datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-1 | ---
pretty_name: Evaluation run of juhwanlee/llmdo-Mistral-7B-case-1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [juhwanlee/llmdo-Mistral-7B-case-1](https://huggingface.co/juhwanlee/llmdo-Mistral-7B-case-1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-11T19:13:37.745204](https://huggingface.co/datasets/open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-1/blob/main/results_2024-03-11T19-13-37.745204.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\": 0.6324163759083724,\n\
\ \"acc_stderr\": 0.032412220193761165,\n \"acc_norm\": 0.6377710742584999,\n\
\ \"acc_norm_stderr\": 0.0330673242578233,\n \"mc1\": 0.3084455324357405,\n\
\ \"mc1_stderr\": 0.01616803938315687,\n \"mc2\": 0.45690932932637046,\n\
\ \"mc2_stderr\": 0.014755655698380018\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5878839590443686,\n \"acc_stderr\": 0.014383915302225402,\n\
\ \"acc_norm\": 0.621160409556314,\n \"acc_norm_stderr\": 0.014175915490000324\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6353316072495518,\n\
\ \"acc_stderr\": 0.004803533333364223,\n \"acc_norm\": 0.8359888468432584,\n\
\ \"acc_norm_stderr\": 0.003695289340514483\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322663,\n\
\ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322663\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\
: 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\
\ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\
\ \"acc_norm_stderr\": 0.036563436533531585\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\":\
\ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5829787234042553,\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\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41798941798941797,\n \"acc_stderr\": 0.025402555503260912,\n \"\
acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.025402555503260912\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\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7483870967741936,\n \"acc_stderr\": 0.02468597928623997,\n \"\
acc_norm\": 0.7483870967741936,\n \"acc_norm_stderr\": 0.02468597928623997\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\": 0.047258156262526066,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.047258156262526066\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593552,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593552\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.02403548967633508,\n \
\ \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.02403548967633508\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.028661201116524575,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.028661201116524575\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\
\ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\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\"\
: 0.8256880733944955,\n \"acc_stderr\": 0.016265675632010333,\n \"\
acc_norm\": 0.8256880733944955,\n \"acc_norm_stderr\": 0.016265675632010333\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977748,\n \"\
acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977748\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7721518987341772,\n \"acc_stderr\": 0.02730348459906943,\n \
\ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.02730348459906943\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.031024411740572206,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.031024411740572206\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\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\": 0.7975460122699386,\n \"acc_stderr\": 0.03157065078911901,\n\
\ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.03157065078911901\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993457,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993457\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.708092485549133,\n \"acc_stderr\": 0.024476994076247323,\n\
\ \"acc_norm\": 0.708092485549133,\n \"acc_norm_stderr\": 0.024476994076247323\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.40782122905027934,\n\
\ \"acc_stderr\": 0.016435865260914746,\n \"acc_norm\": 0.40782122905027934,\n\
\ \"acc_norm_stderr\": 0.016435865260914746\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.02536060379624256,\n\
\ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.02536060379624256\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460845,\n\
\ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460845\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4530638852672751,\n\
\ \"acc_stderr\": 0.012713845972358981,\n \"acc_norm\": 0.4530638852672751,\n\
\ \"acc_norm_stderr\": 0.012713845972358981\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6360294117647058,\n \"acc_stderr\": 0.02922719246003203,\n\
\ \"acc_norm\": 0.6360294117647058,\n \"acc_norm_stderr\": 0.02922719246003203\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507215,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507215\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675606,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675606\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233257,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233257\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\": 0.5060240963855421,\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\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3084455324357405,\n\
\ \"mc1_stderr\": 0.01616803938315687,\n \"mc2\": 0.45690932932637046,\n\
