptwo.com/), [Chu Kin Chan](www.linkedin.com/in/roy-ck-chan), [Joe Makepeace](https://www.linkedin.com/in/joe-makepeace-a872a1183/), [Toby Farmer](https://www.linkedin.com/in/tobyfarmer/), [Malvi Bid](https://www.linkedin.com/in/malvibid/), [Raphael Vienne](https://www.linkedin.com/in/raphael-vienne/), [Nenad Aksentijevic](https://www.linkedin.com/in/nenad-aksentijevic-21629a1b6), [Isaac Nicolas](https://www.linkedin.com/in/isaacnicolas/), [Brian Pulfer](https://www.brianpulfer.ch/), [Aldin Cimpo](https://www.linkedin.com/in/aldin-c-b26334189/), [Nikola Ljubesic](https://nljubesi.github.io/) ## Citation ``` @article{serbian-llm-eval, author = "Gordić Aleksa", title = "Serbian LLM Eval", year = "2023" howpublished = {\url{https://huggingface.co/datasets/gordicaleksa/serbian-llm-eval-v1}}, } ``` ## License Apache 2.0.
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chargoddard/PIPPA-Judged
chargoddard
2023-12-12T11:14:03Z
108
1
[ "task_categories:conversational", "language:en", "license:apache-2.0", "not-for-all-audiences", "conversational", "roleplay", "custom-format", "region:us" ]
[ "conversational" ]
2023-08-20T19:59:10Z
--- license: apache-2.0 task_categories: - conversational language: - en tags: - not-for-all-audiences - conversational - roleplay - custom-format dataset_info: - config_name: adequately_rated features: - name: id dtype: string - name: rating struct: - name: analysis dtype: string - name: judge dtype: string - name: score dtype: float64 - name: submission_timestamp dtype: timestamp[ns] - name: categories sequence: string - name: bot_id dtype: string - name: bot_name dtype: string - name: bot_greeting dtype: string - name: bot_definitions dtype: string - name: bot_description dtype: string - name: conversation struct: - name: is_human sequence: bool - name: message sequence: string splits: - name: train num_bytes: 203748289.37737644 num_examples: 14610 download_size: 111617678 dataset_size: 203748289.37737644 - config_name: best_rated features: - name: id dtype: string - name: rating struct: - name: analysis dtype: string - name: judge dtype: string - name: score dtype: float64 - name: submission_timestamp dtype: timestamp[ns] - name: categories sequence: string - name: bot_id dtype: string - name: bot_name dtype: string - name: bot_greeting dtype: string - name: bot_definitions dtype: string - name: bot_description dtype: string - name: conversation struct: - name: is_human sequence: bool - name: message sequence: string splits: - name: train num_bytes: 10780111.409220532 num_examples: 773 download_size: 9421151 dataset_size: 10780111.409220532 - config_name: default features: - name: id dtype: string - name: rating struct: - name: analysis dtype: string - name: judge dtype: string - name: score dtype: float64 - name: submission_timestamp dtype: timestamp[ns] - name: categories sequence: string - name: bot_id dtype: string - name: bot_name dtype: string - name: bot_greeting dtype: string - name: bot_definitions dtype: string - name: bot_description dtype: string - name: conversation struct: - name: is_human sequence: bool - name: message sequence: string splits: - name: train num_bytes: 234735880 num_examples: 16832 download_size: 116686573 dataset_size: 234735880 - config_name: ratings_only features: - name: success dtype: bool - name: score dtype: float64 - name: response dtype: string - name: id dtype: string splits: - name: train num_bytes: 7190167 num_examples: 16832 download_size: 2848419 dataset_size: 7190167 configs: - config_name: adequately_rated data_files: - split: train path: adequately_rated/train-* - config_name: best_rated data_files: - split: train path: best_rated/train-* - config_name: default data_files: - split: train path: data/train-* - config_name: ratings_only data_files: - split: train path: ratings_only/train-* --- # Dataset Card for "PIPPA-Judged" Pygmalion's [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) dataset augmented with quality scores generated by [TheBloke/OpenOrca-Platypus2-13B-GPTQ](https://huggingface.co/TheBloke/OpenOrca-Platypus2-13B-GPTQ). Making this public so people can reproduce the exact dataset used for one of my models - probably not useful for anything else. If you want data along these lines, look at Ilya Gusev's [pippa_scored](https://huggingface.co/datasets/IlyaGusev/pippa_scored) instead. It's much higher quality and better executed.
