datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
deep1412/pen_auto_train | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_JaeyeonKang__CCK_Gony_v3.2 | ---
pretty_name: Evaluation run of JaeyeonKang/CCK_Gony_v3.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [JaeyeonKang/CCK_Gony_v3.2](https://huggingface.co/JaeyeonKang/CCK_Gony_v3.2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_JaeyeonKang__CCK_Gony_v3.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-02T09:25:22.859036](https://huggingface.co/datasets/open-llm-leaderboard/details_JaeyeonKang__CCK_Gony_v3.2/blob/main/results_2024-02-02T09-25-22.859036.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\": 0.7047084782513622,\n\
\ \"acc_stderr\": 0.030315860999845592,\n \"acc_norm\": 0.7093203962624949,\n\
\ \"acc_norm_stderr\": 0.030894611332654892,\n \"mc1\": 0.4418604651162791,\n\
\ \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.5881032370657441,\n\
\ \"mc2_stderr\": 0.015065851872175183\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6467576791808873,\n \"acc_stderr\": 0.013967822714840055,\n\
\ \"acc_norm\": 0.6945392491467577,\n \"acc_norm_stderr\": 0.013460080478002508\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6735710017924716,\n\
\ \"acc_stderr\": 0.004679479763516775,\n \"acc_norm\": 0.8691495717984465,\n\
\ \"acc_norm_stderr\": 0.003365474860676741\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411021,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411021\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6888888888888889,\n\
\ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n\
\ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7960526315789473,\n \"acc_stderr\": 0.0327900040631005,\n\
\ \"acc_norm\": 0.7960526315789473,\n \"acc_norm_stderr\": 0.0327900040631005\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.769811320754717,\n \"acc_stderr\": 0.02590789712240817,\n\
\ \"acc_norm\": 0.769811320754717,\n \"acc_norm_stderr\": 0.02590789712240817\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8541666666666666,\n\
\ \"acc_stderr\": 0.029514245964291766,\n \"acc_norm\": 0.8541666666666666,\n\
\ \"acc_norm_stderr\": 0.029514245964291766\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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-college_mathematics|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7456647398843931,\n\
\ \"acc_stderr\": 0.03320556443085569,\n \"acc_norm\": 0.7456647398843931,\n\
\ \"acc_norm_stderr\": 0.03320556443085569\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\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6553191489361702,\n \"acc_stderr\": 0.03106898596312215,\n\
\ \"acc_norm\": 0.6553191489361702,\n \"acc_norm_stderr\": 0.03106898596312215\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5789473684210527,\n\
\ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.5789473684210527,\n\
\ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6689655172413793,\n \"acc_stderr\": 0.03921545312467122,\n\
\ \"acc_norm\": 0.6689655172413793,\n \"acc_norm_stderr\": 0.03921545312467122\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.49206349206349204,\n \"acc_stderr\": 0.025748065871673272,\n \"\
acc_norm\": 0.49206349206349204,\n \"acc_norm_stderr\": 0.025748065871673272\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5158730158730159,\n\
\ \"acc_stderr\": 0.044698818540726076,\n \"acc_norm\": 0.5158730158730159,\n\
\ \"acc_norm_stderr\": 0.044698818540726076\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8548387096774194,\n\
\ \"acc_stderr\": 0.020039563628053283,\n \"acc_norm\": 0.8548387096774194,\n\
\ \"acc_norm_stderr\": 0.020039563628053283\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5911330049261084,\n \"acc_stderr\": 0.034590588158832314,\n\
\ \"acc_norm\": 0.5911330049261084,\n \"acc_norm_stderr\": 0.034590588158832314\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.0315841532404771,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.0315841532404771\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8585858585858586,\n \"acc_stderr\": 0.024825909793343336,\n \"\
acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.024825909793343336\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9481865284974094,\n \"acc_stderr\": 0.01599622932024412,\n\
\ \"acc_norm\": 0.9481865284974094,\n \"acc_norm_stderr\": 0.01599622932024412\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7153846153846154,\n \"acc_stderr\": 0.0228783227997063,\n \
\ \"acc_norm\": 0.7153846153846154,\n \"acc_norm_stderr\": 0.0228783227997063\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35555555555555557,\n \"acc_stderr\": 0.0291857149498574,\n \
\ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.0291857149498574\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7983193277310925,\n \"acc_stderr\": 0.026064313406304534,\n\
\ \"acc_norm\": 0.7983193277310925,\n \"acc_norm_stderr\": 0.026064313406304534\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4304635761589404,\n \"acc_stderr\": 0.040428099613956346,\n \"\
acc_norm\": 0.4304635761589404,\n \"acc_norm_stderr\": 0.040428099613956346\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8825688073394495,\n \"acc_stderr\": 0.013802780227377342,\n \"\
acc_norm\": 0.8825688073394495,\n \"acc_norm_stderr\": 0.013802780227377342\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\"\
: 0.8725490196078431,\n \"acc_stderr\": 0.02340553048084631,\n \"\
acc_norm\": 0.8725490196078431,\n \"acc_norm_stderr\": 0.02340553048084631\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8607594936708861,\n \"acc_stderr\": 0.022535526352692705,\n \
\ \"acc_norm\": 0.8607594936708861,\n \"acc_norm_stderr\": 0.022535526352692705\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.757847533632287,\n\
\ \"acc_stderr\": 0.028751392398694755,\n \"acc_norm\": 0.757847533632287,\n\
\ \"acc_norm_stderr\": 0.028751392398694755\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.803680981595092,\n \"acc_stderr\": 0.031207970394709218,\n\
\ \"acc_norm\": 0.803680981595092,\n \"acc_norm_stderr\": 0.031207970394709218\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\": 0.8446601941747572,\n \"acc_stderr\": 0.035865947385739734,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.035865947385739734\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\
\ \"acc_stderr\": 0.01700436856813235,\n \"acc_norm\": 0.9273504273504274,\n\
\ \"acc_norm_stderr\": 0.01700436856813235\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \
\ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932262\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8812260536398467,\n\
\ \"acc_stderr\": 0.011569134791715655,\n \"acc_norm\": 0.8812260536398467,\n\
\ \"acc_norm_stderr\": 0.011569134791715655\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7687861271676301,\n \"acc_stderr\": 0.02269865716785571,\n\
\ \"acc_norm\": 0.7687861271676301,\n \"acc_norm_stderr\": 0.02269865716785571\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.464804469273743,\n\
\ \"acc_stderr\": 0.01668102093107665,\n \"acc_norm\": 0.464804469273743,\n\
\ \"acc_norm_stderr\": 0.01668102093107665\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8202614379084967,\n \"acc_stderr\": 0.021986032182064148,\n\
\ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.021986032182064148\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8006430868167203,\n\
\ \"acc_stderr\": 0.022691033780549656,\n \"acc_norm\": 0.8006430868167203,\n\
\ \"acc_norm_stderr\": 0.022691033780549656\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.021185893615225153,\n\
\ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.021185893615225153\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5319148936170213,\n \"acc_stderr\": 0.02976667507587387,\n \
\ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.02976667507587387\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5378096479791395,\n\
\ \"acc_stderr\": 0.012733671880342506,\n \"acc_norm\": 0.5378096479791395,\n\
\ \"acc_norm_stderr\": 0.012733671880342506\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8014705882352942,\n \"acc_stderr\": 0.024231013370541073,\n\
\ \"acc_norm\": 0.8014705882352942,\n \"acc_norm_stderr\": 0.024231013370541073\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7532679738562091,\n \"acc_stderr\": 0.0174408203674025,\n \
\ \"acc_norm\": 0.7532679738562091,\n \"acc_norm_stderr\": 0.0174408203674025\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7510204081632653,\n \"acc_stderr\": 0.027682979522960234,\n\
\ \"acc_norm\": 0.7510204081632653,\n \"acc_norm_stderr\": 0.027682979522960234\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8756218905472637,\n\
\ \"acc_stderr\": 0.023335401790166327,\n \"acc_norm\": 0.8756218905472637,\n\
\ \"acc_norm_stderr\": 0.023335401790166327\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\": 0.5060240963855421,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.02464806896136615,\n\
\ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.02464806896136615\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4418604651162791,\n\
\ \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.5881032370657441,\n\
\ \"mc2_stderr\": 0.015065851872175183\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8097868981846882,\n \"acc_stderr\": 0.01103033579861744\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5708870356330553,\n \
\ \"acc_stderr\": 0.013633369425647236\n }\n}\n```"
repo_url: https://huggingface.co/JaeyeonKang/CCK_Gony_v3.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|arc:challenge|25_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|arc:challenge|25_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|arc:challenge|25_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-02T09-25-22.859036.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|gsm8k|5_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|gsm8k|5_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|gsm8k|5_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-02T09-25-22.859036.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|hellaswag|10_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|hellaswag|10_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|hellaswag|10_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-02T09-25-22.859036.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T08-37-17.217721.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T08-37-17.217721.parquet'
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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data_files:
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path:
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data_files:
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data_files:
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data_files:
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path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T09-25-22.859036.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T09-25-22.859036.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T09-25-22.859036.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T09-25-22.859036.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T09-25-22.859036.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- '**/details_harness|winogrande|5_2024-02-02T08-37-17.217721.parquet'
- split: 2024_02_02T09_00_50.830888
path:
- '**/details_harness|winogrande|5_2024-02-02T09-00-50.830888.parquet'
- split: 2024_02_02T09_25_22.859036
path:
- '**/details_harness|winogrande|5_2024-02-02T09-25-22.859036.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-02T09-25-22.859036.parquet'
- config_name: results
data_files:
- split: 2024_02_02T08_37_17.217721
path:
- results_2024-02-02T08-37-17.217721.parquet
- split: 2024_02_02T09_00_50.830888
path:
- results_2024-02-02T09-00-50.830888.parquet
- split: 2024_02_02T09_25_22.859036
path:
- results_2024-02-02T09-25-22.859036.parquet
- split: latest
path:
- results_2024-02-02T09-25-22.859036.parquet
---
# Dataset Card for Evaluation run of JaeyeonKang/CCK_Gony_v3.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [JaeyeonKang/CCK_Gony_v3.2](https://huggingface.co/JaeyeonKang/CCK_Gony_v3.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_JaeyeonKang__CCK_Gony_v3.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-02T09:25:22.859036](https://huggingface.co/datasets/open-llm-leaderboard/details_JaeyeonKang__CCK_Gony_v3.2/blob/main/results_2024-02-02T09-25-22.859036.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": 0.7047084782513622,
"acc_stderr": 0.030315860999845592,
"acc_norm": 0.7093203962624949,
"acc_norm_stderr": 0.030894611332654892,
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.5881032370657441,
"mc2_stderr": 0.015065851872175183
},
"harness|arc:challenge|25": {
"acc": 0.6467576791808873,
"acc_stderr": 0.013967822714840055,
"acc_norm": 0.6945392491467577,
"acc_norm_stderr": 0.013460080478002508
},
"harness|hellaswag|10": {
"acc": 0.6735710017924716,
"acc_stderr": 0.004679479763516775,
"acc_norm": 0.8691495717984465,
"acc_norm_stderr": 0.003365474860676741
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411021,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411021
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6888888888888889,
"acc_stderr": 0.039992628766177214,
"acc_norm": 0.6888888888888889,
"acc_norm_stderr": 0.039992628766177214
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7960526315789473,
"acc_stderr": 0.0327900040631005,
"acc_norm": 0.7960526315789473,
"acc_norm_stderr": 0.0327900040631005
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.769811320754717,
"acc_stderr": 0.02590789712240817,
"acc_norm": 0.769811320754717,
"acc_norm_stderr": 0.02590789712240817
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8541666666666666,
"acc_stderr": 0.029514245964291766,
"acc_norm": 0.8541666666666666,
"acc_norm_stderr": 0.029514245964291766
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7456647398843931,
"acc_stderr": 0.03320556443085569,
"acc_norm": 0.7456647398843931,
"acc_norm_stderr": 0.03320556443085569
},
"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": 0.8,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6553191489361702,
"acc_stderr": 0.03106898596312215,
"acc_norm": 0.6553191489361702,
"acc_norm_stderr": 0.03106898596312215
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5789473684210527,
"acc_stderr": 0.046446020912223177,
"acc_norm": 0.5789473684210527,
"acc_norm_stderr": 0.046446020912223177
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6689655172413793,
"acc_stderr": 0.03921545312467122,
"acc_norm": 0.6689655172413793,
"acc_norm_stderr": 0.03921545312467122
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.49206349206349204,
"acc_stderr": 0.025748065871673272,
"acc_norm": 0.49206349206349204,
"acc_norm_stderr": 0.025748065871673272
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5158730158730159,
"acc_stderr": 0.044698818540726076,
"acc_norm": 0.5158730158730159,
"acc_norm_stderr": 0.044698818540726076
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8548387096774194,
"acc_stderr": 0.020039563628053283,
"acc_norm": 0.8548387096774194,
"acc_norm_stderr": 0.020039563628053283
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5911330049261084,
"acc_stderr": 0.034590588158832314,
"acc_norm": 0.5911330049261084,
"acc_norm_stderr": 0.034590588158832314
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.793939393939394,
"acc_stderr": 0.0315841532404771,
"acc_norm": 0.793939393939394,
"acc_norm_stderr": 0.0315841532404771
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8585858585858586,
"acc_stderr": 0.024825909793343336,
"acc_norm": 0.8585858585858586,
"acc_norm_stderr": 0.024825909793343336
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9481865284974094,
"acc_stderr": 0.01599622932024412,
"acc_norm": 0.9481865284974094,
"acc_norm_stderr": 0.01599622932024412
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7153846153846154,
"acc_stderr": 0.0228783227997063,
"acc_norm": 0.7153846153846154,
"acc_norm_stderr": 0.0228783227997063
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35555555555555557,
"acc_stderr": 0.0291857149498574,
"acc_norm": 0.35555555555555557,
"acc_norm_stderr": 0.0291857149498574
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7983193277310925,
"acc_stderr": 0.026064313406304534,
"acc_norm": 0.7983193277310925,
"acc_norm_stderr": 0.026064313406304534
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4304635761589404,
"acc_stderr": 0.040428099613956346,
"acc_norm": 0.4304635761589404,
"acc_norm_stderr": 0.040428099613956346
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8825688073394495,
"acc_stderr": 0.013802780227377342,
"acc_norm": 0.8825688073394495,
"acc_norm_stderr": 0.013802780227377342
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6064814814814815,
"acc_stderr": 0.03331747876370312,
"acc_norm": 0.6064814814814815,
"acc_norm_stderr": 0.03331747876370312
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8725490196078431,
"acc_stderr": 0.02340553048084631,
"acc_norm": 0.8725490196078431,
"acc_norm_stderr": 0.02340553048084631
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8607594936708861,
"acc_stderr": 0.022535526352692705,
"acc_norm": 0.8607594936708861,
"acc_norm_stderr": 0.022535526352692705
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.757847533632287,
"acc_stderr": 0.028751392398694755,
"acc_norm": 0.757847533632287,
"acc_norm_stderr": 0.028751392398694755
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8760330578512396,
"acc_stderr": 0.030083098716035202,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035202
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.03755265865037182,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.03755265865037182
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.803680981595092,
"acc_stderr": 0.031207970394709218,
"acc_norm": 0.803680981595092,
"acc_norm_stderr": 0.031207970394709218
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5982142857142857,
"acc_stderr": 0.04653333146973647,
"acc_norm": 0.5982142857142857,
"acc_norm_stderr": 0.04653333146973647
},
"harness|hendrycksTest-management|5": {
"acc": 0.8446601941747572,
"acc_stderr": 0.035865947385739734,
"acc_norm": 0.8446601941747572,
"acc_norm_stderr": 0.035865947385739734
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9273504273504274,
"acc_stderr": 0.01700436856813235,
"acc_norm": 0.9273504273504274,
"acc_norm_stderr": 0.01700436856813235
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932262,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932262
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8812260536398467,
"acc_stderr": 0.011569134791715655,
"acc_norm": 0.8812260536398467,
"acc_norm_stderr": 0.011569134791715655
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7687861271676301,
"acc_stderr": 0.02269865716785571,
"acc_norm": 0.7687861271676301,
"acc_norm_stderr": 0.02269865716785571
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.464804469273743,
"acc_stderr": 0.01668102093107665,
"acc_norm": 0.464804469273743,
"acc_norm_stderr": 0.01668102093107665
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8202614379084967,
"acc_stderr": 0.021986032182064148,
"acc_norm": 0.8202614379084967,
"acc_norm_stderr": 0.021986032182064148
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.8006430868167203,
"acc_stderr": 0.022691033780549656,
"acc_norm": 0.8006430868167203,
"acc_norm_stderr": 0.022691033780549656
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8240740740740741,
"acc_stderr": 0.021185893615225153,
"acc_norm": 0.8240740740740741,
"acc_norm_stderr": 0.021185893615225153
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5319148936170213,
"acc_stderr": 0.02976667507587387,
"acc_norm": 0.5319148936170213,
"acc_norm_stderr": 0.02976667507587387
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5378096479791395,
"acc_stderr": 0.012733671880342506,
"acc_norm": 0.5378096479791395,
"acc_norm_stderr": 0.012733671880342506
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.8014705882352942,
"acc_stderr": 0.024231013370541073,
"acc_norm": 0.8014705882352942,
"acc_norm_stderr": 0.024231013370541073
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7532679738562091,
"acc_stderr": 0.0174408203674025,
"acc_norm": 0.7532679738562091,
"acc_norm_stderr": 0.0174408203674025
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7510204081632653,
"acc_stderr": 0.027682979522960234,
"acc_norm": 0.7510204081632653,
"acc_norm_stderr": 0.027682979522960234
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8756218905472637,
"acc_stderr": 0.023335401790166327,
"acc_norm": 0.8756218905472637,
"acc_norm_stderr": 0.023335401790166327
},
"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": 0.5060240963855421,
"acc_stderr": 0.03892212195333045,
"acc_norm": 0.5060240963855421,
"acc_norm_stderr": 0.03892212195333045
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8830409356725146,
"acc_stderr": 0.02464806896136615,
"acc_norm": 0.8830409356725146,
"acc_norm_stderr": 0.02464806896136615
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.5881032370657441,
"mc2_stderr": 0.015065851872175183
},
"harness|winogrande|5": {
"acc": 0.8097868981846882,
"acc_stderr": 0.01103033579861744
},
"harness|gsm8k|5": {
"acc": 0.5708870356330553,
"acc_stderr": 0.013633369425647236
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
prince-canuma/SmallOrca | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 167546891
num_examples: 102371
download_size: 84881127
dataset_size: 167546891
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Multimodal-Fatima/OxfordFlowers_test_facebook_opt_6.7b_Attributes_Caption_ns_6149_random | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_1_bs_16
num_bytes: 269126072.375
num_examples: 6149
- name: fewshot_3_bs_16
num_bytes: 272734326.375
num_examples: 6149
download_size: 524724011
dataset_size: 541860398.75
---
# Dataset Card for "OxfordFlowers_test_facebook_opt_6.7b_Attributes_Caption_ns_6149_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xwjzds/ag_news_embed | ---
dataset_info:
features:
- name: train
sequence: float32
splits:
- name: train
num_bytes: 77000000
num_examples: 50000
download_size: 5355833
dataset_size: 77000000
---
# Dataset Card for "ag_news_embed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HPGomes/MichaelJacksonFalsetto | ---
license: openrail
---
|
Bepitic/DND-description-Action | ---
license: gpl-3.0
task_categories:
- question-answering
pretty_name: DND Description Action
--- |
mekaneeky/Synthetic_Runyankole_VITS_22.5k | ---
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: eng
dtype: string
- name: lug
dtype: string
- name: ach
dtype: string
- name: teo
dtype: string
- name: lgg
dtype: string
- name: nyn
dtype: string
- name: ID
dtype: string
- name: runyankole_synthetic_audio
sequence:
sequence: float32
splits:
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num_bytes: 14611930432
num_examples: 23947
- name: dev
num_bytes: 304694860
num_examples: 500
- name: test
num_bytes: 324234504
num_examples: 500
download_size: 15255437028
dataset_size: 15240859796
---
# Dataset Card for "Synthetic_Runyankole_VITS_22.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anan-2024/twitter_dataset_1713160687 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 105351
num_examples: 285
download_size: 59405
dataset_size: 105351
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dhyay/Leetcode_QA_nl | ---
license: mit
---
|
liuyanchen1015/MULTI_VALUE_sst2_what_comparative | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
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num_examples: 5
- name: test
num_bytes: 138
num_examples: 1
- name: train
num_bytes: 13891
num_examples: 108
download_size: 11564
dataset_size: 14807
---
# Dataset Card for "MULTI_VALUE_sst2_what_comparative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dayaburamshetty/reuters_articles | ---
dataset_info:
features:
- name: title
dtype: string
- name: body
dtype: string
splits:
- name: train
num_bytes: 13792576
num_examples: 17262
- name: validation
num_bytes: 1870389
num_examples: 2158
- name: test
num_bytes: 1379190
num_examples: 2158
download_size: 10073414
dataset_size: 17042155
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
CyberHarem/hakozaki_serika_theidolmstermillionlive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hakozaki_serika/箱崎星梨花 (THE iDOLM@STER: Million Live!)