\ \"mc2_stderr\": 0.014755655698380018\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7916337805840569,\n \"acc_stderr\": 0.011414554399987729\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3912054586808188,\n \
\ \"acc_stderr\": 0.013442502402794302\n }\n}\n```"
repo_url: https://huggingface.co/juhwanlee/llmdo-Mistral-7B-case-1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|arc:challenge|25_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|gsm8k|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hellaswag|10_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T19-13-37.745204.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-11T19-13-37.745204.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- '**/details_harness|winogrande|5_2024-03-11T19-13-37.745204.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-11T19-13-37.745204.parquet'
- config_name: results
data_files:
- split: 2024_03_11T19_13_37.745204
path:
- results_2024-03-11T19-13-37.745204.parquet
- split: latest
path:
- results_2024-03-11T19-13-37.745204.parquet
---
# Dataset Card for Evaluation run of juhwanlee/llmdo-Mistral-7B-case-1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [juhwanlee/llmdo-Mistral-7B-case-1](https://huggingface.co/juhwanlee/llmdo-Mistral-7B-case-1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-11T19:13:37.745204](https://huggingface.co/datasets/open-llm-leaderboard/details_juhwanlee__llmdo-Mistral-7B-case-1/blob/main/results_2024-03-11T19-13-37.745204.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": 0.6324163759083724,
"acc_stderr": 0.032412220193761165,
"acc_norm": 0.6377710742584999,
"acc_norm_stderr": 0.0330673242578233,
"mc1": 0.3084455324357405,
"mc1_stderr": 0.01616803938315687,
"mc2": 0.45690932932637046,
"mc2_stderr": 0.014755655698380018
},
"harness|arc:challenge|25": {
"acc": 0.5878839590443686,
"acc_stderr": 0.014383915302225402,
"acc_norm": 0.621160409556314,
"acc_norm_stderr": 0.014175915490000324
},
"harness|hellaswag|10": {
"acc": 0.6353316072495518,
"acc_stderr": 0.004803533333364223,
"acc_norm": 0.8359888468432584,
"acc_norm_stderr": 0.003695289340514483
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.03823428969926605,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926605
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6943396226415094,
"acc_stderr": 0.028353298073322663,
"acc_norm": 0.6943396226415094,
"acc_norm_stderr": 0.028353298073322663
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.037455547914624555,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.037455547914624555
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6416184971098265,
"acc_stderr": 0.036563436533531585,
"acc_norm": 0.6416184971098265,
"acc_norm_stderr": 0.036563436533531585
},
"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": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41798941798941797,
"acc_stderr": 0.025402555503260912,
"acc_norm": 0.41798941798941797,
"acc_norm_stderr": 0.025402555503260912
},
"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": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7483870967741936,
"acc_stderr": 0.02468597928623997,
"acc_norm": 0.7483870967741936,
"acc_norm_stderr": 0.02468597928623997
},
"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": 0.047258156262526066,
"acc_norm": 0.67,
"acc_norm_stderr": 0.047258156262526066
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.03192271569548301,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.03192271569548301
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.028335609732463362,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463362
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.023814477086593552,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.023814477086593552
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.658974358974359,
"acc_stderr": 0.02403548967633508,
"acc_norm": 0.658974358974359,
"acc_norm_stderr": 0.02403548967633508
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.028661201116524575,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.028661201116524575
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6848739495798319,
"acc_stderr": 0.030176808288974337,
"acc_norm": 0.6848739495798319,
"acc_norm_stderr": 0.030176808288974337
},
"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": 0.8256880733944955,
"acc_stderr": 0.016265675632010333,
"acc_norm": 0.8256880733944955,
"acc_norm_stderr": 0.016265675632010333
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4861111111111111,
"acc_stderr": 0.03408655867977748,
"acc_norm": 0.4861111111111111,
"acc_norm_stderr": 0.03408655867977748
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7990196078431373,
"acc_stderr": 0.028125972265654373,
"acc_norm": 0.7990196078431373,
"acc_norm_stderr": 0.028125972265654373
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7721518987341772,
"acc_stderr": 0.02730348459906943,
"acc_norm": 0.7721518987341772,
"acc_norm_stderr": 0.02730348459906943
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.031024411740572206,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.031024411740572206
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7633587786259542,
"acc_stderr": 0.03727673575596913,
"acc_norm": 0.7633587786259542,
"acc_norm_stderr": 0.03727673575596913
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7975460122699386,
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"acc_norm": 0.7975460122699386,
"acc_norm_stderr": 0.03157065078911901
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5178571428571429,