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nikgerasimenko/ru_sci_bench
nikgerasimenko
2023-12-11T17:59:50Z
108
0
[ "license:mit", "region:us" ]
null
2023-12-11T16:24:15Z
--- license: mit dataset_info: features: - name: paper_id dtype: int64 - name: title dtype: string - name: abstract dtype: string splits: - name: ru num_bytes: 249159304.89138317 num_examples: 194071 - name: en num_bytes: 234221635.10861683 num_examples: 182436 download_size: 246881206 dataset_size: 483380940.0 ---
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librarian-bots/dataset_cards_with_metadata_with_embeddings
librarian-bots
2023-12-11T10:09:00Z
107
1
[ "region:us" ]
null
2023-11-30T11:42:47Z
--- dataset_info: features: - name: datasetId dtype: string - name: author dtype: string - name: last_modified dtype: timestamp[us, tz=UTC] - name: downloads dtype: int64 - name: likes dtype: int64 - name: tags sequence: string - name: task_categories sequence: string - name: createdAt dtype: timestamp[us, tz=UTC] - name: card dtype: string - name: embedding sequence: float32 splits: - name: train num_bytes: 376744038.57453775 num_examples: 56424 download_size: 241039915 dataset_size: 376744038.57453775 configs: - config_name: default data_files: - split: train path: data/train-* ---
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jwl25b/final_project_dataset
jwl25b
2023-12-17T01:57:40Z
106
0
[ "region:us" ]
null
2023-12-17T00:48:16Z
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 3623271.0 num_examples: 50 download_size: 3619422 dataset_size: 3623271.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
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hotal/emergency_alpaca
hotal
2023-12-13T07:22:13Z
102
1
[ "region:us" ]
null
2023-12-13T07:15:51Z
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: system dtype: string splits: - name: train num_bytes: 26661977 num_examples: 26488 download_size: 2937912 dataset_size: 26661977 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "emergency_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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open-llm-leaderboard/results
open-llm-leaderboard
2023-12-17T03:33:59Z
101
20
[ "language:en", "region:us" ]
null
2023-06-19T15:15:24Z
--- language: - en --- ![HuggingFace LeaderBoard](https://cdn-uploads.huggingface.co/production/uploads/6202a599216215a22221dea9/Uh5JX7Kq-rUxoVrdsV-M-.gif) # Open LLM Leaderboard Results This repository contains the outcomes of your submitted models that have been evaluated through the Open LLM Leaderboard. Our goal is to shed light on the cutting-edge Large Language Models (LLMs) and chatbots, enabling you to make well-informed decisions regarding your chosen application. ## Evaluation Methodology The evaluation process involves running your models against several benchmarks from the Eleuther AI Harness, a unified framework for measuring the effectiveness of generative language models. Below is a brief overview of each benchmark: 1. AI2 Reasoning Challenge (ARC) - Grade-School Science Questions (25-shot) 2. HellaSwag - Commonsense Inference (10-shot) 3. MMLU - Massive Multi-Task Language Understanding, knowledge on 57 domains (5-shot) 4. TruthfulQA - Propensity to Produce Falsehoods (0-shot) 5. Winogrande - Adversarial Winograd Schema Challenge (5-shot) 6. GSM8k - Grade School Math Word Problems Solving Complex Mathematical Reasoning (5-shot) Together, these benchmarks provide an assessment of a model's capabilities in terms of knowledge, reasoning, and some math, in various scenarios. ## Exploring Model Details For further insights into the inputs and outputs of specific models, locate the "📄" emoji associated with the desired model in the leaderboard. Clicking on this icon will direct you to the respective GitHub page containing detailed information about the model's behavior during the evaluation process.
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TeamUNIVA/mathQA
TeamUNIVA
2023-12-17T18:17:25Z
97
0
[ "region:us" ]
null
2023-12-17T11:02:33Z
--- dataset_info: features: - name: question_filename dtype: string - name: id dtype: string - name: question_text dtype: string - name: question_bbox dtype: string - name: answer_text dtype: string - name: answer_bbox dtype: string - name: explanation_filename dtype: string - name: explanation_text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 1787424.8960725602 num_examples: 1000 - name: valid num_bytes: 5968211.727986279 num_examples: 3339 - name: train num_bytes: 53729992.375941165 num_examples: 30060 download_size: 27179142 dataset_size: 61485629.0 configs: - config_name: default data_files: - split: test path: data/test-* - split: valid path: data/valid-* - split: train path: data/train-* ---
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booksouls/booksum-cleaned
booksouls
2023-12-15T20:12:35Z
96
0
[ "region:us" ]
null
2023-12-15T20:12:27Z
--- dataset_info: features: - name: chapter dtype: string - name: summary dtype: string splits: - name: train num_bytes: 161588585.20105472 num_examples: 8145 - name: validation num_bytes: 24977290.21094265 num_examples: 1259 - name: test num_bytes: 24104374.588002637 num_examples: 1215 download_size: 134806852 dataset_size: 210670250.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
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thefrankhsu/hate_speech_twitter
thefrankhsu
2023-12-15T03:47:33Z
92
1
[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:en", "health", "tweet", "hate speech", "mental health", "hate speech detection", "hate speech classification", "social media", "mobile health", "region:us" ]
[ "text-classification" ]
2023-12-08T04:19:30Z