This is the dataset of hakozaki_serika/箱崎星梨花 (THE iDOLM@STER: Million Live!), containing 396 images and their tags.
The core tags of this character are `brown_hair, twintails, long_hair, brown_eyes, ahoge, bangs, ribbon, hair_ribbon, bow, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 396 | 441.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 396 | 276.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 915 | 562.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 396 | 399.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 915 | 767.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hakozaki_serika_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, green_ribbon, looking_at_viewer, solo, :d, blush, open_mouth, sitting, white_background, green_dress, simple_background, short_sleeves |
| 1 | 15 |  |  |  |  |  | 1girl, solo, :d, open_mouth, looking_at_viewer, dress |
| 2 | 9 |  |  |  |  |  | 1girl, navel, solo, open_mouth, smile, white_bikini, blush, sailor_bikini, simple_background, white_background, looking_at_viewer, collarbone, small_breasts |
| 3 | 5 |  |  |  |  |  | 1girl, detached_collar, playboy_bunny, rabbit_ears, wrist_cuffs, solo, black_leotard, fake_animal_ears, looking_at_viewer, bare_shoulders, black_bowtie, blush, rabbit_tail, simple_background, small_breasts, strapless_leotard, thighband_pantyhose |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | green_ribbon | looking_at_viewer | solo | :d | blush | open_mouth | sitting | white_background | green_dress | simple_background | short_sleeves | dress | navel | smile | white_bikini | sailor_bikini | collarbone | small_breasts | detached_collar | playboy_bunny | rabbit_ears | wrist_cuffs | black_leotard | fake_animal_ears | bare_shoulders | black_bowtie | rabbit_tail | strapless_leotard | thighband_pantyhose |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------|:-----|:--------|:-------------|:----------|:-------------------|:--------------|:--------------------|:----------------|:--------|:--------|:--------|:---------------|:----------------|:-------------|:----------------|:------------------|:----------------|:--------------|:--------------|:----------------|:-------------------|:-----------------|:---------------|:--------------|:--------------------|:----------------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | | X | X | X | | X | | | | | | X | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | X | X | | X | X | | X | | X | | | X | X | X | X | X | X | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | X | | X | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
Hunterlin/test | ---
license: openrail
---
|
aminlouhichi/donut4 | ---
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: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 25755968.0
num_examples: 60
- name: validation
num_bytes: 25755968.0
num_examples: 60
- name: test
num_bytes: 25755968.0
num_examples: 60
download_size: 55048836
dataset_size: 77267904.0
---
# Dataset Card for "donut4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
treezy254/hf-codegen_v1 | ---
dataset_info:
features:
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 20203861
num_examples: 1776
download_size: 5927051
dataset_size: 20203861
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hf-codegen_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
llm-aes/toy1_dataset_hanna_2_prompts | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: worker_id
dtype: string
- name: human_label
dtype: int64
- name: llm_label
dtype: int64
- name: generator_1
dtype: string
- name: generator_2
dtype: string
- name: premise
dtype: string
splits:
- name: train
num_bytes: 22467
num_examples: 110
download_size: 5579
dataset_size: 22467
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-anli-plain_text-f2dca1-2066067125 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- anli
eval_info:
task: natural_language_inference
model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
metrics: []
dataset_name: anli
dataset_config: plain_text
dataset_split: dev_r1
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
* Dataset: anli
* Config: plain_text
* Split: dev_r1
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ctkang](https://huggingface.co/ctkang) for evaluating this model. |
yjernite/prof_report__stabilityai-stable-diffusion-2-1-base__multi__24 | ---
dataset_info:
features:
- name: cluster_id
dtype: int64
- name: cluster_size
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num_bytes: 1720
num_examples: 5
- name: roofer
num_bytes: 1696
num_examples: 4
- name: sales_manager
num_bytes: 1744
num_examples: 6
- name: salesperson
num_bytes: 1816
num_examples: 9
- name: school_bus_driver
num_bytes: 1792
num_examples: 8
- name: scientist
num_bytes: 1768
num_examples: 7
- name: security_guard
num_bytes: 1720
num_examples: 5
- name: sheet_metal_worker
num_bytes: 1744
num_examples: 6
- name: singer
num_bytes: 1888
num_examples: 12
- name: social_assistant
num_bytes: 1864
num_examples: 11
- name: social_worker
num_bytes: 1936
num_examples: 14
- name: software_developer
num_bytes: 1816
num_examples: 9
- name: stocker
num_bytes: 1792
num_examples: 8
- name: supervisor
num_bytes: 1816
num_examples: 9
- name: taxi_driver
num_bytes: 1792
num_examples: 8
- name: teacher
num_bytes: 1792
num_examples: 8
- name: teaching_assistant
num_bytes: 1840
num_examples: 10
- name: teller
num_bytes: 1912
num_examples: 13
- name: therapist
num_bytes: 1792
num_examples: 8
- name: tractor_operator
num_bytes: 1744
num_examples: 6
- name: truck_driver
num_bytes: 1768
num_examples: 7
- name: tutor
num_bytes: 1792
num_examples: 8
- name: underwriter
num_bytes: 1888
num_examples: 12
- name: veterinarian
num_bytes: 1792
num_examples: 8
- name: welder
num_bytes: 1744
num_examples: 6
- name: wholesale_buyer
num_bytes: 1840
num_examples: 10
- name: writer
num_bytes: 1744
num_examples: 6
download_size: 636209
dataset_size: 262232
---
# Dataset Card for "prof_report__stabilityai-stable-diffusion-2-1-base__multi__24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/data-standardized_cluster_3_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 9488520
num_examples: 3974
download_size: 3945455
dataset_size: 9488520
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_3_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lizongliang/mnist | ---
license: apache-2.0
---
|
jschavesh/mini-platypus | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245925
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tiedong/goat | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset.json file contains ~1.7 million synthetic data for arithmetic tasks, generated by dataset.ipynb.
### 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] |
GEM/xlsum | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- und
license:
- cc-by-nc-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: xlsum
---
# Dataset Card for GEM/xlsum
## Dataset Description
- **Homepage:** https://github.com/csebuetnlp/xl-sum
- **Repository:** https://huggingface.co/datasets/csebuetnlp/xlsum/tree/main/data
- **Paper:** https://aclanthology.org/2021.findings-acl.413/
- **Leaderboard:** http://explainaboard.nlpedia.ai/leaderboard/task_xlsum/
- **Point of Contact:** Tahmid Hasan
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xlsum).
### Dataset Summary
XLSum is a highly multilingual summarization dataset supporting 44 language. The data stems from BBC news articles.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/xlsum')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/xlsum).
#### website
[Github](https://github.com/csebuetnlp/xl-sum)
#### paper
[ACL Anthology](https://aclanthology.org/2021.findings-acl.413/)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/csebuetnlp/xl-sum)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Huggingface](https://huggingface.co/datasets/csebuetnlp/xlsum/tree/main/data)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2021.findings-acl.413/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.413",
pages = "4693--4703",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Tahmid Hasan
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
tahmidhasan@cse.buet.ac.bd
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
[Explainaboard](http://explainaboard.nlpedia.ai/leaderboard/task_xlsum/)
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
The leaderboard ranks models based on ROUGE scores (R1/R2/RL) of the generated summaries.
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Amharic`, `Arabic`, `Azerbaijani`, `Bengali, Bangla`, `Burmese`, `Chinese (family)`, `English`, `French`, `Gujarati`, `Hausa`, `Hindi`, `Igbo`, `Indonesian`, `Japanese`, `Rundi`, `Korean`, `Kirghiz, Kyrgyz`, `Marathi`, `Nepali (individual language)`, `Oromo`, `Pushto, Pashto`, `Persian`, `Ghanaian Pidgin English`, `Portuguese`, `Panjabi, Punjabi`, `Russian`, `Scottish Gaelic, Gaelic`, `Serbian`, `Romano-Serbian`, `Sinhala, Sinhalese`, `Somali`, `Spanish, Castilian`, `Swahili (individual language), Kiswahili`, `Tamil`, `Telugu`, `Thai`, `Tigrinya`, `Turkish`, `Ukrainian`, `Urdu`, `Uzbek`, `Vietnamese`, `Welsh`, `Yoruba`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Abstractive summarization has centered around the English language, as most large abstractive summarization datasets are available in English only. Though there have been some recent efforts for curating multilingual abstractive summarization datasets, they are limited in terms of the number of languages covered, the number of training samples, or both. To this end, **XL-Sum** presents a large-scale abstractive summarization dataset of 1.35 million news articles from 45 languages crawled from the British Broadcasting Corporation website. It is intended to be used for both multilingual and per-language summarization tasks.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Summarize news-like text in one of 45 languages.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Bangladesh University of Engineering and Technology
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Tahmid Hasan (Bangladesh University of Engineering and Technology), Abhik Bhattacharjee (Bangladesh University of Engineering and Technology)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id`: A string representing the article ID.
- `url`: A string representing the article URL.
- `title`: A string containing the article title.
- `summary`: A string containing the article summary.
- `text` : A string containing the article text.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"gem_id": "GEM-xlsum_english-train-1589",
"url": "[BBC news](https://www.bbc.com/news)/technology-17657859",
"title": "Yahoo files e-book advert system patent applications",
"summary": "Yahoo has signalled it is investigating e-book adverts as a way to stimulate its earnings.",
"text": "Yahoo's patents suggest users could weigh the type of ads against the sizes of discount before purchase. It says in two US patent applications that ads for digital book readers have been \"less than optimal\" to date. The filings suggest that users could be offered titles at a variety of prices depending on the ads' prominence They add that the products shown could be determined by the type of book being read, or even the contents of a specific chapter, phrase or word. The paperwork was published by the US Patent and Trademark Office late last week and relates to work carried out at the firm's headquarters in Sunnyvale, California. \"Greater levels of advertising, which may be more valuable to an advertiser and potentially more distracting to an e-book reader, may warrant higher discounts,\" it states. Free books It suggests users could be offered ads as hyperlinks based within the book's text, in-laid text or even \"dynamic content\" such as video. Another idea suggests boxes at the bottom of a page could trail later chapters or quotes saying \"brought to you by Company A\". It adds that the more willing the customer is to see the ads, the greater the potential discount. \"Higher frequencies... may even be great enough to allow the e-book to be obtained for free,\" it states. The authors write that the type of ad could influence the value of the discount, with \"lower class advertising... such as teeth whitener advertisements\" offering a cheaper price than \"high\" or \"middle class\" adverts, for things like pizza. The inventors also suggest that ads could be linked to the mood or emotional state the reader is in as a they progress through a title. For example, they say if characters fall in love or show affection during a chapter, then ads for flowers or entertainment could be triggered. The patents also suggest this could applied to children's books - giving the Tom Hanks animated film Polar Express as an example. It says a scene showing a waiter giving the protagonists hot drinks \"may be an excellent opportunity to show an advertisement for hot cocoa, or a branded chocolate bar\". Another example states: \"If the setting includes young characters, a Coke advertisement could be provided, inviting the reader to enjoy a glass of Coke with his book, and providing a graphic of a cool glass.\" It adds that such targeting could be further enhanced by taking account of previous titles the owner has bought. 'Advertising-free zone' At present, several Amazon and Kobo e-book readers offer full-screen adverts when the device is switched off and show smaller ads on their menu screens, but the main text of the titles remains free of marketing. Yahoo does not currently provide ads to these devices, and a move into the area could boost its shrinking revenues. However, Philip Jones, deputy editor of the Bookseller magazine, said that the internet firm might struggle to get some of its ideas adopted. \"This has been mooted before and was fairly well decried,\" he said. \"Perhaps in a limited context it could work if the merchandise was strongly related to the title and was kept away from the text. \"But readers - particularly parents - like the fact that reading is an advertising-free zone. Authors would also want something to say about ads interrupting their narrative flow.\""
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The splits in the dataset are specified by the language names, which are as follows:
- `amharic`
- `arabic`
- `azerbaijani`
- `bengali`
- `burmese`
- `chinese_simplified`
- `chinese_traditional`
- `english`
- `french`
- `gujarati`
- `hausa`
- `hindi`
- `igbo`
- `indonesian`
- `japanese`
- `kirundi`
- `korean`
- `kyrgyz`
- `marathi`
- `nepali`
- `oromo`
- `pashto`
- `persian`
- `pidgin`
- `portuguese`
- `punjabi`
- `russian`
- `scottish_gaelic`
- `serbian_cyrillic`
- `serbian_latin`
- `sinhala`
- `somali`
- `spanish`
- `swahili`
- `tamil`
- `telugu`
- `thai`
- `tigrinya`
- `turkish`
- `ukrainian`
- `urdu`
- `uzbek`
- `vietnamese`
- `welsh`
- `yoruba`
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
We used a 80%-10%-10% split for all languages with a few exceptions. `English` was split 93%-3.5%-3.5% for the evaluation set size to resemble that of `CNN/DM` and `XSum`; `Scottish Gaelic`, `Kyrgyz` and `Sinhala` had relatively fewer samples, their evaluation sets were increased to 500 samples for more reliable evaluation. Same articles were used for evaluation in the two variants of Chinese and Serbian to prevent data leakage in multilingual training. Individual dataset download links with train-dev-test example counts are given below:
Language | ISO 639-1 Code | BBC subdomain(s) | Train | Dev | Test | Total |
--------------|----------------|------------------|-------|-----|------|-------|
Amharic | am | [BBC amharic](https://www.bbc.com/amharic) | 5761 | 719 | 719 | 7199 |
Arabic | ar | [BBC arabic](https://www.bbc.com/arabic) | 37519 | 4689 | 4689 | 46897 |
Azerbaijani | az | [BBC azeri](https://www.bbc.com/azeri) | 6478 | 809 | 809 | 8096 |
Bengali | bn | [BBC bengali](https://www.bbc.com/bengali) | 8102 | 1012 | 1012 | 10126 |
Burmese | my | [BBC burmese](https://www.bbc.com/burmese) | 4569 | 570 | 570 | 5709 |
Chinese (Simplified) | zh-CN | [BBC ukchina](https://www.bbc.com/ukchina)/simp, [BBC zhongwen](https://www.bbc.com/zhongwen)/simp | 37362 | 4670 | 4670 | 46702 |
Chinese (Traditional) | zh-TW | [BBC ukchina](https://www.bbc.com/ukchina)/trad, [BBC zhongwen](https://www.bbc.com/zhongwen)/trad | 37373 | 4670 | 4670 | 46713 |
English | en | [BBC english](https://www.bbc.com/english), [BBC sinhala](https://www.bbc.com/sinhala) `*` | 306522 | 11535 | 11535 | 329592 |
French | fr | [BBC afrique](https://www.bbc.com/afrique) | 8697 | 1086 | 1086 | 10869 |
Gujarati | gu | [BBC gujarati](https://www.bbc.com/gujarati) | 9119 | 1139 | 1139 | 11397 |
Hausa | ha | [BBC hausa](https://www.bbc.com/hausa) | 6418 | 802 | 802 | 8022 |
Hindi | hi | [BBC hindi](https://www.bbc.com/hindi) | 70778 | 8847 | 8847 | 88472 |
Igbo | ig | [BBC igbo](https://www.bbc.com/igbo) | 4183 | 522 | 522 | 5227 |
Indonesian | id | [BBC indonesia](https://www.bbc.com/indonesia) | 38242 | 4780 | 4780 | 47802 |
Japanese | ja | [BBC japanese](https://www.bbc.com/japanese) | 7113 | 889 | 889 | 8891 |
Kirundi | rn | [BBC gahuza](https://www.bbc.com/gahuza) | 5746 | 718 | 718 | 7182 |
Korean | ko | [BBC korean](https://www.bbc.com/korean) | 4407 | 550 | 550 | 5507 |
Kyrgyz | ky | [BBC kyrgyz](https://www.bbc.com/kyrgyz) | 2266 | 500 | 500 | 3266 |
Marathi | mr | [BBC marathi](https://www.bbc.com/marathi) | 10903 | 1362 | 1362 | 13627 |
Nepali | np | [BBC nepali](https://www.bbc.com/nepali) | 5808 | 725 | 725 | 7258 |
Oromo | om | [BBC afaanoromoo](https://www.bbc.com/afaanoromoo) | 6063 | 757 | 757 | 7577 |
Pashto | ps | [BBC pashto](https://www.bbc.com/pashto) | 14353 | 1794 | 1794 | 17941 |
Persian | fa | [BBC persian](https://www.bbc.com/persian) | 47251 | 5906 | 5906 | 59063 |
Pidgin`**` | pcm | [BBC pidgin](https://www.bbc.com/pidgin) | 9208 | 1151 | 1151 | 11510 |
Portuguese | pt | [BBC portuguese](https://www.bbc.com/portuguese) | 57402 | 7175 | 7175 | 71752 |
Punjabi | pa | [BBC punjabi](https://www.bbc.com/punjabi) | 8215 | 1026 | 1026 | 10267 |
Russian | ru | [BBC russian](https://www.bbc.com/russian), [BBC ukrainian](https://www.bbc.com/ukrainian) `*` | 62243 | 7780 | 7780 | 77803 |
Scottish Gaelic | gd | [BBC naidheachdan](https://www.bbc.com/naidheachdan) | 1313 | 500 | 500 | 2313 |
Serbian (Cyrillic) | sr | [BBC serbian](https://www.bbc.com/serbian)/cyr | 7275 | 909 | 909 | 9093 |
Serbian (Latin) | sr | [BBC serbian](https://www.bbc.com/serbian)/lat | 7276 | 909 | 909 | 9094 |
Sinhala | si | [BBC sinhala](https://www.bbc.com/sinhala) | 3249 | 500 | 500 | 4249 |
Somali | so | [BBC somali](https://www.bbc.com/somali) | 5962 | 745 | 745 | 7452 |
Spanish | es | [BBC mundo](https://www.bbc.com/mundo) | 38110 | 4763 | 4763 | 47636 |
Swahili | sw | [BBC swahili](https://www.bbc.com/swahili) | 7898 | 987 | 987 | 9872 |
Tamil | ta | [BBC tamil](https://www.bbc.com/tamil) | 16222 | 2027 | 2027 | 20276 |
Telugu | te | [BBC telugu](https://www.bbc.com/telugu) | 10421 | 1302 | 1302 | 13025 |
Thai | th | [BBC thai](https://www.bbc.com/thai) | 6616 | 826 | 826 | 8268 |
Tigrinya | ti | [BBC tigrinya](https://www.bbc.com/tigrinya) | 5451 | 681 | 681 | 6813 |
Turkish | tr | [BBC turkce](https://www.bbc.com/turkce) | 27176 | 3397 | 3397 | 33970 |
Ukrainian | uk | [BBC ukrainian](https://www.bbc.com/ukrainian) | 43201 | 5399 | 5399 | 53999 |
Urdu | ur | [BBC urdu](https://www.bbc.com/urdu) | 67665 | 8458 | 8458 | 84581 |
Uzbek | uz | [BBC uzbek](https://www.bbc.com/uzbek) | 4728 | 590 | 590 | 5908 |
Vietnamese | vi | [BBC vietnamese](https://www.bbc.com/vietnamese) | 32111 | 4013 | 4013 | 40137 |
Welsh | cy | [BBC cymrufyw](https://www.bbc.com/cymrufyw) | 9732 | 1216 | 1216 | 12164 |
Yoruba | yo | [BBC yoruba](https://www.bbc.com/yoruba) | 6350 | 793 | 793 | 7936 |
`*` A lot of articles in BBC Sinhala and BBC Ukrainian were written in English and Russian respectively. They were identified using [Fasttext](https://arxiv.org/abs/1607.01759) and moved accordingly.