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"acc_norm": 0.5178571428571429,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
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"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
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"acc_norm": 0.71,
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},
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"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.40782122905027934,
"acc_norm_stderr": 0.016435865260914746
},
"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.7320261437908496,
"acc_norm_stderr": 0.02536060379624256
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.707395498392283,
"acc_norm_stderr": 0.02583989833487798
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7345679012345679,
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"acc_norm": 0.7345679012345679,
"acc_norm_stderr": 0.024569223600460845
},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4530638852672751,
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"acc_norm": 0.4530638852672751,
"acc_norm_stderr": 0.012713845972358981
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6360294117647058,
"acc_stderr": 0.02922719246003203,
"acc_norm": 0.6360294117647058,
"acc_norm_stderr": 0.02922719246003203
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.673202614379085,
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"acc_norm": 0.673202614379085,
"acc_norm_stderr": 0.018975427920507215
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.04607582090719976,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.04607582090719976
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.028920583220675606,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.028920583220675606
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
"acc_stderr": 0.026508590656233257,
"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.026508590656233257
},
"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": 0.5060240963855421,
"acc_stderr": 0.03892212195333045,
"acc_norm": 0.5060240963855421,
"acc_norm_stderr": 0.03892212195333045
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.029547741687640044,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640044
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3084455324357405,
"mc1_stderr": 0.01616803938315687,
"mc2": 0.45690932932637046,
"mc2_stderr": 0.014755655698380018
},
"harness|winogrande|5": {
"acc": 0.7916337805840569,
"acc_stderr": 0.011414554399987729
},
"harness|gsm8k|5": {
"acc": 0.3912054586808188,
"acc_stderr": 0.013442502402794302
}
}
```
## 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] |
AdapterOcean/physics_dataset_standardized | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
splits:
- name: train
num_bytes: 50580506
num_examples: 40000
download_size: 22905844
dataset_size: 50580506
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AchrafLou/image_captioned_40 | ---
dataset_info:
features:
- name: text
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 246618.0
num_examples: 38
- name: validation
num_bytes: 59992.0
num_examples: 10
download_size: 280777
dataset_size: 306610.0
---
# Dataset Card for "image_captioned_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Seenka/directv-zocalos_1.0fps_21-08-2023_24-08-2023 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_filename
dtype: string
- name: frame_time
dtype: time64[us]
- name: video_storage_path
dtype: string
- name: zocalo_id
dtype: string
- name: frame_number
dtype: int64
- name: is_L_shape
dtype: bool
- name: horizontal_check
dtype: bool
- name: vertical_check
dtype: bool
- name: black_image
dtype: bool
- name: horizontal_xmin
dtype: int64
- name: horizontal_xmax
dtype: int64
- name: horizontal_ymin
dtype: int64
- name: horizontal_ymax
dtype: int64
- name: vertical_xmin
dtype: int64
- name: vertical_xmax
dtype: int64
- name: vertical_ymin
dtype: int64
- name: vertical_ymax
dtype: int64
- name: cropped_image_horizontal
dtype: image
- name: cropped_image_vertical
dtype: 'null'
- name: width
dtype: int64
- name: height
dtype: int64
- name: embedding_horizontal
sequence: float32
splits:
- name: train
num_bytes: 8554665.0
num_examples: 10
download_size: 4263397
dataset_size: 8554665.0
---
# Dataset Card for "directv-zocalos_1.0fps_21-08-2023_24-08-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
desiai/archiveoldsamachaar | ---
license: odc-by
---
|
Elapsedf/Plant-Pathology | ---
license: mit
---
|
joey234/mmlu-high_school_european_history-original-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_prompt
dtype: string
splits:
- name: test
num_bytes: 468329
num_examples: 140
download_size: 250111
dataset_size: 468329
---
# Dataset Card for "mmlu-high_school_european_history-original-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cmxuebuhui/glm_train | ---
license: apache-2.0
---
|
gayanin/babylon-native-mixed | ---
dataset_info:
- config_name: prob-0.1
features:
- name: refs
dtype: string
- name: trans
dtype: string
splits:
- name: train
num_bytes: 707260
num_examples: 5293
- name: test
num_bytes: 79603
num_examples: 662
- name: validation
num_bytes: 77176
num_examples: 662
download_size: 498401
dataset_size: 864039
- config_name: prob-0.2
features:
- name: refs
dtype: string
- name: trans
dtype: string
splits:
- name: train
num_bytes: 707658
num_examples: 5293
- name: test
num_bytes: 79464
num_examples: 662
- name: validation
num_bytes: 77409
num_examples: 662
download_size: 513529
dataset_size: 864531
- config_name: prob-0.3
features:
- name: refs
dtype: string
- name: trans
dtype: string
splits:
- name: train
num_bytes: 707952
num_examples: 5293
- name: test
num_bytes: 79465
num_examples: 662
- name: validation
num_bytes: 77440
num_examples: 662
download_size: 525969
dataset_size: 864857
- config_name: prob-0.4
features:
- name: refs
dtype: string
- name: trans
dtype: string
splits:
- name: train
num_bytes: 708853
num_examples: 5293
- name: test
num_bytes: 79806
num_examples: 662
- name: validation
num_bytes: 77283
num_examples: 662
download_size: 536929
dataset_size: 865942
- config_name: prob-0.5
features:
- name: refs
dtype: string
- name: trans
dtype: string
splits:
- name: train
num_bytes: 709628
num_examples: 5293
- name: test
num_bytes: 79791
num_examples: 662
- name: validation
num_bytes: 77038
num_examples: 662
download_size: 545198
dataset_size: 866457
configs:
- config_name: prob-0.1
data_files:
- split: train
path: prob-0.1/train-*
- split: test
path: prob-0.1/test-*
- split: validation
path: prob-0.1/validation-*
- config_name: prob-0.2
data_files:
- split: train
path: prob-0.2/train-*
- split: test
path: prob-0.2/test-*
- split: validation
path: prob-0.2/validation-*
- config_name: prob-0.3
data_files:
- split: train
path: prob-0.3/train-*
- split: test
path: prob-0.3/test-*
- split: validation
path: prob-0.3/validation-*
- config_name: prob-0.4
data_files:
- split: train
path: prob-0.4/train-*
- split: test
path: prob-0.4/test-*
- split: validation
path: prob-0.4/validation-*
- config_name: prob-0.5
data_files:
- split: train
path: prob-0.5/train-*
- split: test
path: prob-0.5/test-*
- split: validation
path: prob-0.5/validation-*
---
|
argilla/ultrafeedback-binarized-preferences-cleaned-kto | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- text-generation
pretty_name: UltraFeedback Binarized Preferences Cleaned KTO
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: label
dtype: bool
- name: model
dtype: string
- name: average_rating
dtype: float64
- name: annotations
struct:
- name: helpfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: honesty
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: instruction_following
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: truthfulness
struct:
- name: Rating
dtype: string
- name: Rationale
dtype: string
- name: Rationale For Rating
dtype: string
- name: Type
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 673880007
num_examples: 230720
download_size: 226134542
dataset_size: 673880007
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- kto
- preference
- ultrafeedback
---
# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned) KTO
> A KTO signal transformed version of the highly loved [UltraFeedback Binarized Preferences Cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned), the preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback
This dataset represents a new iteration on top of [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/argilla/ultrafeedback-binarized-preferences),
and is the **recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback**.
Read more about Argilla's approach towards UltraFeedback binarization at [`argilla/ultrafeedback-binarized-preferences/README.md`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences/blob/main/README.md).
## Why KTO?
The [KTO paper](https://arxiv.org/abs/2402.01306) states:
- KTO matches or exceeds DPO performance at scales from 1B to 30B parameters.1 That is, taking a preference dataset of n DPO pairs and breaking it up into 2n examples for KTO can yield better generations, despite the model ostensibly learning from a weaker signal.
- KTO can handle extreme data imbalances, matching DPO performance while using up to 90% fewer desirable examples (i.e., examples of good generations). Its success thus cannot be ascribed to the alignment data being sourced from a preference dataset.
- When the pretrained model is sufficiently good, one can skip supervised finetuning and go straight to KTO without a loss in generation quality. In contrast, we find that without doing SFT first, DPO-aligned models are significantly worse at all scales.
## Reproduce KTO Transformation
Orginal [UltraFeedback binarized prefrence cleaned DPO dataset](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)
<a target="_blank" href="https://colab.research.google.com/drive/10MwyxzcQogwO8e1ZcVu7aGTQvjXWpFuD?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> |
malteos/m_hellaswag | ---
configs:
- config_name: ar
data_files:
- split: validation
path: ar_validation*
dataset_info:
features: # "endings": datasets.features.Sequence(datasets.Value("string")),
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_a
dtype: string
- name: ctx
dtype: string
- name: endings
dtype: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
splits:
- name: validation
---
Mirror of https://github.com/nlp-uoregon/mlmm-evaluation |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-22000 | ---
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: 661232
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
abhilashpotluri/lfqa_summary | ---
license: cc-by-sa-4.0
task_categories:
- summarization
language:
- en
size_categories:
- 1K<n<10K
pretty_name: lfqa_summary
---
# Dataset Card for LFQA Summary
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [Repo](https://github.com/utcsnlp/lfqa_summary)
- **Paper:** [Concise Answers to Complex Questions: Summarization of Long-Form Answers](TODO)
- **Point of Contact:** acpotluri[at]utexas.edu
### Dataset Summary
This dataset contains summarization data for long-form question answers.