--- task_categories: - text-classification language: - en tags: - health - tweet - hate speech - mental health - hate speech detection - hate speech classification - social media - mobile health size_categories: - 1K<n<10K --- ## Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> The dataset is designed to analyze and address hate speech within online platforms. It consists of two sets: the training and testing sets. The two datasets have been labeled and categorized instances of hate speech into nine distinct categories. ## Dataset Description <!-- Provide a longer summary of what this dataset is. --> The dataset comprises three key features: tweets, labels (with hate speech denoted as 1 and non-hate speech as 0), and categories (behavior, class, disability, ethnicity, gender, physical appearance, race, religion, sexual orientation). * Training set: contains a total of 5679 tweets (Hate Speech: 1516 / Non Hate Speech: 4163), and the number of hate speech in each category is not equally distributed. * Testing set: contains a total of 1000 tweets (Hate Speech: 500 / Non Hate Speech: 500), and the number of hate speech in each category is generally even. ## Uses This dataset can be utilized for various purposes, including but not limited to: * Developing and training machine learning models for hate speech detection. * Analyzing the prevalence and patterns of hate speech across different categories. * Understanding the challenges associated with categorizing hate speech on social media platforms. Check it out for the example [project](https://github.com/Wei-Hsi/AI4health)! ## Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> The dataset utilized in this study is sourced from Kaggle and named the [Hate Speech and Offensive Language dataset](https://www.kaggle.com/datasets/mrmorj/hate-speech-and-offensive-language-dataset/). Hate speech instances are identified by selecting tweets within the "class" column. ## Annotations <!-- 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. --> Category labels were generated through an OpenAI API call employing the GPT-3.5 model. It's important to note the instability in category predictions when utilizing GPT-3.5 for label generation, as it tends to predict different categories each time. However, we have confirmed that these tweets were labeled correctly. If there are any misclassified labels, please feel free to reach out. Thank you in advance for your assistance. ## Dataset Card Contact Please feel free to contact me via [email protected]!
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Omnia-S-Abdulnabi/faseeh-testing
Omnia-S-Abdulnabi
2023-12-12T12:51:14Z
90
0
[ "region:us" ]
null
2023-12-12T12:51:14Z
--- dataset_info: features: - name: Arabic dtype: string - name: English dtype: string splits: - name: train num_bytes: 35782.0 num_examples: 101 download_size: 21909 dataset_size: 35782.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
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pminervini/inverse-scaling
pminervini
2023-12-13T20:44:17Z
89
0
[ "license:apache-2.0", "region:us" ]
null
2023-12-13T20:30:37Z
--- license: apache-2.0 dataset_info: - config_name: hindsight-neglect features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 936961 num_examples: 315 download_size: 152660 dataset_size: 936961 - config_name: into-the-unknown features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 947052 num_examples: 1824 download_size: 355350 dataset_size: 947052 - config_name: memo-trap features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 134868 num_examples: 936 download_size: 49639 dataset_size: 134868 - config_name: modus-tollens features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 379054 num_examples: 1236 download_size: 52678 dataset_size: 379054 - config_name: neqa features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 73246 num_examples: 300 download_size: 27381 dataset_size: 73246 - config_name: pattern-matching-suppression features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 292400 num_examples: 1428 download_size: 31469 dataset_size: 292400 - config_name: prompt-injection features: - name: prompt dtype: string - name: completion dtype: string - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 818087 num_examples: 1000 download_size: 260964 dataset_size: 818087 - config_name: redefine features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 159713 num_examples: 1244 download_size: 23235 dataset_size: 159713 - config_name: repetitive-algebra features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 698039 num_examples: 1000 download_size: 162763 dataset_size: 698039 - config_name: resisting-correction features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 3261368 num_examples: 7344 download_size: 937560 dataset_size: 3261368 - config_name: sig-figs features: - name: prompt dtype: string - name: classes dtype: string - name: answer_index dtype: int64 - name: round dtype: int64 - name: part dtype: int64 splits: - name: data num_bytes: 14110021 num_examples: 20897 download_size: 2301790 dataset_size: 14110021 configs: - config_name: hindsight-neglect data_files: - split: data path: hindsight-neglect/data-* - config_name: into-the-unknown data_files: - split: data path: into-the-unknown/data-* - config_name: memo-trap data_files: - split: data path: memo-trap/data-* - config_name: modus-tollens data_files: - split: data path: modus-tollens/data-* - config_name: neqa data_files: - split: data path: neqa/data-* - config_name: pattern-matching-suppression data_files: - split: data path: pattern-matching-suppression/data-* - config_name: prompt-injection data_files: - split: data path: prompt-injection/data-* - config_name: redefine data_files: - split: data path: redefine/data-* - config_name: repetitive-algebra data_files: - split: data path: repetitive-algebra/data-* - config_name: resisting-correction data_files: - split: data path: resisting-correction/data-* - config_name: sig-figs data_files: - split: data path: sig-figs/data-* ---
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