`**` West African Pidgin English
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
Traditional abstractive text summarization has been centered around English and other high-resource languages. **XL-Sum** provides a large collection of high-quality article-summary pairs for 45 languages where the languages range from high-resource to extremely low-resource. This enables the research community to explore the summarization capabilities of different models for multiple languages and languages in isolation. We believe the addition of **XL-Sum** to GEM makes the domain of abstractive text summarization more diversified and inclusive to the research community. We hope our efforts in this work will encourage the community to push the boundaries of abstractive text summarization beyond the English language, especially for low and mid-resource languages, bringing technological advances to communities of these languages that have been traditionally under-served.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The summaries are highly concise and abstractive.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Conciseness, abstractiveness, and overall summarization capability.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Conciseness, abstractiveness, and overall summarization capability.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
ROUGE is the de facto evaluation metric used for text summarization. However, it was designed specifically for evaluating English texts. Due to the nature of the metric, scores are heavily dependent on text tokenization / stemming / unnecessary character removal, etc. Some modifications to the original ROUGE evaluation were done such as punctuation only removal, language specific tokenization/stemming to enable reliable comparison of source and target summaries across different scripts.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
State-of-the-art text summarization models are heavily data-driven, i.e., a large number of article-summary pairs are required to train them effectively. As a result, abstractive summarization has centered around the English language, as most large abstractive summarization datasets are available in English only. Though there have been some recent efforts for curating multilingual abstractive summarization datasets, they are limited in terms of the number of languages covered, the number of training samples, or both. To this end, we curate **XL-Sum**, a large-scale abstractive summarization dataset of 1.35 million news articles from 45 languages crawled from the British Broadcasting Corporation website.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Introduce new languages in the english-centric domain of abstractive text summarization and enable both multilingual and per-language summarization.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
British Broadcasting Corporation (BBC) news websites.
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Multiple websites`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The language content was written by professional news editors hired by BBC.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
News
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
We used 'NFKC' normalization on all text instances.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
We designed a crawler to recursively crawl pages starting from the homepage by visiting different article links present in each page visited. We were able to take advantage of the fact that all BBC sites have somewhat similar structures, and were able to scrape articles from all sites. We discarded pages with no textual contents (mostly pages consisting of multimedia contents) before further processing. We designed a number of heuristics to make the extraction effective by carefully examining the HTML structures of the crawled pages:
1. The desired summary must be present within the beginning two paragraphs of an article.
2. The summary paragraph must have some portion of texts in bold format.
3. The summary paragraph may contain some hyperlinks that may not be bold. The proportion of bold texts and hyperlinked texts to the total length of the paragraph in consideration must be at least 95\%.
4. All texts except the summary and the headline must be included in the input text (including image captions).
5. The input text must be at least twice as large as the summary.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
BBC's policy specifies that the text content within its websites can be used for non-commercial research only.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
This dataset introduces summarization corpus for many languages where there weren't any datasets like this curated before.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
Yes
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`research use only`, `non-commercial use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`research use only`, `non-commercial use only`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
Human evaluation showed most languages had a high percentage of good summaries in the upper nineties, almost none of the summaries contained any conflicting information, while about one-third on average had information that was not directly inferrable from the source article. Since generally multiple articles are written regarding an important event, there could be an overlap between the training and evaluation data in terms on content.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
The dataset is limited to news domain only. Hence it wouldn't be advisable to use a model trained on this dataset for summarizing texts from a different domain i.e. literature, scientific text etc. Another pitfall could be hallucinations in the model generated summary.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
ROUGE evaluates the quality of the summary as a whole by considering up to 4-gram overlaps. Therefore, in an article about India if the word "India" in the generated summary gets replaced by "Pakistan" due to model hallucination, the overall score wouldn't be reduced significantly, but the entire meaning could get changed.
|
liuyanchen1015/MULTI_VALUE_mrpc_negative_concord | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 17986
num_examples: 70
- name: train
num_bytes: 39506
num_examples: 150
- name: validation
num_bytes: 6781
num_examples: 26
download_size: 53966
dataset_size: 64273
---
# Dataset Card for "MULTI_VALUE_mrpc_negative_concord"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fun1021183/cvt2_GS3_1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 1131317391.0
num_examples: 8100
- name: test
num_bytes: 2796623.0
num_examples: 20
download_size: 1073448506
dataset_size: 1134114014.0
---
# Dataset Card for "cvt2_GS3_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HedayatiEmad/skin_segmentation_dataset | ---
license: other
---
|
open-llm-leaderboard/details_cookinai__Blitz-v0.2 | ---
pretty_name: Evaluation run of cookinai/Blitz-v0.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cookinai/Blitz-v0.2](https://huggingface.co/cookinai/Blitz-v0.2) 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_cookinai__Blitz-v0.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-09T19:10:33.087092](https://huggingface.co/datasets/open-llm-leaderboard/details_cookinai__Blitz-v0.2/blob/main/results_2024-03-09T19-10-33.087092.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\": 0.6259373955029214,\n\
\ \"acc_stderr\": 0.032583230555173225,\n \"acc_norm\": 0.6323336636080772,\n\
\ \"acc_norm_stderr\": 0.0332497653182241,\n \"mc1\": 0.28518971848225216,\n\
\ \"mc1_stderr\": 0.015805827874454892,\n \"mc2\": 0.42713204934683263,\n\
\ \"mc2_stderr\": 0.014117274045734679\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5537542662116041,\n \"acc_stderr\": 0.01452670554853998,\n\
\ \"acc_norm\": 0.590443686006826,\n \"acc_norm_stderr\": 0.01437035863247244\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6223859788886676,\n\
\ \"acc_stderr\": 0.004837995637638541,\n \"acc_norm\": 0.8300139414459271,\n\
\ \"acc_norm_stderr\": 0.0037485288878381204\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\": 0.042446332383532265,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.042446332383532265\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.03878139888797611,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.03878139888797611\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\
\ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|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-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\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\
\ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055263,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055263\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7548387096774194,\n \"acc_stderr\": 0.024472243840895514,\n \"\
acc_norm\": 0.7548387096774194,\n \"acc_norm_stderr\": 0.024472243840895514\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\
acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7525252525252525,\n \"acc_stderr\": 0.03074630074212451,\n \"\
acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.03074630074212451\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\
\ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.02394672474156397,\n \
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.02394672474156397\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473072,\n \
\ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473072\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n\
\ \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7963302752293578,\n \"acc_stderr\": 0.0172667420876308,\n \"acc_norm\"\
: 0.7963302752293578,\n \"acc_norm_stderr\": 0.0172667420876308\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5324074074074074,\n\
\ \"acc_stderr\": 0.03402801581358966,\n \"acc_norm\": 0.5324074074074074,\n\
\ \"acc_norm_stderr\": 0.03402801581358966\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849323,\n\
\ \"acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849323\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7932489451476793,\n \"acc_stderr\": 0.026361651668389094,\n \
\ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.026361651668389094\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\
\ \"acc_stderr\": 0.03160295143776679,\n \"acc_norm\": 0.6681614349775785,\n\
\ \"acc_norm_stderr\": 0.03160295143776679\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.036959801280988254,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.036959801280988254\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.022801382534597518,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.022801382534597518\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\": 0.8071519795657727,\n\
\ \"acc_stderr\": 0.014108533515757433,\n \"acc_norm\": 0.8071519795657727,\n\
\ \"acc_norm_stderr\": 0.014108533515757433\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6965317919075145,\n \"acc_stderr\": 0.024752411960917202,\n\
\ \"acc_norm\": 0.6965317919075145,\n \"acc_norm_stderr\": 0.024752411960917202\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2860335195530726,\n\
\ \"acc_stderr\": 0.015113972129062136,\n \"acc_norm\": 0.2860335195530726,\n\
\ \"acc_norm_stderr\": 0.015113972129062136\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\
\ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\
\ \"acc_stderr\": 0.026385273703464482,\n \"acc_norm\": 0.684887459807074,\n\
\ \"acc_norm_stderr\": 0.026385273703464482\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.025483115601195455,\n\
\ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.025483115601195455\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \
\ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43285528031290743,\n\
\ \"acc_stderr\": 0.012654565234622864,\n \"acc_norm\": 0.43285528031290743,\n\
\ \"acc_norm_stderr\": 0.012654565234622864\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.02833295951403121,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.02833295951403121\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6633986928104575,\n \"acc_stderr\": 0.019117213911495155,\n \
\ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.019117213911495155\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\": 0.6938775510204082,\n \"acc_stderr\": 0.02950489645459596,\n\
\ \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.02950489645459596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197773,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197773\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368043,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368043\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.28518971848225216,\n\
\ \"mc1_stderr\": 0.015805827874454892,\n \"mc2\": 0.42713204934683263,\n\
\ \"mc2_stderr\": 0.014117274045734679\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7829518547750592,\n \"acc_stderr\": 0.011585871710209401\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.33586050037907506,\n \
\ \"acc_stderr\": 0.013009224714267359\n }\n}\n```"
repo_url: https://huggingface.co/cookinai/Blitz-v0.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|arc:challenge|25_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|gsm8k|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hellaswag|10_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-10-33.087092.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-09T19-10-33.087092.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- '**/details_harness|winogrande|5_2024-03-09T19-10-33.087092.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-09T19-10-33.087092.parquet'
- config_name: results
data_files:
- split: 2024_03_09T19_10_33.087092
path:
- results_2024-03-09T19-10-33.087092.parquet
- split: latest
path:
- results_2024-03-09T19-10-33.087092.parquet
---
# Dataset Card for Evaluation run of cookinai/Blitz-v0.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cookinai/Blitz-v0.2](https://huggingface.co/cookinai/Blitz-v0.2) 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_cookinai__Blitz-v0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-09T19:10:33.087092](https://huggingface.co/datasets/open-llm-leaderboard/details_cookinai__Blitz-v0.2/blob/main/results_2024-03-09T19-10-33.087092.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": 0.6259373955029214,
"acc_stderr": 0.032583230555173225,
"acc_norm": 0.6323336636080772,
"acc_norm_stderr": 0.0332497653182241,
"mc1": 0.28518971848225216,
"mc1_stderr": 0.015805827874454892,
"mc2": 0.42713204934683263,
"mc2_stderr": 0.014117274045734679
},
"harness|arc:challenge|25": {
"acc": 0.5537542662116041,
"acc_stderr": 0.01452670554853998,
"acc_norm": 0.590443686006826,
"acc_norm_stderr": 0.01437035863247244
},
"harness|hellaswag|10": {
"acc": 0.6223859788886676,
"acc_stderr": 0.004837995637638541,
"acc_norm": 0.8300139414459271,
"acc_norm_stderr": 0.0037485288878381204
},
"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": 0.042446332383532265,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.042446332383532265
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6513157894736842,
"acc_stderr": 0.03878139888797611,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.03878139888797611
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
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```
## 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. -->
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#### 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. -->
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### 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]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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agency888/TaoGPT-v2 | ---
language:
- eng
pretty_name: "TaoGPT-7B"
tags:
- spirituality
- tao-science
- quantum-physics
license: mit
---
# TaoGPT-7B Dataset
### General Information
- **Dataset Name**: TaoGPT-7B
- **Version**: 2.0
- **Size**: ~6,000 QA pairs
- **Domain**: Tao Science, Quantum Physics, Spirituality
- **Date Released**: [Release Date]
### Short Description
TaoGPT-7B is a unique dataset developed for an advanced language model, focusing on Tao Science. It includes over 6000 QA responses on quantum physics, spirituality, and Tao wisdom, co-authored by Dr. Zhi Gang Sha and Dr. Rulin Xiu.
### Dataset Structure
Each data entry in TaoGPT-7B follows this structure:
- `question`: A query related to Tao Science or quantum physics.
- `answer`: The response generated based on Tao Science principles.
- `metadata`: Additional information about the entry.
- `content`: The detailed content related to the query.
### Example
```json
{
"question": "What is the relationship between Tao Science and quantum physics?",
"answer": "Tao Science integrates the principles of quantum physics with spiritual wisdom, exploring the interconnectedness of the universe...",
"metadata": "Quantum Physics, Tao Science, Spirituality",
"content": "..."