### Languages
The dataset contains data in English.
## Dataset Structure
### Data Instances
Each instance is a (question, long-form answer) pair from one of the three data sources -- ELI5, WebGPT, and NQ.
### Data Fields
Each instance is in a json dictionary format with the following fields:
* `type`: The type of the annotation, all data should have `summary` as the value.
* `dataset`: The dataset this QA pair belongs to, one of [`NQ`, `ELI5`, `Web-GPT`].
* `q_id`: The question id, same as the original NQ or ELI5 dataset.
* `a_id`: The answer id, same as the original ELI5 dataset. For NQ, we populate a dummy `a_id` (1).
* `question`: The question.
* `answer_paragraph`: The answer paragraph.
* `answer_sentences`: The list of answer sentences, tokenzied from the answer paragraph.
* `summary_sentences`: The list of summary sentence index (starting from 1).
* `is_summary_count`: The list of count of annotators selecting this sentence as summary for the sentence in `answer_sentences`.
* `is_summary_1`: List of boolean value indicating whether annotator one selected the corresponding sentence as a summary sentence.
* `is_summary_2`: List of boolean value indicating whether annotator two selected the corresponding sentence as a summary sentence.
* `is_summary_3`: List of boolean value indicating whether annotator three selected the corresponding sentence as a summary sentence.
### Data Splits
The train/dev/test are provided in the uploaded dataset.
## Dataset Creation
Please refer to our [paper](TODO) and datasheet for details on dataset creation, annotation process, and discussion of limitations.
## Additional Information
### Licensing Information
https://creativecommons.org/licenses/by-sa/4.0/legalcode
### Citation Information
```
@inproceedings{TODO,
title = {Concise Answers to Complex Questions: Summarization of Long-Form Answers},
author = {Potluri,Abhilash and Xu, Fangyuan and Choi, Eunsol},
year = 2023,
booktitle = {Proceedings of the Annual Meeting of the Association for Computational Linguistics},
note = {Long paper}
}
``` |
corysilas/VzCory | ---
license: openrail
---
|
irds/mmarco_de_train | ---
pretty_name: '`mmarco/de/train`'
viewer: false
source_datasets: ['irds/mmarco_de']
task_categories:
- text-retrieval
---
# Dataset Card for `mmarco/de/train`
The `mmarco/de/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/de/train).
# Data
This dataset provides:
- `queries` (i.e., topics); count=808,731
- `qrels`: (relevance assessments); count=532,761
- `docpairs`; count=39,780,811
- For `docs`, use [`irds/mmarco_de`](https://huggingface.co/datasets/irds/mmarco_de)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_de_train', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_de_train', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
docpairs = load_dataset('irds/mmarco_de_train', 'docpairs')
for record in docpairs:
record # {'query_id': ..., 'doc_id_a': ..., 'doc_id_b': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
|
NLPC-UOM/document_alignment_dataset-Sinhala-Tamil-English | ---
task_categories:
- sentence-similarity
language:
- si
- ta
- en
---
### **Dataset summary**
This is a gold-standard benchmark dataset for document alignment, between Sinhala-English-Tamil languages.
Data had been crawled from the following news websites.
| News Source | url |
| ------------- |-----------------------------|
| Army | https://www.army.lk/ |
| Hiru | http://www.hirunews.lk |
| ITN | https://www.newsfirst.lk |
| Newsfirst | https://www.itnnews.lk |
The aligned documents have been manually annotated.
### **Dataset**
The folder structure for each news source is as follows.