}
|
liuyanchen1015/MULTI_VALUE_mnli_nomo_existential | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: train
num_bytes: 218957
num_examples: 1020
- name: dev_matched
num_bytes: 5157
num_examples: 26
- name: dev_mismatched
num_bytes: 4733
num_examples: 22
- name: test_matched
num_bytes: 5635
num_examples: 26
- name: test_mismatched
num_bytes: 3802
num_examples: 19
download_size: 148198
dataset_size: 238284
---
# Dataset Card for "MULTI_VALUE_MNLI_nomo_existential"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maxardito/beatbox | ---
pretty_name: "Beatbox Dataset"
tags:
- Audio
- Voice
- Percussion
license: "mit"
arxiv: https://doi.org/10.1007/978-3-031-05981-0_14
---
# Beatbox Dataset
Dataset consisting of isolated beatbox samples. Reimplementation of a dataset from the paper **[BaDumTss: Multi-task
Learning for Beatbox
Transcription](https://link.springer.com/chapter/10.1007/978-3-031-05981-0_14])**
## Citations
Mehta, P., Maheshwari, M., Joshi, B., Chakraborty, T. (2022). BaDumTss: Multi-task
Learning for Beatbox Transcription. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y.,
Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes
in Computer Science(), vol 13282. Springer, Cham.
https://doi.org/10.1007/978-3-031-05981-0_14
|
sarahpann/MATH | ---
dataset_info:
features:
- name: problem
dtype: string
- name: type
dtype: string
- name: level
dtype: string
- name: solution
dtype: string
splits:
- name: test
num_bytes: 3817407
num_examples: 5000
- name: validation
num_bytes: 301338
num_examples: 375
- name: train
num_bytes: 5815376
num_examples: 7125
download_size: 7941761
dataset_size: 9934121
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
ritabratamaiti/sold-alpaca | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 3461985
num_examples: 7500
- name: test
num_bytes: 1168819
num_examples: 2500
download_size: 1396980
dataset_size: 4630804
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
AlekseyKorshuk/hh-chatml | ---
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: do_train
dtype: bool
- name: role
dtype: string
splits:
- name: train
num_bytes: 183931784
num_examples: 169352
download_size: 92779456
dataset_size: 183931784
---
# Dataset Card for "hh-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Kevinger/hub-report-dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: id
dtype: string
- name: score
dtype: float64
- name: title
dtype: string
- name: text
dtype: string
- name: business
dtype: int64
- name: crime
dtype: int64
- name: culture
dtype: int64
- name: entertainment
dtype: int64
- name: politics
dtype: int64
- name: science
dtype: int64
- name: sports
dtype: int64
- name: weather
dtype: int64
splits:
- name: train
num_bytes: 6111039.522317189
num_examples: 2211
- name: test
num_bytes: 1310100.7388414056
num_examples: 474
- name: valid
num_bytes: 1310100.7388414056
num_examples: 474
download_size: 5314452
dataset_size: 8731241.0
---
# Dataset Card for "hub-report-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rohansolo/BB_HindiHinglish-sgpt | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train_sft
num_bytes: 531053832
num_examples: 199137
- name: test_sft
num_bytes: 131987986
num_examples: 49785
download_size: 263983731
dataset_size: 663041818
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
---
|
CATIE-AQ/fquad_fr_prompt_question_generation_with_context | ---
language:
- fr
license:
- cc-by-nc-sa-3.0
size_categories:
- 100k<n<1M
task_categories:
- text-generation
tags:
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- fquad
---
# fquad_fr_prompt_question_generation_with_context
## Summary
**fquad_fr_prompt_question_generation_with_context** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP)).
It contains **574,056** rows that can be used for a question-generation (with context) task.
The original data (without prompts) comes from the dataset [FQuAD]( https://huggingface.co/datasets/fquad) by d'Hoffschmidt et al. and was augmented by questions in SQUAD 2.0 format in the [FrenchQA]( https://huggingface.co/datasets/CATIE-AQ/frenchQA) dataset.
As FQuAD's license does not allow data to be shared, we simply share the prompts used, so that users can recreate the dataset themselves in the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
## Prompts used
### List
24 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.
```
'"'+context+'"\n Générer une question à partir du texte ci-dessus : ',
'"'+context+'"\n Génère une question à partir du texte ci-dessus : ',
'"'+context+'"\n Générez une question à partir du texte ci-dessus : ',
'"'+context+'"\n Trouver une question à partir du texte ci-dessus : ',
'"'+context+'"\n Trouve une question à partir du texte ci-dessus : ',
'"'+context+'"\n Trouvez une question à partir du texte ci-dessus : ',
'"'+context+'"\n Créer une bonne question à partir du texte ci-dessus : ',
'"'+context+'"\n Crée trouver une bonne question à partir du texte ci-dessus : ',
'"'+context+'"\n Créez trouver une bonne question à partir du texte ci-dessus : ',
'"'+context+'"\n Ecrire une bonne question à partir du texte ci-dessus : ',
'"'+context+'"\n Ecris une bonne question à partir du texte ci-dessus : ',
'"'+context+'"\n Ecrivez une bonne question à partir du texte ci-dessus : ',
'Générer une bonne question pour le texte suivant : "'+context+'"',
'Génère une bonne question pour le texte suivant : "'+context+'"',
'Générez une bonne question pour le texte suivant : "'+context+'"',
'Trouver une bonne question pour le texte suivant : "'+context+'"',
'Trouve une bonne question pour le texte suivant : "'+context+'"',
'Trouvez trouver une bonne question pour le texte suivant : "'+context+'"',
'Créer une bonne question pour le texte suivant : "'+context+'"',
'Crée trouver une bonne question pour le texte suivant : "'+context+'"',
'Créez trouver une bonne question pour le texte suivant : "'+context+'"',
'Ecrire une bonne question pour le texte suivant : "'+context+'"',
'Ecris une bonne question pour le texte suivant : "'+context+'"',
'Ecrivez une bonne question pour le texte suivant : "'+context+'"'
```
# Splits
- `train` with 497,544 samples
- `valid` with 76,512 samples
- no test split
# How to use?
This repository doesn't contain any data.
# Citation
## Original data
> @ARTICLE{2020arXiv200206071
author = {Martin, d'Hoffschmidt and Maxime, Vidal and Wacim, Belblidia and Tom, Brendlé},
title = "{FQuAD: French Question Answering Dataset}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = "2020",
month = "Feb",
eid = {arXiv:2002.06071},
pages = {arXiv:2002.06071},
archivePrefix = {arXiv},
eprint = {2002.06071},
primaryClass = {cs.CL}
}
## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,
author = { {Centre Aquitain des Technologies de l'Information et Electroniques} },
title = { DFP (Revision 1d24c09) },
year = 2023,
url = { https://huggingface.co/datasets/CATIE-AQ/DFP },
doi = { 10.57967/hf/1200 },
publisher = { Hugging Face }
}
## License
CC BY-NC-SA 3.0 |
4turkuaz/machine-generated-text-detection | ---
license: mit
---
|
dpaul93/FB-email-market | ---
dataset_info:
features:
- name: product
dtype: string
- name: description
dtype: string
- name: marketing_email
dtype: string
splits:
- name: train
num_bytes: 20754
num_examples: 10
download_size: 27429
dataset_size: 20754
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "FB-email-market"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Parth/code-llama-1 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 897590
num_examples: 1000
download_size: 412703
dataset_size: 897590
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zolak/twitter_dataset_79_1713123372 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 269729
num_examples: 640
download_size: 133765
dataset_size: 269729
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Cherishh/ner-slu-aug-v1 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 3933942
num_examples: 17604
- name: val
num_bytes: 439860
num_examples: 1957
- name: test
num_bytes: 163660
num_examples: 749
download_size: 649747
dataset_size: 4537462
---
# Dataset Card for "ner-slu-aug-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
qq67878980/Niggermaxxx_benchmark | ---
license: cc
---
A benchmark for LLMs, real tests for real people, and real usecases. THE benchmark for the ages. |
adamo1139/PS_AD_Office365_02 | ---
license: apache-2.0
---
Second version of the synthetic dataset created by putting a part of a textbook in the context of 7B model and then asking the model
to create a few questions and answers related to the dataset.
It contains information about PowerShell basics, Office 365 basics and Active Directory/GPO basics. |
open-llm-leaderboard/details_ResplendentAI__Luna-2x7B-MoE | ---
pretty_name: Evaluation run of ResplendentAI/Luna-2x7B-MoE
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ResplendentAI/Luna-2x7B-MoE](https://huggingface.co/ResplendentAI/Luna-2x7B-MoE)\
\ 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_ResplendentAI__Luna-2x7B-MoE\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-31T06:07:43.229531](https://huggingface.co/datasets/open-llm-leaderboard/details_ResplendentAI__Luna-2x7B-MoE/blob/main/results_2024-03-31T06-07-43.229531.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\": 0.6489723051206086,\n\
\ \"acc_stderr\": 0.03212812269467186,\n \"acc_norm\": 0.6492343395648444,\n\
\ \"acc_norm_stderr\": 0.032788826728238844,\n \"mc1\": 0.5312117503059975,\n\
\ \"mc1_stderr\": 0.017469364874577523,\n \"mc2\": 0.6865517424879445,\n\
\ \"mc2_stderr\": 0.015117104750583922\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6936860068259386,\n \"acc_stderr\": 0.013470584417276514,\n\
\ \"acc_norm\": 0.71160409556314,\n \"acc_norm_stderr\": 0.013238394422428175\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7173869747062338,\n\
\ \"acc_stderr\": 0.004493495872000115,\n \"acc_norm\": 0.8811989643497311,\n\
\ \"acc_norm_stderr\": 0.003228929916459684\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\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n\
\ \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\
\ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\
\ \"acc_stderr\": 0.035149425512674394,\n \"acc_norm\": 0.6936416184971098,\n\
\ \"acc_norm_stderr\": 0.035149425512674394\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266344,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266344\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\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\
\ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778405,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778405\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026704,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026704\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.02247325333276877,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.02247325333276877\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657262,\n\
\ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657262\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7100840336134454,\n \"acc_stderr\": 0.029472485833136094,\n\
\ \"acc_norm\": 0.7100840336134454,\n \"acc_norm_stderr\": 0.029472485833136094\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374308,\n \"\
acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374308\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8382352941176471,\n \"acc_stderr\": 0.02584501798692692,\n \"\
acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.02584501798692692\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8270042194092827,\n \"acc_stderr\": 0.024621562866768424,\n \
\ \"acc_norm\": 0.8270042194092827,\n \"acc_norm_stderr\": 0.024621562866768424\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\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\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281365,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281365\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \
\ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526094\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n\
\ \"acc_stderr\": 0.013586619219903335,\n \"acc_norm\": 0.8250319284802043,\n\
\ \"acc_norm_stderr\": 0.013586619219903335\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526501,\n\
\ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526501\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4748603351955307,\n\
\ \"acc_stderr\": 0.01670135084268263,\n \"acc_norm\": 0.4748603351955307,\n\
\ \"acc_norm_stderr\": 0.01670135084268263\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.02526169121972948,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.02526169121972948\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \
\ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4621903520208605,\n\
\ \"acc_stderr\": 0.012733671880342506,\n \"acc_norm\": 0.4621903520208605,\n\
\ \"acc_norm_stderr\": 0.012733671880342506\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6985294117647058,\n \"acc_stderr\": 0.027875982114273168,\n\
\ \"acc_norm\": 0.6985294117647058,\n \"acc_norm_stderr\": 0.027875982114273168\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6519607843137255,\n \"acc_stderr\": 0.019270998708223977,\n \
\ \"acc_norm\": 0.6519607843137255,\n \"acc_norm_stderr\": 0.019270998708223977\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\
\ \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n\
\ \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5312117503059975,\n\
\ \"mc1_stderr\": 0.017469364874577523,\n \"mc2\": 0.6865517424879445,\n\
\ \"mc2_stderr\": 0.015117104750583922\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8326756116811366,\n \"acc_stderr\": 0.010490608806828075\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6315390447308568,\n \
\ \"acc_stderr\": 0.013287342651674569\n }\n}\n```"
repo_url: https://huggingface.co/ResplendentAI/Luna-2x7B-MoE
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|arc:challenge|25_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|gsm8k|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hellaswag|10_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-31T06-07-43.229531.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-31T06-07-43.229531.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- '**/details_harness|winogrande|5_2024-03-31T06-07-43.229531.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-31T06-07-43.229531.parquet'
- config_name: results
data_files:
- split: 2024_03_31T06_07_43.229531
path:
- results_2024-03-31T06-07-43.229531.parquet
- split: latest
path:
- results_2024-03-31T06-07-43.229531.parquet
---
# Dataset Card for Evaluation run of ResplendentAI/Luna-2x7B-MoE
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [ResplendentAI/Luna-2x7B-MoE](https://huggingface.co/ResplendentAI/Luna-2x7B-MoE) 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_ResplendentAI__Luna-2x7B-MoE",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-31T06:07:43.229531](https://huggingface.co/datasets/open-llm-leaderboard/details_ResplendentAI__Luna-2x7B-MoE/blob/main/results_2024-03-31T06-07-43.229531.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": 0.6489723051206086,
"acc_stderr": 0.03212812269467186,
"acc_norm": 0.6492343395648444,
"acc_norm_stderr": 0.032788826728238844,
"mc1": 0.5312117503059975,
"mc1_stderr": 0.017469364874577523,
"mc2": 0.6865517424879445,
"mc2_stderr": 0.015117104750583922
},
"harness|arc:challenge|25": {
"acc": 0.6936860068259386,
"acc_stderr": 0.013470584417276514,
"acc_norm": 0.71160409556314,
"acc_norm_stderr": 0.013238394422428175
},
"harness|hellaswag|10": {
"acc": 0.7173869747062338,
"acc_stderr": 0.004493495872000115,
"acc_norm": 0.8811989643497311,
"acc_norm_stderr": 0.003228929916459684
},
"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": 0.6222222222222222,
"acc_stderr": 0.04188307537595853,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595853
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7171052631578947,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.7171052631578947,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.028152837942493864,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.028152837942493864
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6936416184971098,
"acc_stderr": 0.035149425512674394,
"acc_norm": 0.6936416184971098,
"acc_norm_stderr": 0.035149425512674394
},
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"harness|truthfulqa:mc|0": {
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"acc_stderr": 0.010490608806828075
},
"harness|gsm8k|5": {
"acc": 0.6315390447308568,
"acc_stderr": 0.013287342651674569
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
mikoube/decklink_test | ---
license: apache-2.0
---
|
Namitoo/gui | ---
language:
- ja
annotation_creators:
- abf
- 1234
models:
- 12
--- |
TigerResearch/tigerbot-HC3-zh-12k | ---
license: cc-by-sa-4.0
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 基于公开的HC3数据集加工生成的常识问答sft数据集
<p align="center" width="40%">
原始来源：[https://huggingface.co/datasets/Hello-SimpleAI/HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3)
If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same.
If not, they follow CC-BY-SA license.
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-HC3-zh-12k')
```
|
jdchang/hh_static_rlhf_pythia | ---
dataset_info:
features:
- name: query
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: reject
dtype: string
- name: query_response
dtype: string
- name: query_reject
dtype: string
- name: query_token
sequence: int64
- name: response_token
sequence: int64
- name: chosen_token
sequence: int64
- name: reject_token
sequence: int64
- name: query_response_token
sequence: int64
- name: query_reject_token
sequence: int64
splits:
- name: train
num_bytes: 4555916603
num_examples: 126057
- name: val
num_bytes: 240353762
num_examples: 6650
- name: test
num_bytes: 255972017
num_examples: 7084
download_size: 529242090
dataset_size: 5052242382
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
cestwc/hdb0110 | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 16067.0
num_examples: 110
download_size: 13149
dataset_size: 16067.0
---
# Dataset Card for "hdb0110"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
indicbench/truthfulqa_ml | ---
dataset_info:
- config_name: default
features:
- name: _data_files
list:
- name: filename
dtype: string
- name: _fingerprint
dtype: string
- name: _format_columns
dtype: 'null'
- name: _format_kwargs
dtype: string
- name: _format_type
dtype: 'null'
- name: _output_all_columns
dtype: bool
- name: _split
dtype: 'null'
splits:
- name: train
num_bytes: 119
num_examples: 2
download_size: 3715
dataset_size: 119
- config_name: generation
features:
- name: type
dtype: string
- name: category
dtype: string
- name: question
dtype: string
- name: best_answer
dtype: string
- name: correct_answers
sequence: string
- name: incorrect_answers
sequence: string
- name: source
dtype: string
splits:
- name: validation
num_bytes: 1261180
num_examples: 817
download_size: 379018
dataset_size: 1261180
- config_name: multiple_choice
features:
- name: question
dtype: string
- name: mc1_targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int64
- name: mc2_targets
struct:
- name: choices
sequence: string
- name: labels
sequence: int64
splits:
- name: validation
num_bytes: 1762133
num_examples: 817
download_size: 492747
dataset_size: 1762133
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: generation
data_files:
- split: validation
path: generation/validation-*
- config_name: multiple_choice
data_files:
- split: validation
path: multiple_choice/validation-*
---
|
Braddy/alpaca_customised | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 7785502.647554285
num_examples: 10004
download_size: 4693114
dataset_size: 7785502.647554285
---
# Dataset Card for "alpaca_customised"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_DreadPoor__JustToSuffer-7B-slerp | ---
pretty_name: Evaluation run of DreadPoor/JustToSuffer-7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [DreadPoor/JustToSuffer-7B-slerp](https://huggingface.co/DreadPoor/JustToSuffer-7B-slerp)\
\ 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_DreadPoor__JustToSuffer-7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-17T16:45:59.965925](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__JustToSuffer-7B-slerp/blob/main/results_2024-02-17T16-45-59.965925.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\": 0.6495053299013857,\n\
\ \"acc_stderr\": 0.03213728048297641,\n \"acc_norm\": 0.6511170368490796,\n\
\ \"acc_norm_stderr\": 0.03278191957728323,\n \"mc1\": 0.4541003671970624,\n\
\ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.6269022526171036,\n\
\ \"mc2_stderr\": 0.015516860373603584\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6604095563139932,\n \"acc_stderr\": 0.013839039762820167,\n\
\ \"acc_norm\": 0.689419795221843,\n \"acc_norm_stderr\": 0.013522292098053067\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7030472017526389,\n\
\ \"acc_stderr\": 0.00455981758918207,\n \"acc_norm\": 0.8678550089623581,\n\
\ \"acc_norm_stderr\": 0.003379562298387565\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\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.035868792800803406,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.035868792800803406\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\
\ \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n\
\ \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.049135952012744975,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.049135952012744975\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\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\"\
: 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\
acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083525,\n \"\
acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083525\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"\
acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\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\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\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\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768776,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768776\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971125,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971125\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.02995382389188704,\n \
\ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.02995382389188704\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\"\
: 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700476,\n \"\
acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700476\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\
acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"\
acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.030636591348699813,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.030636591348699813\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728744,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728744\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\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\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\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\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.822477650063857,\n\
\ \"acc_stderr\": 0.013664230995834838,\n \"acc_norm\": 0.822477650063857,\n\
\ \"acc_norm_stderr\": 0.013664230995834838\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069363,\n\
\ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069363\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.40893854748603353,\n\
\ \"acc_stderr\": 0.016442830654715544,\n \"acc_norm\": 0.40893854748603353,\n\
\ \"acc_norm_stderr\": 0.016442830654715544\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n\
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712992,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712992\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.02979071924382972,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.02979071924382972\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46088657105606257,\n\
\ \"acc_stderr\": 0.012731102790504515,\n \"acc_norm\": 0.46088657105606257,\n\
\ \"acc_norm_stderr\": 0.012731102790504515\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6985294117647058,\n \"acc_stderr\": 0.027875982114273168,\n\
\ \"acc_norm\": 0.6985294117647058,\n \"acc_norm_stderr\": 0.027875982114273168\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6584967320261438,\n \"acc_stderr\": 0.019184639328092487,\n \
\ \"acc_norm\": 0.6584967320261438,\n \"acc_norm_stderr\": 0.019184639328092487\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.02879518557429129,\n\
\ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.02879518557429129\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4541003671970624,\n\
\ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.6269022526171036,\n\
\ \"mc2_stderr\": 0.015516860373603584\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.011235328382625856\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5974222896133434,\n \
\ \"acc_stderr\": 0.013508523063663423\n }\n}\n```"
repo_url: https://huggingface.co/DreadPoor/JustToSuffer-7B-slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|arc:challenge|25_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|gsm8k|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hellaswag|10_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T16-45-59.965925.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T16-45-59.965925.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- '**/details_harness|winogrande|5_2024-02-17T16-45-59.965925.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-17T16-45-59.965925.parquet'
- config_name: results
data_files:
- split: 2024_02_17T16_45_59.965925
path:
- results_2024-02-17T16-45-59.965925.parquet
- split: latest
path:
- results_2024-02-17T16-45-59.965925.parquet
---
# Dataset Card for Evaluation run of DreadPoor/JustToSuffer-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [DreadPoor/JustToSuffer-7B-slerp](https://huggingface.co/DreadPoor/JustToSuffer-7B-slerp) 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_DreadPoor__JustToSuffer-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T16:45:59.965925](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__JustToSuffer-7B-slerp/blob/main/results_2024-02-17T16-45-59.965925.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": 0.6495053299013857,
"acc_stderr": 0.03213728048297641,
"acc_norm": 0.6511170368490796,
"acc_norm_stderr": 0.03278191957728323,
"mc1": 0.4541003671970624,
"mc1_stderr": 0.017429593091323522,
"mc2": 0.6269022526171036,
"mc2_stderr": 0.015516860373603584
},
"harness|arc:challenge|25": {
"acc": 0.6604095563139932,
"acc_stderr": 0.013839039762820167,
"acc_norm": 0.689419795221843,
"acc_norm_stderr": 0.013522292098053067
},
"harness|hellaswag|10": {
"acc": 0.7030472017526389,
"acc_stderr": 0.00455981758918207,
"acc_norm": 0.8678550089623581,
"acc_norm_stderr": 0.003379562298387565
},
"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": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544067,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544067
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.035868792800803406,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.035868792800803406
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.0355068398916558,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.0355068398916558
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.049135952012744975,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.049135952012744975
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146268,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146268
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.025279850397404904,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.025279850397404904
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.0442626668137991,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.0442626668137991
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7903225806451613,
"acc_stderr": 0.023157879349083525,
"acc_norm": 0.7903225806451613,
"acc_norm_stderr": 0.023157879349083525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252609,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252609
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"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": 0.8911917098445595,
"acc_stderr": 0.022473253332768776,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768776
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6692307692307692,
"acc_stderr": 0.023854795680971125,
"acc_norm": 0.6692307692307692,
"acc_norm_stderr": 0.023854795680971125
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.02882088466625326,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.02882088466625326
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6932773109243697,
"acc_stderr": 0.02995382389188704,
"acc_norm": 0.6932773109243697,
"acc_norm_stderr": 0.02995382389188704
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8550458715596331,
"acc_stderr": 0.015094215699700476,
"acc_norm": 0.8550458715596331,
"acc_norm_stderr": 0.015094215699700476
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5324074074074074,
"acc_stderr": 0.03402801581358966,
"acc_norm": 0.5324074074074074,
"acc_norm_stderr": 0.03402801581358966
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8382352941176471,
"acc_stderr": 0.025845017986926917,
"acc_norm": 0.8382352941176471,
"acc_norm_stderr": 0.025845017986926917
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.02553010046023349,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.02553010046023349
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7040358744394619,
"acc_stderr": 0.030636591348699813,
"acc_norm": 0.7040358744394619,
"acc_norm_stderr": 0.030636591348699813
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7557251908396947,
"acc_stderr": 0.03768335959728744,
"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.03768335959728744
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228733,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228733
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.032262193772867744,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.032262193772867744
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.02093019318517933,
"acc_norm": 0.8846153846153846,
"acc_norm_stderr": 0.02093019318517933
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.73,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.822477650063857,
"acc_stderr": 0.013664230995834838,
"acc_norm": 0.822477650063857,
"acc_norm_stderr": 0.013664230995834838
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7398843930635838,
"acc_stderr": 0.023618678310069363,
"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.023618678310069363
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.40893854748603353,
"acc_stderr": 0.016442830654715544,
"acc_norm": 0.40893854748603353,
"acc_norm_stderr": 0.016442830654715544
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7516339869281046,
"acc_stderr": 0.02473998135511359,
"acc_norm": 0.7516339869281046,
"acc_norm_stderr": 0.02473998135511359
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7170418006430869,
"acc_stderr": 0.025583062489984813,
"acc_norm": 0.7170418006430869,
"acc_norm_stderr": 0.025583062489984813
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7469135802469136,
"acc_stderr": 0.024191808600712992,
"acc_norm": 0.7469135802469136,
"acc_norm_stderr": 0.024191808600712992
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.475177304964539,
"acc_stderr": 0.02979071924382972,
"acc_norm": 0.475177304964539,
"acc_norm_stderr": 0.02979071924382972
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46088657105606257,
"acc_stderr": 0.012731102790504515,
"acc_norm": 0.46088657105606257,
"acc_norm_stderr": 0.012731102790504515
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6985294117647058,
"acc_stderr": 0.027875982114273168,
"acc_norm": 0.6985294117647058,
"acc_norm_stderr": 0.027875982114273168
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6584967320261438,
"acc_stderr": 0.019184639328092487,
"acc_norm": 0.6584967320261438,
"acc_norm_stderr": 0.019184639328092487
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7183673469387755,
"acc_stderr": 0.02879518557429129,
"acc_norm": 0.7183673469387755,
"acc_norm_stderr": 0.02879518557429129
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.025538433368578337,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.025538433368578337
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.03588702812826371,
"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5120481927710844,
"acc_stderr": 0.03891364495835817,
"acc_norm": 0.5120481927710844,
"acc_norm_stderr": 0.03891364495835817
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8421052631578947,
"acc_stderr": 0.027966785859160893,
"acc_norm": 0.8421052631578947,
"acc_norm_stderr": 0.027966785859160893
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4541003671970624,
"mc1_stderr": 0.017429593091323522,
"mc2": 0.6269022526171036,
"mc2_stderr": 0.015516860373603584
},
"harness|winogrande|5": {
"acc": 0.8003157063930545,
"acc_stderr": 0.011235328382625856
},
"harness|gsm8k|5": {
"acc": 0.5974222896133434,
"acc_stderr": 0.013508523063663423
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
ankity09/guanaco-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966694
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cyrilzhang/wiki-bpe-48k | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 20505990100
num_examples: 5001461
- name: test
num_bytes: 206143900
num_examples: 50279
download_size: 9547305598
dataset_size: 20712134000
---
# Dataset Card for "wiki-bpe-48k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Carlisle/msmacro-test | ---
license: mit
---
|
cakiki/batchfile_paths | ---
dataset_info:
features:
- name: repository_name
dtype: string
splits:
- name: train
num_bytes: 11616420
num_examples: 423086
download_size: 8986923
dataset_size: 11616420
---
# Dataset Card for "batchfile_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
VTaPo/mulchoice_math | ---
license: cc
---
|
YuehHanChen/sst2_finetuning_dataset_few_shot_1 | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: answer
dtype: int64
splits:
- name: train
num_bytes: 35588462
num_examples: 68221
download_size: 5218065
dataset_size: 35588462
---
# Dataset Card for "sst2_finetuning_dataset_few_shot_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-xsum-9cdb3b8b-10115340 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: eslamxm/mbert2mbert-finetune-fa
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: eslamxm/mbert2mbert-finetune-fa
* Dataset: xsum
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@iserralv](https://huggingface.co/iserralv) for evaluating this model. |
Devanshj7/Company-dataset | ---
language:
- en
license: apache-2.0
---
|
yoshitomo-matsubara/srsd-feynman_easy | ---
pretty_name: SRSD-Feynman (Easy)
annotations_creators:
- expert
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- tabular-regression
task_ids: []
---
# Dataset Card for SRSD-Feynman (Easy set)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/omron-sinicx/srsd-benchmark
- **Paper:** [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://arxiv.org/abs/2206.10540)
- **Point of Contact:** [Yoshitaka Ushiku](mailto:yoshitaka.ushiku@sinicx.com)
### Dataset Summary
Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.
This is the ***Easy set*** of our SRSD-Feynman datasets, which consists of the following 30 different physics formulas:
[](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_easy/resolve/main/problem_table.pdf)
More details of these datasets are provided in [the paper and its supplementary material](https://openreview.net/forum?id=qrUdrXsiXX).
### Supported Tasks and Leaderboards
Symbolic Regression
## Dataset Structure
### Data Instances
Tabular data + Ground-truth equation per equation
Tabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables (`num_variables`) varies from equation to equation.
Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.
### Data Fields
For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)
### Data Splits
- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation
## Dataset Creation
### Curation Rationale
We chose target equations based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html).
### Annotations
#### Annotation process
We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.
#### Who are the annotators?
The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.
### Discussion of Biases
Our choices of target equations are based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html), which are focused on a field of Physics.
### Other Known Limitations
Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})
## Additional Information
### Dataset Curators
The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
[[OpenReview](https://openreview.net/forum?id=qrUdrXsiXX)] [[Video](https://www.youtube.com/watch?v=MmeOXuUUAW0)] [[Preprint](https://arxiv.org/abs/2206.10540)]
```bibtex
@article{matsubara2024rethinking,
title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka},
journal={Journal of Data-centric Machine Learning Research},
year={2024},
url={https://openreview.net/forum?id=qrUdrXsiXX}
}
```
### Contributions
Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)
|
facebook/voxpopuli | ---
annotations_creators: []
language:
- en
- de
- fr
- es
- pl
- it
- ro
- hu
- cs
- nl
- fi
- hr
- sk
- sl
- et
- lt
language_creators: []
license:
- cc0-1.0
- other
multilinguality:
- multilingual
pretty_name: VoxPopuli
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for Voxpopuli
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/facebookresearch/voxpopuli
- **Repository:** https://github.com/facebookresearch/voxpopuli
- **Paper:** https://arxiv.org/abs/2101.00390
- **Point of Contact:** [changhan@fb.com](mailto:changhan@fb.com), [mriviere@fb.com](mailto:mriviere@fb.com), [annl@fb.com](mailto:annl@fb.com)
### Dataset Summary
VoxPopuli is a large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation.
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home). We acknowledge the European Parliament for creating and sharing these materials.
This implementation contains transcribed speech data for 18 languages.
It also contains 29 hours of transcribed speech data of non-native English intended for research in ASR for accented speech (15 L2 accents)
### Example usage
VoxPopuli contains labelled data for 18 languages. To load a specific language pass its name as a config name:
```python
from datasets import load_dataset
voxpopuli_croatian = load_dataset("facebook/voxpopuli", "hr")
```
To load all the languages in a single dataset use "multilang" config name:
```python
voxpopuli_all = load_dataset("facebook/voxpopuli", "multilang")
```
To load a specific set of languages, use "multilang" config name and pass a list of required languages to `languages` parameter:
```python
voxpopuli_slavic = load_dataset("facebook/voxpopuli", "multilang", languages=["hr", "sk", "sl", "cs", "pl"])
```
To load accented English data, use "en_accented" config name:
```python
voxpopuli_accented = load_dataset("facebook/voxpopuli", "en_accented")
```
**Note that L2 English subset contains only `test` split.**
### Supported Tasks and Leaderboards
* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
Accented English subset can also be used for research in ASR for accented speech (15 L2 accents)
### Languages
VoxPopuli contains labelled (transcribed) data for 18 languages:
| Language | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
|:---:|:---:|:---:|:---:|:---:|
| English | En | 543 | 1313 | 4.8M |
| German | De | 282 | 531 | 2.3M |
| French | Fr | 211 | 534 | 2.1M |
| Spanish | Es | 166 | 305 | 1.6M |
| Polish | Pl | 111 | 282 | 802K |
| Italian | It | 91 | 306 | 757K |
| Romanian | Ro | 89 | 164 | 739K |
| Hungarian | Hu | 63 | 143 | 431K |
| Czech | Cs | 62 | 138 | 461K |
| Dutch | Nl | 53 | 221 | 488K |
| Finnish | Fi | 27 | 84 | 160K |
| Croatian | Hr | 43 | 83 | 337K |
| Slovak | Sk | 35 | 96 | 270K |
| Slovene | Sl | 10 | 45 | 76K |
| Estonian | Et | 3 | 29 | 18K |
| Lithuanian | Lt | 2 | 21 | 10K |
| Total | | 1791 | 4295 | 15M |
Accented speech transcribed data has 15 various L2 accents:
| Accent | Code | Transcribed Hours | Transcribed Speakers |
|:---:|:---:|:---:|:---:|
| Dutch | en_nl | 3.52 | 45 |
| German | en_de | 3.52 | 84 |
| Czech | en_cs | 3.30 | 26 |
| Polish | en_pl | 3.23 | 33 |
| French | en_fr | 2.56 | 27 |
| Hungarian | en_hu | 2.33 | 23 |
| Finnish | en_fi | 2.18 | 20 |
| Romanian | en_ro | 1.85 | 27 |
| Slovak | en_sk | 1.46 | 17 |
| Spanish | en_es | 1.42 | 18 |
| Italian | en_it | 1.11 | 15 |
| Estonian | en_et | 1.08 | 6 |
| Lithuanian | en_lt | 0.65 | 7 |
| Croatian | en_hr | 0.42 | 9 |
| Slovene | en_sl | 0.25 | 7 |
## Dataset Structure
### Data Instances
```python
{
'audio_id': '20180206-0900-PLENARY-15-hr_20180206-16:10:06_5',
'language': 11, # "hr"
'audio': {
'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/train_part_0/20180206-0900-PLENARY-15-hr_20180206-16:10:06_5.wav',
'array': array([-0.01434326, -0.01055908, 0.00106812, ..., 0.00646973], dtype=float32),
'sampling_rate': 16000
},
'raw_text': '',
'normalized_text': 'poast genitalnog sakaenja ena u europi tek je jedna od manifestacija takve tetne politike.',
'gender': 'female',
'speaker_id': '119431',
'is_gold_transcript': True,
'accent': 'None'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `language` (datasets.ClassLabel) - numerical id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `raw_text` (string) - original (orthographic) audio segment text
* `normalized_text` (string) - normalized audio segment transcription
* `gender` (string) - gender of speaker
* `speaker_id` (string) - id of speaker
* `is_gold_transcript` (bool) - ?
* `accent` (string) - type of accent, for example "en_lt", if applicable, else "None".
### Data Splits
All configs (languages) except for accented English contain data in three splits: train, validation and test. Accented English `en_accented` config contains only test split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The raw data is collected from 2009-2020 [European Parliament event recordings](https://multimedia.europarl.europa.eu/en/home)
#### Initial Data Collection and Normalization
The VoxPopuli transcribed set comes from aligning the full-event source speech audio with the transcripts for plenary sessions. Official timestamps
are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture
of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps,
we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation.
Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available.
The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a
maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts.
The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data.
The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment.
We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER).
#### Who are the source language producers?
Speakers are participants of the European Parliament events, many of them are EU officials.
### 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
Gender speakers distribution is imbalanced, percentage of female speakers is mostly lower than 50% across languages, with the minimum of 15% for the Lithuanian language data.
VoxPopuli includes all available speeches from the 2009-2020 EP events without any selections on the topics or speakers.
The speech contents represent the standpoints of the speakers in the EP events, many of which are EU officials.
### Other Known Limitations
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is distributet under CC0 license, see also [European Parliament's legal notice](https://www.europarl.europa.eu/legal-notice/en/) for the raw data.