```python
army
|--Sinhala
|--English
|--Tamil
|--armynews_english_sinhala.txt
|--armynews_english_tamil.txt
|--armynews_sinhala_tamil.txt
```
Sinhala/English/Tamil - contain the crawled data for the respective news source
army_news_english_sinhala.txt - contains the annotated aligned documents between English and Sinhala languages.
armynews_english_tamil.txt - contains the annotated aligned documents between English and Tamil languages.
armynews_sinhala_tamil.txt - contains the annotated aligned documents between Sinhala and Tamil languages.
## **Citation Information**
@article{fernando2022exploiting,<br/>
title={Exploiting bilingual lexicons to improve multilingual embedding-based document and sentence alignment for low-resource languages},<br/>
author={Fernando, Aloka and Ranathunga, Surangika and Sachintha, Dilan and Piyarathna, Lakmali and Rajitha, Charith},<br/>
journal={Knowledge and Information Systems},<br/>
pages={1--42},<br/>
year={2022},<br/>
publisher={Springer}<br/>
} |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-35000 | ---
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: 1089471
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
adamjweintraut/eli5_lfqa_top | ---
dataset_info:
features:
- name: index
dtype: int64
- name: q_id
dtype: string
- name: question
dtype: string
- name: best_answer
dtype: string
- name: all_answers
sequence: string
- name: num_answers
dtype: int64
- name: top_answers
sequence: string
- name: num_top_answers
dtype: int64
- name: context
dtype: string
- name: orig
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 2794678797.7618504
num_examples: 183333
- name: test
num_bytes: 349340566.11907476
num_examples: 22917
- name: validation
num_bytes: 349340566.11907476
num_examples: 22917
download_size: 2106260396
dataset_size: 3493359930.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
liuyanchen1015/MULTI_VALUE_qqp_me_coordinate_subjects | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 4841
num_examples: 19
- name: test
num_bytes: 50642
num_examples: 197
- name: train
num_bytes: 37708
num_examples: 145
download_size: 65241
dataset_size: 93191
---
# Dataset Card for "MULTI_VALUE_qqp_me_coordinate_subjects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
huggingartists/ajr | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ajr"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.216409 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/84cbe6ced3b5398a810e82a9b65cff26.1000x1000x1.png')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ajr">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">AJR</div>
<a href="https://genius.com/artists/ajr">
<div style="text-align: center; font-size: 14px;">@ajr</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/ajr).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ajr")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|142| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/ajr")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
autoevaluate/autoeval-eval-kmfoda__booksum-kmfoda__booksum-373400-1514054915 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- kmfoda/booksum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP14
metrics: []
dataset_name: kmfoda/booksum
dataset_config: kmfoda--booksum
dataset_split: test
col_mapping:
text: chapter
target: summary_text
---
# 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/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP14
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
Anees-Aslam/Cloud | ---
license: cc-by-nc-nd-4.0
---
|
murilo22/vozclone1 | ---
license: openrail
---
|
CyberHarem/hyuuga_hinata_naruto | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hyuuga_hinata (NARUTO)
This is the dataset of hyuuga_hinata (NARUTO), containing 200 images and their tags.
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)).
|
lsy641/PsyQA | ---
license: mit
---
The data is originally source from (Sun et al,2021). (Liu et al, 2023) processed the data to make it a dataset vis huggingface api with taining/validation/testing splitting
**Please cite:**
```
@misc{liu2023enhancing,
title={Enhancing Long-form Text Generation in Mental Health with Task-adaptive Tokenization},
author={Siyang Liu and Naihao Deng and Sahand Sabour and Yilin Jia and Minlie Huang and Rada Mihalcea},
year={2023},
eprint={2310.05317},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@inproceedings{sun2021psyqa,
title={PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support},
author={Sun, Hao and Lin, Zhenru and Zheng, Chujie and Liu, Siyang and Huang, Minlie},
booktitle={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
pages={1489--1503},
year={2021}
}
``` |
nirantk/dbpedia-entities-google-palm-gemini-embedding-001-100K | ---
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: embedding
sequence: float64
splits:
- name: train
num_bytes: 653564666
num_examples: 100000
download_size: 671003094
dataset_size: 653564666
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
pretty_name: 'DBPedia 100K: Gemini Google Embedding Model 001'
size_categories:
- 10K<n<100K
---
# Dataset Card for DBPedia 100K: Gemini Google Embedding Model 001
100K vectors from DBPedia!
Embedding Model: Google's latest Embedding Model 001 -- the successor to the Gecko Models!