### Citation Information
Please cite this paper:
```bibtex
@inproceedings{wang-etal-2021-voxpopuli,
title = "{V}ox{P}opuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation",
author = "Wang, Changhan and
Riviere, Morgane and
Lee, Ann and
Wu, Anne and
Talnikar, Chaitanya and
Haziza, Daniel and
Williamson, Mary and
Pino, Juan and
Dupoux, Emmanuel",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.80",
pages = "993--1003",
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
|
ayyuce/klingon_chat | ---
license: gpl-3.0
---
|
CyberHarem/m1897_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of m1897/M1897/M1897 (Girls' Frontline)
This is the dataset of m1897/M1897/M1897 (Girls' Frontline), containing 21 images and their tags.
The core tags of this character are `blue_eyes, short_hair, blonde_hair, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 21 | 23.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1897_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 21 | 17.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1897_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 46 | 29.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1897_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 21 | 22.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1897_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 46 | 36.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/m1897_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/m1897_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | 1girl, solo, gun, boots, gloves, open_mouth, looking_at_viewer, full_body, frills, thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | gun | boots | gloves | open_mouth | looking_at_viewer | full_body | frills | thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:------|:--------|:---------|:-------------|:--------------------|:------------|:---------|:-------------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X |
|
Isamu136/big-animal-dataset-with-embedding | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
- name: l14_embeddings
sequence: float32
- name: moco_vitb_imagenet_embeddings
sequence: float32
- name: moco_vitb_imagenet_embeddings_without_last_layer
sequence: float32
splits:
- name: train
num_bytes: 2125655956.375
num_examples: 62149
download_size: 2238679414
dataset_size: 2125655956.375
---
|
freshpearYoon/v3_train_free_concat_11 | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 3842579056
num_examples: 2500
download_size: 1812781254
dataset_size: 3842579056
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
pankajemplay/mistral-intent-data-1615 | ---
dataset_info:
features:
- name: User Query
dtype: string
- name: Intent
dtype: string
- name: id type
dtype: string
- name: id value
dtype: string
- name: id slot filled
dtype: bool
- name: Task
dtype: string
- name: task slot filled
dtype: bool
- name: Bot Response
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1203158
num_examples: 1615
download_size: 257325
dataset_size: 1203158
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mistral-intent-data-1615"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Gabriel1322/joaocaetano500epochs | ---
license: openrail
---
|
Seanxh/twitter_dataset_1713161922 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 28178
num_examples: 66
download_size: 14481
dataset_size: 28178
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-xsum-default-1c6815-27497144912 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: pszemraj/led-base-book-summary
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/led-base-book-summary
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kaprerna135](https://huggingface.co/kaprerna135) for evaluating this model. |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-41000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 667090
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cl-nagoya/nu-mnli | ---
language:
- ja
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
multilinguality:
- bilingual
size_categories:
- 100K<n<1M
source_datasets:
- nyu-mll/multi_nli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
pretty_name: Nagoya University MNLI
license_details: Open Portion of the American National Corpus
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: genre
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 77409029.55918737
num_examples: 296912
download_size: 46081796
dataset_size: 77409029.55918737
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
## Translation Code
We used vLLM for a faster, batched generation.
```python
import datasets as ds
from vllm import LLM, SamplingParams, RequestOutput
from transformers import AutoTokenizer
model_path = "hoge/fuga"
dataset: ds.Dataset = ds.load_dataset("nyu-mll/multi_nli", split="train")
dataset = dataset.select_columns(["premise", "hypothesis", "label", "genre"])
llm = LLM(
model=model_path,
quantization=None,
dtype="bfloat16",
tensor_parallel_size=4,
enforce_eager=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# temperature must be 0 when using beam search
sampling_params = SamplingParams(
temperature=0,
use_beam_search=True,
best_of=5,
max_tokens=256,
repetition_penalty=1.05,
length_penalty=2,
)
def formatting_func(sentences: list[str]):
output_texts = []
for sentence in sentences:
messages = [
{
"role": "user",
"content": "Translate this English sentence into Japanese.\n" + sentence.replace("\n", " ").strip(),
},
]
output_texts.append(tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True))
return output_texts
print(f"Processing Dataset: {len(dataset)} samples")
premises_en = dataset["premise"]
hypotheses_en = dataset["hypothesis"]
prompts = list(set(premises_en + hypotheses_en))
formatted_prompts = formatting_func(prompts)
input_ids = tokenizer(formatted_prompts, add_special_tokens=False).input_ids
responses: list[RequestOutput] = llm.generate(prompt_token_ids=input_ids, sampling_params=sampling_params)
output_texts: list[str] = [response.outputs[0].text.strip() for response in responses]
translation_dict = {en: ja.strip() for en, ja in zip(prompts, output_texts)}
def mapping(x: dict):
return {
"premise_ja": translation_dict[x["premise"]],
"hypothesis_ja": translation_dict[x["hypothesis"]],
}
dataset = dataset.map(mapping, num_proc=8)
dataset = dataset.rename_columns({"premise": "premise_en", "hypothesis": "hypothesis_en"})
dataset = dataset.select_columns(
[
"premise_ja",
"hypothesis_ja",
"label",
"premise_en",
"hypothesis_en",
"genre",
]
)
dataset.push_to_hub("hoge/fuga")
```
|
Antonio49/ULTIMAPRUEBA | ---
license: apache-2.0
---
|
erfanzar/Zeus-v0.1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: float64
- name: source
dtype: string
- name: category
dtype: string
- name: model
dtype: 'null'
splits:
- name: train
num_bytes: 651231416
num_examples: 386175
download_size: 327788195
dataset_size: 651231416
---
# Dataset Card for "Zeus-v0.1"
this dataset is chosen from parts of `teknium/OpenHermes-2.5` that contains system message or system prompt and include some chosen math problems. |
open-llm-leaderboard/details_ericpolewski__ASTS-PFAF | ---
pretty_name: Evaluation run of ericpolewski/ASTS-PFAF
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ericpolewski/ASTS-PFAF](https://huggingface.co/ericpolewski/ASTS-PFAF) 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_ericpolewski__ASTS-PFAF\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-10T08:56:33.730792](https://huggingface.co/datasets/open-llm-leaderboard/details_ericpolewski__ASTS-PFAF/blob/main/results_2024-02-10T08-56-33.730792.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\": 0.5872594845662887,\n\
\ \"acc_stderr\": 0.033439222133984044,\n \"acc_norm\": 0.5940986184977506,\n\
\ \"acc_norm_stderr\": 0.03415141564915455,\n \"mc1\": 0.2974296205630355,\n\
\ \"mc1_stderr\": 0.016002651487361002,\n \"mc2\": 0.437377568404521,\n\
\ \"mc2_stderr\": 0.015017384026746418\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5708191126279863,\n \"acc_stderr\": 0.014464085894870655,\n\
\ \"acc_norm\": 0.6126279863481229,\n \"acc_norm_stderr\": 0.01423587248790987\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6354311890061741,\n\
\ \"acc_stderr\": 0.004803253812881041,\n \"acc_norm\": 0.829416450906194,\n\
\ \"acc_norm_stderr\": 0.003753759220205047\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5111111111111111,\n\
\ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.5111111111111111,\n\
\ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5921052631578947,\n \"acc_stderr\": 0.039993097127774734,\n\
\ \"acc_norm\": 0.5921052631578947,\n \"acc_norm_stderr\": 0.039993097127774734\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.54,\n\
\ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.030151134457776292,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.030151134457776292\n \
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6388888888888888,\n\
\ \"acc_stderr\": 0.04016660030451233,\n \"acc_norm\": 0.6388888888888888,\n\
\ \"acc_norm_stderr\": 0.04016660030451233\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\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\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5317919075144508,\n\
\ \"acc_stderr\": 0.03804749744364764,\n \"acc_norm\": 0.5317919075144508,\n\
\ \"acc_norm_stderr\": 0.03804749744364764\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n\
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.03257901482099835,\n\
\ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.03257901482099835\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n\
\ \"acc_stderr\": 0.044045561573747664,\n \"acc_norm\": 0.32456140350877194,\n\
\ \"acc_norm_stderr\": 0.044045561573747664\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\
\ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3386243386243386,\n \"acc_stderr\": 0.024373197867983063,\n \"\
acc_norm\": 0.3386243386243386,\n \"acc_norm_stderr\": 0.024373197867983063\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n\
\ \"acc_stderr\": 0.042407993275749255,\n \"acc_norm\": 0.3412698412698413,\n\
\ \"acc_norm_stderr\": 0.042407993275749255\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.6870967741935484,\n \"acc_stderr\": 0.02637756702864586,\n \"\
acc_norm\": 0.6870967741935484,\n \"acc_norm_stderr\": 0.02637756702864586\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.45320197044334976,\n \"acc_stderr\": 0.03502544650845872,\n \"\
acc_norm\": 0.45320197044334976,\n \"acc_norm_stderr\": 0.03502544650845872\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\
: 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6909090909090909,\n \"acc_stderr\": 0.036085410115739666,\n\
\ \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.036085410115739666\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121434,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121434\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.617948717948718,\n \"acc_stderr\": 0.024635549163908234,\n \
\ \"acc_norm\": 0.617948717948718,\n \"acc_norm_stderr\": 0.024635549163908234\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \
\ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6050420168067226,\n \"acc_stderr\": 0.031753678460966245,\n\
\ \"acc_norm\": 0.6050420168067226,\n \"acc_norm_stderr\": 0.031753678460966245\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8055045871559633,\n \"acc_stderr\": 0.01697028909045802,\n \"\
acc_norm\": 0.8055045871559633,\n \"acc_norm_stderr\": 0.01697028909045802\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\": 0.7941176470588235,\n\
\ \"acc_stderr\": 0.028379449451588667,\n \"acc_norm\": 0.7941176470588235,\n\
\ \"acc_norm_stderr\": 0.028379449451588667\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7426160337552743,\n \"acc_stderr\": 0.02845882099146029,\n\
\ \"acc_norm\": 0.7426160337552743,\n \"acc_norm_stderr\": 0.02845882099146029\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\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\":\
\ 0.7024793388429752,\n \"acc_stderr\": 0.041733491480835,\n \"acc_norm\"\
: 0.7024793388429752,\n \"acc_norm_stderr\": 0.041733491480835\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6993865030674846,\n \"acc_stderr\": 0.03602511318806771,\n\
\ \"acc_norm\": 0.6993865030674846,\n \"acc_norm_stderr\": 0.03602511318806771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n\
\ \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.4017857142857143,\n\
\ \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6893203883495146,\n \"acc_stderr\": 0.0458212416016155,\n\
\ \"acc_norm\": 0.6893203883495146,\n \"acc_norm_stderr\": 0.0458212416016155\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.022509033937077812,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.022509033937077812\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7624521072796935,\n\
\ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.7624521072796935,\n\
\ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.02552247463212161,\n\
\ \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.02552247463212161\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.47374301675977654,\n\
\ \"acc_stderr\": 0.016699427672784768,\n \"acc_norm\": 0.47374301675977654,\n\
\ \"acc_norm_stderr\": 0.016699427672784768\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6372549019607843,\n \"acc_stderr\": 0.027530078447110303,\n\
\ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.027530078447110303\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818767,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818767\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6790123456790124,\n \"acc_stderr\": 0.02597656601086274,\n\
\ \"acc_norm\": 0.6790123456790124,\n \"acc_norm_stderr\": 0.02597656601086274\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4645390070921986,\n \"acc_stderr\": 0.029752389657427047,\n \
\ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.029752389657427047\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4654498044328553,\n\
\ \"acc_stderr\": 0.012739711554045704,\n \"acc_norm\": 0.4654498044328553,\n\
\ \"acc_norm_stderr\": 0.012739711554045704\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5772058823529411,\n \"acc_stderr\": 0.03000856284500348,\n\
\ \"acc_norm\": 0.5772058823529411,\n \"acc_norm_stderr\": 0.03000856284500348\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5882352941176471,\n \"acc_stderr\": 0.019910377463105935,\n \
\ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.019910377463105935\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6612244897959184,\n \"acc_stderr\": 0.030299506562154185,\n\
\ \"acc_norm\": 0.6612244897959184,\n \"acc_norm_stderr\": 0.030299506562154185\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7910447761194029,\n\
\ \"acc_stderr\": 0.028748298931728655,\n \"acc_norm\": 0.7910447761194029,\n\
\ \"acc_norm_stderr\": 0.028748298931728655\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\
\ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2974296205630355,\n\
\ \"mc1_stderr\": 0.016002651487361002,\n \"mc2\": 0.437377568404521,\n\
\ \"mc2_stderr\": 0.015017384026746418\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.23805913570887036,\n \
\ \"acc_stderr\": 0.011731278748420906\n }\n}\n```"
repo_url: https://huggingface.co/ericpolewski/ASTS-PFAF
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|arc:challenge|25_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|gsm8k|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hellaswag|10_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T08-56-33.730792.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-10T08-56-33.730792.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- '**/details_harness|winogrande|5_2024-02-10T08-56-33.730792.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-10T08-56-33.730792.parquet'
- config_name: results
data_files:
- split: 2024_02_10T08_56_33.730792
path:
- results_2024-02-10T08-56-33.730792.parquet
- split: latest
path:
- results_2024-02-10T08-56-33.730792.parquet
---
# Dataset Card for Evaluation run of ericpolewski/ASTS-PFAF
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [ericpolewski/ASTS-PFAF](https://huggingface.co/ericpolewski/ASTS-PFAF) 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_ericpolewski__ASTS-PFAF",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-10T08:56:33.730792](https://huggingface.co/datasets/open-llm-leaderboard/details_ericpolewski__ASTS-PFAF/blob/main/results_2024-02-10T08-56-33.730792.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": 0.5872594845662887,
"acc_stderr": 0.033439222133984044,
"acc_norm": 0.5940986184977506,
"acc_norm_stderr": 0.03415141564915455,
"mc1": 0.2974296205630355,
"mc1_stderr": 0.016002651487361002,
"mc2": 0.437377568404521,
"mc2_stderr": 0.015017384026746418
},
"harness|arc:challenge|25": {
"acc": 0.5708191126279863,
"acc_stderr": 0.014464085894870655,
"acc_norm": 0.6126279863481229,
"acc_norm_stderr": 0.01423587248790987
},
"harness|hellaswag|10": {
"acc": 0.6354311890061741,
"acc_stderr": 0.004803253812881041,
"acc_norm": 0.829416450906194,
"acc_norm_stderr": 0.003753759220205047
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5111111111111111,
"acc_stderr": 0.04318275491977976,
"acc_norm": 0.5111111111111111,
"acc_norm_stderr": 0.04318275491977976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5921052631578947,
"acc_stderr": 0.039993097127774734,
"acc_norm": 0.5921052631578947,
"acc_norm_stderr": 0.039993097127774734
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6,
"acc_stderr": 0.030151134457776292,
"acc_norm": 0.6,
"acc_norm_stderr": 0.030151134457776292
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.04016660030451233,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.04016660030451233
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"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": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5317919075144508,
"acc_stderr": 0.03804749744364764,
"acc_norm": 0.5317919075144508,
"acc_norm_stderr": 0.03804749744364764
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.047840607041056527,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.047840607041056527
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4595744680851064,
"acc_stderr": 0.03257901482099835,
"acc_norm": 0.4595744680851064,
"acc_norm_stderr": 0.03257901482099835
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.32456140350877194,
"acc_stderr": 0.044045561573747664,
"acc_norm": 0.32456140350877194,
"acc_norm_stderr": 0.044045561573747664
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5241379310344828,
"acc_stderr": 0.0416180850350153,
"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.0416180850350153
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3386243386243386,
"acc_stderr": 0.024373197867983063,
"acc_norm": 0.3386243386243386,
"acc_norm_stderr": 0.024373197867983063
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3412698412698413,
"acc_stderr": 0.042407993275749255,
"acc_norm": 0.3412698412698413,
"acc_norm_stderr": 0.042407993275749255
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6870967741935484,
"acc_stderr": 0.02637756702864586,
"acc_norm": 0.6870967741935484,
"acc_norm_stderr": 0.02637756702864586
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.45320197044334976,
"acc_stderr": 0.03502544650845872,
"acc_norm": 0.45320197044334976,
"acc_norm_stderr": 0.03502544650845872
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.036085410115739666,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.036085410115739666
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7676767676767676,
"acc_stderr": 0.030088629490217487,
"acc_norm": 0.7676767676767676,
"acc_norm_stderr": 0.030088629490217487
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8808290155440415,
"acc_stderr": 0.023381935348121434,
"acc_norm": 0.8808290155440415,
"acc_norm_stderr": 0.023381935348121434
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.617948717948718,
"acc_stderr": 0.024635549163908234,
"acc_norm": 0.617948717948718,
"acc_norm_stderr": 0.024635549163908234
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34814814814814815,
"acc_stderr": 0.029045600290616255,
"acc_norm": 0.34814814814814815,
"acc_norm_stderr": 0.029045600290616255
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6050420168067226,
"acc_stderr": 0.031753678460966245,
"acc_norm": 0.6050420168067226,
"acc_norm_stderr": 0.031753678460966245
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8055045871559633,
"acc_stderr": 0.01697028909045802,
"acc_norm": 0.8055045871559633,
"acc_norm_stderr": 0.01697028909045802
},
"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": 0.7941176470588235,
"acc_stderr": 0.028379449451588667,
"acc_norm": 0.7941176470588235,
"acc_norm_stderr": 0.028379449451588667
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7426160337552743,
"acc_stderr": 0.02845882099146029,
"acc_norm": 0.7426160337552743,
"acc_norm_stderr": 0.02845882099146029
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6564885496183206,
"acc_stderr": 0.041649760719448786,
"acc_norm": 0.6564885496183206,
"acc_norm_stderr": 0.041649760719448786
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7024793388429752,
"acc_stderr": 0.041733491480835,
"acc_norm": 0.7024793388429752,
"acc_norm_stderr": 0.041733491480835
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252626,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252626
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6993865030674846,
"acc_stderr": 0.03602511318806771,
"acc_norm": 0.6993865030674846,
"acc_norm_stderr": 0.03602511318806771
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4017857142857143,
"acc_stderr": 0.04653333146973646,
"acc_norm": 0.4017857142857143,
"acc_norm_stderr": 0.04653333146973646
},
"harness|hendrycksTest-management|5": {
"acc": 0.6893203883495146,
"acc_stderr": 0.0458212416016155,
"acc_norm": 0.6893203883495146,
"acc_norm_stderr": 0.0458212416016155
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8632478632478633,
"acc_stderr": 0.022509033937077812,
"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.022509033937077812
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7624521072796935,
"acc_stderr": 0.015218733046150193,
"acc_norm": 0.7624521072796935,
"acc_norm_stderr": 0.015218733046150193
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.02552247463212161,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.02552247463212161
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.47374301675977654,
"acc_stderr": 0.016699427672784768,
"acc_norm": 0.47374301675977654,
"acc_norm_stderr": 0.016699427672784768
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6372549019607843,
"acc_stderr": 0.027530078447110303,
"acc_norm": 0.6372549019607843,
"acc_norm_stderr": 0.027530078447110303
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.025922371788818767,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.025922371788818767
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6790123456790124,
"acc_stderr": 0.02597656601086274,
"acc_norm": 0.6790123456790124,
"acc_norm_stderr": 0.02597656601086274
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.029752389657427047,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.029752389657427047
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4654498044328553,
"acc_stderr": 0.012739711554045704,
"acc_norm": 0.4654498044328553,
"acc_norm_stderr": 0.012739711554045704
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5772058823529411,
"acc_stderr": 0.03000856284500348,
"acc_norm": 0.5772058823529411,
"acc_norm_stderr": 0.03000856284500348
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5882352941176471,
"acc_stderr": 0.019910377463105935,
"acc_norm": 0.5882352941176471,
"acc_norm_stderr": 0.019910377463105935
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6612244897959184,
"acc_stderr": 0.030299506562154185,
"acc_norm": 0.6612244897959184,
"acc_norm_stderr": 0.030299506562154185
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7910447761194029,
"acc_stderr": 0.028748298931728655,
"acc_norm": 0.7910447761194029,
"acc_norm_stderr": 0.028748298931728655
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.0377525168068637,
"acc_norm": 0.83,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4578313253012048,
"acc_stderr": 0.038786267710023595,
"acc_norm": 0.4578313253012048,
"acc_norm_stderr": 0.038786267710023595
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.030611116557432528,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.030611116557432528
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2974296205630355,
"mc1_stderr": 0.016002651487361002,
"mc2": 0.437377568404521,
"mc2_stderr": 0.015017384026746418
},
"harness|winogrande|5": {
"acc": 0.7687450670876085,
"acc_stderr": 0.01185004012485051
},
"harness|gsm8k|5": {
"acc": 0.23805913570887036,
"acc_stderr": 0.011731278748420906
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
may-ohta/tatoeba | ---
license: cc-by-2.0
---
---
annotations_creators:
- found
language_creators:
- found
language:
- ab
- acm
- ady
- af
- afb
- afh
- aii
- ain
- ajp
- akl
- aln
- am
- an
- ang
- aoz
- apc
- ar
- arq
- ary
- arz
- as
- ast
- avk
- awa
- ayl
- az
- ba
- bal
- bar
- be
- ber
- bg
- bho
- bjn
- bm
- bn
- bo
- br
- brx
- bs
- bua
- bvy
- bzt
- ca
- cay
- cbk
- ce
- ceb
- ch
- chg
- chn
- cho
- chr
- cjy
- ckb
- ckt
- cmn
- co
- code
- cpi
- crh
- crk
- cs
- csb
- cv
- cy
- da
- de
- dng
- drt
- dsb
- dtp
- dv
- dws
- ee
- egl
- el
- emx
- en
- enm
- eo
- es
- et
- eu
- ext
- fi
- fj
- fkv
- fo
- fr
- frm
- fro
- frr
- fuc
- fur
- fuv
- fy
- ga
- gag
- gan
- gbm
- gcf
- gd
- gil
- gl
- gn
- gom
- gos
- got
- grc
- gsw
- gu
- gv
- ha
- hak
- haw
- hbo
- he
- hi
- hif
- hil
- hnj
- hoc
- hr
- hrx
- hsb
- hsn
- ht
- hu
- hy
- ia
- iba
- id
- ie
- ig
- ii
- ike
- ilo
- io
- is
- it
- izh
- ja
- jam
- jbo
- jdt
- jpa
- jv
- ka
- kaa
- kab
- kam
- kek
- kha
- kjh
- kk
- kl
- km
- kmr
- kn
- ko
- koi
- kpv
- krc
- krl
- ksh
- ku
- kum
- kw
- kxi
- ky
- la
- laa
- lad
- lb
- ldn
- lfn
- lg
- lij
- liv
- lkt
- lld
- lmo
- ln
- lo
- lt
- ltg
- lut
- lv
- lzh
- lzz
- mad
- mai
- max
- mdf
- mfe
- mg
- mgm
- mh
- mhr
- mi
- mic
- min
- mk
- ml
- mn
- mni
- mnw
- moh
- mr
- mt
- mvv
- mwl
- mww
- my
- myv
- na
- nah
- nan
- nb
- nch
- nds
- ngt
- ngu
- niu
- nl
- nlv
- nn
- nog
- non
- nov
- npi
- nst
- nus
- nv
- ny
- nys
- oar
- oc
- ofs
- ood
- or
- orv
- os
- osp
- ota
- otk
- pa
- pag
- pal
- pam
- pap
- pau
- pcd
- pdc
- pes
- phn
- pi
- pl
- pms
- pnb
- ppl
- prg
- ps
- pt
- qu
- quc
- qya
- rap
- rif
- rm
- rn
- ro
- rom
- ru
- rue
- rw
- sa
- sah
- sc
- scn
- sco
- sd
- sdh
- se
- sg
- sgs
- shs
- shy
- si
- sjn
- sl
- sm
- sma
- sn
- so
- sq
- sr
- stq
- su
- sux
- sv
- swg
- swh
- syc
- ta
- te
- tet
- tg
- th
- thv
- ti
- tig
- tk
- tl
- tlh
- tly
- tmr
- tmw
- tn
- to
- toi
- tok
- tpi
- tpw
- tr
- ts
- tt
- tts
- tvl
- ty
- tyv
- tzl
- udm
- ug
- uk
- umb
- ur
- uz
- vec
- vep
- vi
- vo
- vro
- wa
- war
- wo
- wuu
- xal
- xh
- xqa
- yi
- yo
- yue
- zlm
- zsm
- zu
- zza
license:
- cc-by-2.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: tatoeba
pretty_name: Tatoeba
dataset_info:
- config_name: en-mr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- mr
splits:
- name: train
num_bytes: 6190484
num_examples: 53462
download_size: 1436200
dataset_size: 6190484
- config_name: eo-nl
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- eo
- nl
splits:
- name: train
num_bytes: 8150048
num_examples: 93650
download_size: 3020382
dataset_size: 8150048
- config_name: es-pt
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- es
- pt
splits:
- name: train
num_bytes: 6180464
num_examples: 67782
download_size: 2340361
dataset_size: 6180464
- config_name: fr-ru
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- fr
- ru
splits:
- name: train
num_bytes: 19775390
num_examples: 195161
download_size: 5509784
dataset_size: 19775390
- config_name: es-gl
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- es
- gl
splits:
- name: train
num_bytes: 287683
num_examples: 3135
download_size: 128506
dataset_size: 287683
---
# Dataset Card for Tatoeba
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/Tatoeba.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Tatoeba is a collection of sentences and translations.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Tatoeba.php
E.g.
`dataset = load_dataset("tatoeba", lang1="en", lang2="he")`
The default date is v2021-07-22, but you can also change the date with
`dataset = load_dataset("tatoeba", lang1="en", lang2="he", date="v2020-11-09")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- ab
- acm
- ady
- af
- afb
- afh
- aii
- ain
- ajp
- akl
- aln
- am
- an
- ang
- aoz
- apc
- ar
- arq
- ary
- arz
- as
- ast
- avk
- awa
- ayl
- az
- ba
- bal
- bar
- be
- ber
- bg
- bho
- bjn
- bm
- bn
- bo
- br
- brx
- bs
- bua
- bvy
- bzt
- ca
- cay
- cbk
- ce
- ceb
- ch
- chg
- chn
- cho
- chr
- cjy
- ckb
- ckt
- cmn
- co
- code
- cpi
- crh
- crk
- cs
- csb
- cv
- cy
- da
- de
- dng
- drt
- dsb
- dtp
- dv
- dws
- ee
- egl
- el
- emx
- en
- enm
- eo
- es
- et
- eu
- ext
- fi
- fj
- fkv
- fo
- fr
- frm
- fro
- frr
- fuc
- fur
- fuv
- fy
- ga
- gag
- gan
- gbm
- gcf
- gd
- gil
- gl
- gn
- gom
- gos
- got
- grc
- gsw
- gu
- gv
- ha
- hak
- haw
- hbo
- he
- hi
- hif
- hil
- hnj
- hoc
- hr
- hrx
- hsb
- hsn
- ht
- hu
- hy
- ia
- iba
- id
- ie
- ig
- ii
- ike
- ilo
- io
- is
- it
- izh
- ja
- jam
- jbo
- jdt
- jpa
- jv
- ka
- kaa
- kab
- kam
- kek
- kha
- kjh
- kk
- kl
- km
- kmr
- kn
- ko
- koi
- kpv
- krc
- krl
- ksh
- ku
- kum
- kw
- kxi
- ky
- kzj: Coastal Kadazan (deprecated tag; preferred value: Kadazan Dusun; Central Dusun (`dtp`))
- la
- laa
- lad
- lb
- ldn
- lfn
- lg
- lij
- liv
- lkt
- lld
- lmo
- ln
- lo
- lt
- ltg
- lut
- lv
- lzh
- lzz
- mad
- mai
- max
- mdf
- mfe
- mg
- mgm
- mh
- mhr
- mi
- mic
- min
- mk
- ml
- mn
- mni
- mnw
- moh
- mr
- mt
- mvv
- mwl
- mww
- my
- myv
- na
- nah
- nan
- nb
- nch
- nds
- ngt
- ngu
- niu
- nl
- nlv
- nn
- nog
- non
- nov
- npi
- nst
- nus
- nv
- ny
- nys
- oar
- oc
- ofs
- ood
- or
- orv
- os
- osp
- ota
- otk
- pa
- pag
- pal
- pam
- pap
- pau
- pcd
- pdc
- pes
- phn
- pi
- pl
- pms
- pnb
- ppl
- prg
- ps
- pt
- qu
- quc
- qya
- rap
- rif
- rm
- rn
- ro
- rom
- ru
- rue
- rw
- sa
- sah
- sc
- scn
- sco
- sd
- sdh
- se
- sg
- sgs
- shs
- shy
- si
- sjn
- sl
- sm
- sma
- sn
- so
- sq
- sr
- stq
- su
- sux
- sv
- swg
- swh
- syc
- ta
- te
- tet
- tg
- th
- thv
- ti
- tig
- tk
- tl
- tlh
- tly
- tmr
- tmw
- tn
- to
- toi
- tok
- tpi
- tpw
- tr
- ts
- tt
- tts
- tvl
- ty
- tyv
- tzl
- udm
- ug
- uk
- umb
- ur
- uz
- vec
- vep
- vi
- vo
- vro
- wa
- war
- wo
- wuu
- xal
- xh
- xqa
- yi
- yo
- yue
- zlm
- zsm
- zu
- zza
## 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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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 [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
|
boapps/hazifeladat-90k | ---
license: apache-2.0
language:
- hu
size_categories:
- 10K<n<100K
---
Szintetikusan generált adathalmaz kb. 90 000 házifeladat kérdéssel, válasszal és értékeléssel.
A módszer alapját a [GLAN](https://arxiv.org/abs/2402.13064) adta: Előbb generáltattam GPT4-el egy csomó tudományterületet, aztán minden tudományterülethez tantárgyakat (ezt már gemini-jal). Minden tárgyhoz generáltattam egy sor kérdést is, amiket korábbi adathalmazokhoz hasonlóan megválaszoltattam, majd értékeltettem is szintén gemini segítségével. |
thisiskeithkwan/canto_full_7 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 10781838762.132
num_examples: 27269
download_size: 1417911450
dataset_size: 10781838762.132
---
# Dataset Card for "canto_full_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/8e5c33b5 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 186
num_examples: 10
download_size: 1329
dataset_size: 186
---
# Dataset Card for "8e5c33b5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ovior/twitter_dataset_1713222805 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 2710480
num_examples: 8318
download_size: 1532180
dataset_size: 2710480
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
GAIR/MathPile_Commercial | ---
license: cc-by-sa-4.0
extra_gated_prompt: >-
By using this data, you agree to comply with the original usage licenses of
all sources contributing to MathPile_Commercial. The MathPile_Commercial is
governed by the CC BY-SA 4.0 license. Access to this dataset is granted
automatically once you accept the license terms and complete all the required
fields below.
extra_gated_fields:
Your Full Name: text
Organization or Entity you are affiliated with: text
Country or state you are located in: text
Your email: text
What is your intended use(s) for this dataset: text
You AGREE to comply with the original usage licenses of all sources contributing to this dataset and the license of this dataset: checkbox
You AGREE to cite our paper if you use this dataset: checkbox
You ENSURE that the information you have provided is true and accurate: checkbox
language:
- en
size_categories:
- 1B<n<10B
---
<br>
**🔥Update**:
- [2024/01/06] We released the commercial-use version of MathPile, namely `MathPile_Commercial`.
<br>
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
`MathPile_Commercial` is a commercial-use version of [MathPile](https://huggingface.co/datasets/GAIR/MathPile), obtained by culling documents that are prohibited from commercial use in the MathPile (latest version, i.e., `v0.2`). Specifically, we conducted a non-commercial use detection in the source data, utilizing the license information in the metadata for arXiv sources and employing keyword matching for other sources. As a result, we have excluded approximately 8,000 documents from the latest version of MathPile, comprising 7,350 from arXiv, 518 from Creative Commons sources, 68 from textbooks, and 8 from Wikipedia. This version of the dataset contains around 9.2 billion tokens.
MathPile is a diverse and high-quality math-centric corpus comprising about 9.5 billion tokens, which is significantly different from the previous work in the following characteristics:
<div align="center">
<img src="./imgs/mathpile-key-features.png" width=45%/>
</div>
- **Math-centric**: MathPile uniquely caters to the math domain, unlike general domain-focused corpora like Pile and RedPajama, or multilingual-focused ones like ROOTS and The Stack. While there are math-centric corpora, they're often either closed-sourced, like Google's Minerva and OpenAI's MathMix, or lack diversity, such as ProofPile and OpenWebMath.
- **Diversity**: MathPile draws from a wide range of sources: **Textbooks** (including lecture notes), **arXiv**, **Wikipedia**, **ProofWiki**, **StackExchange**, and **Web Pages**. It encompasses mathematical content suitable for K-12, college, postgraduate levels, and math competitions. **This diversity is a first, especially with our release of a significant collection of high-quality textbooks (~0.19B tokens).**
- **High-Quality**: We adhered to the principle of *less is more*, firmly believing in the supremacy of data quality over quantity, even in the pre-training phase. Our meticulous data collection and processing efforts included a complex suite of preprocessing, prefiltering, cleaning, filtering, and deduplication, ensuring the high quality of our corpus.
- **Data Documentation**: To enhance transparency, we've extensively documented MathPile. This includes a **dataset sheet** (see Table 5 in our paper) and **quality annotations** for web-sourced documents, like language identification scores and symbol-to-word ratios. This gives users flexibility to tailor the data to their needs. We've also performed **data contamination detection** to eliminate duplicates from benchmark test sets like MATH and MMLU-STEM.
<div align="center">
<img src="./imgs/mathpile-overview.png" width=70%/>
</div>
## Dataset Details
Refer to Appendix A in [our paper](https://huggingface.co/papers/2312.17120) for the MathPile Dataset Sheet.
### How to download MathPile?
Currently, we recommend that you download it locally from the command line (such as `huggingface-cli`) instead of the python function `load_dataset("GAIR/MathPile")` (due to a possible network issue), unpack the gz file, and then load the jsonl file. Some commands that might be helpful are as follows
```
$ huggingface-cli download --resume-download --repo-type dataset GAIR/MathPile --local-dir /your/path/ --local-dir-use-symlinks False
$ cd /your/path/
$ find . -type f -name "*.gz" -exec gzip -d {} \;
```
Later we will also support the datasets loading via `load_dataset("GAIR/MathPile")`. Stay tuned.
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** GAIR Lab, SJTU
- **Funded by [optional]:** GAIR Lab, SJTU
- **Language(s) (NLP):** English
- **License:** CC BY-SA 4.0
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://github.com/GAIR-NLP/MathPile
- **Paper [optional]:** https://huggingface.co/papers/2312.17120
- **Demo [optional]:** https://gair-nlp.github.io/MathPile/
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
To develop mathematical language models.
<!-- This section describes suitable use cases for the dataset. -->
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
This dataset may be not suitable for scenarios unrelated to mathematics or reasoning.
## 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. -->
```
{
"text": ...,
"SubSet": "CommomCrawl" | "StackExchange" | "Textbooks" | "Wikipedia" | "ProofWiki" | "arXiv"
"meta": {"language_detection_score": , "idx": , "contain_at_least_two_stop_words": ,
}
```
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
To create a diverse and high-quality math-centric corpus, thereby enhancing the mathematical reasoning abilities of language models.
### 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. -->
We sourced data from Textbooks, lecture notes, arXiv, Wikipedia, ProofWiki, StackExchange, and Common Crawl. Throughout the MathPile development, we meticulously source and
gather data, applying a rigorous and math-specific pipeline. This pipeline encompasses various stages such as preprocessing, prefiltering, language identification, cleaning and filtering, and deduplication,
all aimed at maintaining the high quality of the corpus. Please see [our paper](https://arxiv.org/abs/2312.17120) for more details.
### Annotations
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
We provided *quantity annotations* (such as language identification scores and the ratio of symbols to words) for documents from Web pages (i.e., Common Crawl and Wikipedia). These annotations offer future researchers and developers
the flexibility to filter the data according to their criteria, tailoring it to their specific needs.
#### 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. -->
The corpus may potentially contain academic emails and the author's name, as seen in papers from sources like arXiv. However, we view this as justifiable and within acceptable bounds.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- The decisions made during the data collection and processing phases might not always be optimal.
- Some documents in MathPile may not always be of the highest quality. We are committed to continually refining and optimizing this corpus.
### 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.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you find our work useful or use MathPile, please cite our paper:
```
@article{wang2023mathpile,
title={Generative AI for Math: Part I -- MathPile: A Billion-Token-Scale Pretraining Corpus for Math},
author={Wang, Zengzhi and Xia, Rui and Liu, Pengfei},
journal={arXiv preprint arXiv:2312.17120},
year={2023}
}
```
## Dataset Card Authors
[Zengzhi Wang](https://scholar.google.com/citations?user=qLS4f-8AAAAJ&hl=en)
## Dataset Card Contact
stefanpengfei@gmail.com, zzwang.nlp@gmail.com
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-56000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 660346
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cyrilzhang/ace | ---
license: mit
---
|
lshowway/wikipedia.SVO | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6543464364
num_examples: 4003741
download_size: 2591178685
dataset_size: 6543464364
---
# Dataset Card for "wikipedia.SVO"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
saibo/bookcorpus_compact_1024_shard6_of_10_meta | ---
dataset_info:
features:
- name: text
dtype: string
- name: concept_with_offset
dtype: string
- name: cid_arrangement
sequence: int32
- name: schema_lengths
sequence: int64
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sequence: int64
- name: text_lengths
sequence: int64
splits:
- name: train
num_bytes: 7837212848
num_examples: 61605
download_size: 1730877027
dataset_size: 7837212848
---
# Dataset Card for "bookcorpus_compact_1024_shard6_of_10_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
whu9/mediasum_postprocess | ---
dataset_info:
features:
- name: source
dtype: string
- name: summary
dtype: string
- name: source_num_tokens
dtype: int64
- name: summary_num_tokens
dtype: int64
splits:
- name: train
num_bytes: 3913935357
num_examples: 443511
- name: validation
num_bytes: 86873579
num_examples: 9999
- name: test
num_bytes: 88635215
num_examples: 9997
download_size: 2335096802
dataset_size: 4089444151
---
# Dataset Card for "mediasum_postprocess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HeshamHaroon/oasst1-ar-threads | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 12213866
num_examples: 9845
- name: validation
num_bytes: 647138
num_examples: 517
download_size: 5609957
dataset_size: 12861004
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
ilsp/arc_greek | ---
language: el
license: cc-by-nc-sa-4.0
multilinguality: monolingual
size_categories: 1K<n<10K
task_categories:
- multiple-choice
pretty_name: ARC Greek
dataset_info:
- config_name: ARC-Challenge
splits:
- name: train
num_examples: 1114
- name: validation
num_examples: 299
- name: test
num_examples: 1168
- config_name: ARC-Easy
splits:
- name: train
num_examples: 2249
- name: validation
num_examples: 570
- name: test
num_examples: 2376
configs:
- config_name: ARC-Challenge
data_files:
- split: train
path: ARC-Challenge/train-*
- split: validation
path: ARC-Challenge/validation-*
- split: test
path: ARC-Challenge/test-*
- config_name: ARC-Easy
data_files:
- split: train
path: ARC-Easy/train-*
- split: validation
path: ARC-Easy/validation-*
- split: test
path: ARC-Easy/test-*
---
# Dataset Card for ARC Greek
The ARC Greek dataset is a set of 7776 multiple-choice questions from the [AI2 ARC](https://huggingface.co/datasets/allenai/ai2_arc) dataset, machine-translated into Greek. The original dataset containes genuine grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm.
## Dataset Details
### Dataset Description
<!-- -->
- **Curated by:** ILSP/Athena RC
<!--- **Funded by [optional]:** [More Information Needed]-->
<!--- **Shared by [optional]:** [More Information Needed]-->
- **Language(s) (NLP):** el
- **License:** cc-by-nc-sa-4.0
<!--### Dataset Sources [optional]-->
<!-- Provide the basic links for the dataset. -->
<!--- **Repository:** [More Information Needed]-->
<!--- **Paper [optional]:** [More Information Needed]-->
<!--- **Demo [optional]:** [More Information Needed]-->
<!--## Uses-->
<!-- Address questions around how the dataset is intended to be used. -->
<!--### Direct Use-->
<!-- This section describes suitable use cases for the dataset. -->
<!--[More Information Needed]-->
<!--### Out-of-Scope Use-->
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
<!--[More Information Needed]-->
<!--## Dataset Structure-->
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
<!--[More Information Needed]-->
<!--## Dataset Creation-->
<!--### Curation Rationale-->
<!-- Motivation for the creation of this dataset. -->
<!--[More Information Needed]-->
<!--### Source Data-->
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
<!--#### Data Collection and Processing-->
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
<!--[More Information Needed]-->
<!--#### Who are the source data producers?-->
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
<!--[More Information Needed]-->
<!--### Annotations [optional]-->
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
<!--#### Annotation process-->
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
<!--[More Information Needed]-->
<!--#### Who are the annotators?-->
<!-- This section describes the people or systems who created the annotations. -->
<!--[More Information Needed]-->
<!--#### Personal and Sensitive Information-->
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
<!--[More Information Needed]-->
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This dataset is the result of machine translation.
<!--### 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-->
<!-- 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
https://www.athenarc.gr/en/ilsp |
Vageesh1/Smart_Contract_HF_Tokenized | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: address
dtype: string
- name: source_code
dtype: string
- name: bytecode
dtype: string
- name: slither
dtype: string
- name: success
dtype: bool
- name: error
dtype: float64
- name: results
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 2580983760
num_examples: 60000
download_size: 821510844
dataset_size: 2580983760
---
# Dataset Card for "Smart_Contract_HF_Tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ | ---
pretty_name: Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [elliotthwang/Elliott-Chinese-LLaMa-GPTQ](https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-27T04:39:21.377335](https://huggingface.co/datasets/open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ/blob/main/results_2023-10-27T04-39-21.377335.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001363255033557047,\n\
\ \"em_stderr\": 0.0003778609196461018,\n \"f1\": 0.057080536912751875,\n\
\ \"f1_stderr\": 0.0012933707193154948,\n \"acc\": 0.44911238992013397,\n\
\ \"acc_stderr\": 0.01146531039524964\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001363255033557047,\n \"em_stderr\": 0.0003778609196461018,\n\
\ \"f1\": 0.057080536912751875,\n \"f1_stderr\": 0.0012933707193154948\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17210007581501138,\n \
\ \"acc_stderr\": 0.010397328057878982\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620297\n\
\ }\n}\n```"
repo_url: https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|arc:challenge|25_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T04_39_21.377335
path:
- '**/details_harness|drop|3_2023-10-27T04-39-21.377335.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T04-39-21.377335.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T04_39_21.377335
path:
- '**/details_harness|gsm8k|5_2023-10-27T04-39-21.377335.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-27T04-39-21.377335.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hellaswag|10_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T17-15-37.349272.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T17-15-37.349272.parquet'
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- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T17-15-37.349272.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T17-15-37.349272.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-13T17-15-37.349272.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T04_39_21.377335
path:
- '**/details_harness|winogrande|5_2023-10-27T04-39-21.377335.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T04-39-21.377335.parquet'
- config_name: results
data_files:
- split: 2023_09_13T17_15_37.349272
path:
- results_2023-09-13T17-15-37.349272.parquet
- split: 2023_10_27T04_39_21.377335
path:
- results_2023-10-27T04-39-21.377335.parquet
- split: latest
path:
- results_2023-10-27T04-39-21.377335.parquet
---
# Dataset Card for Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [elliotthwang/Elliott-Chinese-LLaMa-GPTQ](https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T04:39:21.377335](https://huggingface.co/datasets/open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ/blob/main/results_2023-10-27T04-39-21.377335.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196461018,
"f1": 0.057080536912751875,
"f1_stderr": 0.0012933707193154948,
"acc": 0.44911238992013397,
"acc_stderr": 0.01146531039524964
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196461018,
"f1": 0.057080536912751875,
"f1_stderr": 0.0012933707193154948
},
"harness|gsm8k|5": {
"acc": 0.17210007581501138,
"acc_stderr": 0.010397328057878982
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620297
}
}
```
### 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] |
liuyanchen1015/MULTI_VALUE_stsb_correlative_constructions | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 8980
num_examples: 37
- name: test
num_bytes: 3056
num_examples: 13
- name: train
num_bytes: 12555
num_examples: 50
download_size: 27111
dataset_size: 24591
---
# Dataset Card for "MULTI_VALUE_stsb_correlative_constructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HydraLM/partitioned_v3_standardized_013 | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: dataset_id
dtype: string
- name: unique_id
dtype: string
splits:
- name: train
num_bytes: 36260944.275211014
num_examples: 67435
download_size: 10436734
dataset_size: 36260944.275211014
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "partitioned_v3_standardized_013"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mstz/arcene | ---
language:
- en
tags:
- arcene
- tabular_classification
- binary_classification
- UCI
pretty_name: Arcene
size_categories:
- n<1K
task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
- tabular-classification
configs:
- arcene
---
# Arcene
The [Arcene dataset](https://archive-beta.ics.uci.edu/dataset/167/arcene) from the [UCI repository](https://archive-beta.ics.uci.edu/).
|
jlbaker361/tex_inv_hot_ip_vanilla | ---
dataset_info:
features:
- name: label
dtype: string
- name: tex_inv_hot_ip_prompt_similarity
dtype: float32
- name: tex_inv_hot_ip_identity_consistency
dtype: float32
- name: tex_inv_hot_ip_negative_prompt_similarity
dtype: float32
- name: tex_inv_hot_ip_target_prompt_similarity
dtype: float32
- name: tex_inv_hot_ip_aesthetic_score
dtype: float32
splits:
- name: train
num_bytes: 308
num_examples: 11
download_size: 4221
dataset_size: 308
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nampdn-ai/tiny-lessons | ---
license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Lessons
size_categories:
- 10K<n<100K
source_datasets:
- nampdn-ai/tiny-en
---
# Tiny Lessons
The dataset is designed to help causal language models learn more effectively from raw web text. It is augmented from public web text and contains two key components: theoretical concepts and practical examples.
The theoretical concepts provide a foundation for understanding the underlying principles and ideas behind the information contained in the raw web text. The practical examples demonstrate how these theoretical concepts can be applied in real-world situations.
This dataset is an ideal resource for ML researchers working with causal language models. I hope you find it useful and welcome any feedback or suggestions you may have.
[View Nomic Atlas](https://atlas.nomic.ai/map/af5b399c-caa4-4ea9-8efc-7165972de209/c096774c-f979-4337-a5ea-08ea18be9fa0) |
davanstrien/model-sizer-bot-stats | ---
dataset_info:
features:
- name: createdAt
dtype: timestamp[us]
- name: pr_number
dtype: int64
- name: status
dtype: large_string
- name: repo_id
dtype: large_string
- name: type
dtype: large_string
- name: isPullRequest
dtype: bool
splits:
- name: train
num_bytes: 3465
num_examples: 44
download_size: 0
dataset_size: 3465
---
# Dataset Card for "model-sizer-bot-stats"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maidalun1020/CrosslingualRetrievalOthersEn2Zh-qrels | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 557395
num_examples: 23002
download_size: 295288
dataset_size: 557395
---
|
zzliang/GRIT | ---
license: ms-pl
language:
- en
multilinguality:
- monolingual
pretty_name: GRIT
size_categories:
- 100M<n<1B
source_datasets:
- COYO-700M
tags:
- image-text-bounding-box pairs
- image-text pairs
task_categories:
- text-to-image
- image-to-text
- object-detection
- zero-shot-classification
task_ids:
- image-captioning
- visual-question-answering
---
# GRIT: Large-Scale Training Corpus of Grounded Image-Text Pairs
### Dataset Description
- **Repository:** [Microsoft unilm](https://github.com/microsoft/unilm/tree/master/kosmos-2)
- **Paper:** [Kosmos-2](https://arxiv.org/abs/2306.14824)
### Dataset Summary
We introduce GRIT, a large-scale dataset of Grounded Image-Text pairs, which is created based on image-text pairs from [COYO-700M](https://github.com/kakaobrain/coyo-dataset) and LAION-2B. We construct a pipeline to extract and link text spans (i.e., noun phrases, and referring expressions) in the caption to their corresponding image regions. More details can be found in the [paper](https://arxiv.org/abs/2306.14824).
### Supported Tasks
During the construction, we excluded the image-caption pairs if no bounding boxes are retained. This procedure resulted in a high-quality image-caption subset of COYO-700M, which we will validate in the future.
Furthermore, this dataset contains text-span-bounding-box pairs. Thus, it can be used in many location-aware mono/multimodal tasks, such as phrase grounding, referring expression comprehension, referring expression generation, and open-world object detection.
### Data Instance
One instance is
```python
{
'key': '000373938',
'clip_similarity_vitb32': 0.353271484375,
'clip_similarity_vitl14': 0.2958984375,
'id': 1795296605919,
'url': "https://www.thestrapsaver.com/wp-content/uploads/customerservice-1.jpg",
'caption': 'a wire hanger with a paper cover that reads we heart our customers',
'width': 1024,
'height': 693,
'noun_chunks': [[19, 32, 0.019644069503434333, 0.31054004033406574, 0.9622142865754519, 0.9603442351023356, 0.79298526], [0, 13, 0.019422357885505368, 0.027634161214033764, 0.9593302408854166, 0.969467560450236, 0.67520964]],
'ref_exps': [[19, 66, 0.019644069503434333, 0.31054004033406574, 0.9622142865754519, 0.9603442351023356, 0.79298526], [0, 66, 0.019422357885505368, 0.027634161214033764, 0.9593302408854166, 0.969467560450236, 0.67520964]]
}
```
- `key`: The generated file name when using img2dataset to download COYO-700M (omit it).
- `clip_similarity_vitb32`: The cosine similarity between text and image(ViT-B/32) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP), provided by COYO-700M.
- `clip_similarity_vitl14`: The cosine similarity between text and image(ViT-L/14) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP), provided by COYO-700M.
- `id`: Unique 64-bit integer ID in COYO-700M.
- `url`: The image URL.
- `caption`: The corresponding caption.
- `width`: The width of the image.
- `height`: The height of the image.
- `noun_chunks`: The noun chunks (extracted by [spaCy](https://spacy.io/)) that have associated bounding boxes (predicted by [GLIP](https://github.com/microsoft/GLIP)). The items in the children list respectively represent 'Start of the noun chunk in caption', 'End of the noun chunk in caption', 'normalized x_min', 'normalized y_min', 'normalized x_max', 'normalized y_max', 'confidence score'.
- `ref_exps`: The corresponding referring expressions. If a noun chunk has no expansion, we just copy it.
### Download image
We recommend to use [img2dataset](https://github.com/rom1504/img2dataset) tool to download the images.
1. Download the metadata. You can download it by cloning current repository:
```bash
git lfs install
git clone https://huggingface.co/datasets/zzliang/GRIT
```
2. Install [img2dataset](https://github.com/rom1504/img2dataset).
```bash
pip install img2dataset
```
3. Download images
You need to replace `/path/to/GRIT_dataset/grit-20m` with the local path to this repository.
```bash
img2dataset --url_list /path/to/GRIT_dataset/grit-20m --input_format "parquet"\
--url_col "url" --caption_col "caption" --output_format webdataset \
--output_folder /tmp/grit --processes_count 4 --thread_count 64 --image_size 256 \
--resize_only_if_bigger=True --resize_mode="keep_ratio" --skip_reencode=True \
--save_additional_columns '["id","noun_chunks","ref_exps","clip_similarity_vitb32","clip_similarity_vitl14"]' \
--enable_wandb False
```
You can adjust some parameters according to your actual needs (e.g., `processes_count`, `thread_count`, `image_size`, `save_additional_columns`).
More img2dataset hyper-parameters can be found in [here](https://github.com/rom1504/img2dataset#api).
### Citation Information
If you apply this dataset to any project and research, please cite our paper and coyo-700m:
```
@article{Kosmos2,
title={Kosmos-2: Grounding Multimodal Large Language Models to the World},
author={Zhiliang Peng and Wenhui Wang and Li Dong and Yaru Hao and Shaohan Huang and Shuming Ma and Furu Wei},
journal={ArXiv},
year={2023},
volume={abs/2306.14824}
}
@misc{kakaobrain2022coyo-700m,
title = {COYO-700M: Image-Text Pair Dataset},
author = {Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, Saehoon Kim},
year = {2022},
howpublished = {\url{https://github.com/kakaobrain/coyo-dataset}},
}
``` |
Nexdata/10000_Chinese_News_Events_Annotation_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
10,000 Chinese news event annotated data. The contents are hot news in 2013. Each piece of news contains one or more events. Each event is annotated. The data is stored in xml and can be used for natural language understanding.
For more details, please refer to the link: https://www.nexdata.ai/dataset/82?source=Huggingface
# Specifications
## Data content
News event annotation
## Data size
10,000
## Annotation
Event word tag
## Collecting period
May 2,013
## Storage format
xml
## Language
Chinese
## Data category
Event extraction; public opinion monitoring
# Licensing Information
Commercial License
|
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