## Dataset Details
### Dataset Description
100K Google Embeddings -- 768 dimensions
Created: December 2023
Text used for Embedding: title (string) + text (string)
Embedding Model: Google's `models/embedding-001`
- **Curated by:** [Nirant Kasliwal](https://nirantk.com/about)
- **Funded by:** [Qdrant Gmbh](https://qdrant.tech)
- **Language(s) (NLP):** English
- **License:** Apache License 2.0
## Uses
This dataset is useful for benchmarking the embedding performance, testing the vectors on an existing dataset. E.g. you can compare Google and OpenAI for the same text using this dataset.
## Dataset Creation
Unlike the OpenAI Embedding, this creation used "title" and "content" attribute of the embedding model along with `task_type="retrieval_document"`
```python
result = genai.embed_content(
model="models/embedding-001",
content="Qdrant is the best vector search engine to use with Gemini",
task_type="retrieval_document",
title="Qdrant x Gemini",
)
```
## Source Data
This dataset is a slice of the earlier work from @KShivendu_: https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M/
The 1M dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity
From those 1M, I selected 100K at random and created embedding from them.
### Recommendations
The dataset is released as is, I'm not aware of biases, limitations or other risks arising from the use of embedding models and datasets.
Embedding models are cryptographically secure and should not be used for security use cases.
## Dataset Card Authors
- [Nirant Kasliwal](https://nirantk.com/about/)
## Dataset Card Contact
Write to nirant [dot] kasliwal [at] qdrant.com if you have questions! |
TeeZee/dolly-15k-pirate-speech | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
- text-generation
language:
- en
pretty_name: pirate speech
---
Dataset for writing style transfer experimentation based on article:
https://ai-r.com/blog/pirate-linguistics-and-tone-of-voice-fine-tuning-llms-to-talk-like-swashbucklers
Only responses are in 'pirate speech'
arrr python library was used to simply change original responses to 'pirate speech' responses
https://pypi.org/project/arrr/ |
liuyanchen1015/MULTI_VALUE_mnli_it_is_non_referential | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 60052
num_examples: 288
- name: dev_mismatched
num_bytes: 76812
num_examples: 317
- name: test_matched
num_bytes: 45727
num_examples: 222
- name: test_mismatched
num_bytes: 68535
num_examples: 292
- name: train
num_bytes: 2157021
num_examples: 10552
download_size: 1446926
dataset_size: 2408147
---
# Dataset Card for "MULTI_VALUE_mnli_it_is_non_referential"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sudeshna84/samanantar-bn-hi | ---
task_categories:
- translation
language:
- hi
- bn
pretty_name: samanantar
--- |
reshinthadith/dfg_augmented_mbpp | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: output
dtype: string
- name: code
dtype: string
splits:
- name: train
num_bytes: 32138
num_examples: 95
download_size: 17897
dataset_size: 32138
---
# Dataset Card for "dfg_augmented_mbpp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chetahy0711/CS6301_sampledata | ---
dataset_info:
features:
- name: image
dtype: image
- name: expression
dtype: string
- name: img_width
dtype: int64
- name: img_height
dtype: int64
- name: x
dtype: float64
- name: y
dtype: float64
- name: w
dtype: float64
- name: h
dtype: float64
splits:
- name: train
num_bytes: 3093853.0
num_examples: 20
download_size: 3094944
dataset_size: 3093853.0
---
# Dataset Card for "CS6301_sampledata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/kiyoshimo_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kiyoshimo/清霜/清霜 (Kantai Collection)
This is the dataset of kiyoshimo/清霜/清霜 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `long_hair, grey_hair, ahoge, twintails, low_twintails, hair_between_eyes, very_long_hair, grey_eyes, bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 445.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiyoshimo_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 290.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiyoshimo_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1044 | 575.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiyoshimo_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 405.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiyoshimo_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1044 | 751.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kiyoshimo_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kiyoshimo_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, bowtie, dress, grey_pantyhose, long_sleeves, looking_at_viewer, school_uniform, solo, white_shirt, halterneck, smile, white_background, multicolored_hair, simple_background |
| 1 | 8 |  |  |  |  |  | 1girl, bowtie, dress, looking_at_viewer, school_uniform, simple_background, solo, white_shirt, single_hair_bun, smile, white_background, halterneck, long_sleeves, upper_body, blush, one-hour_drawing_challenge, open_mouth, twitter_username |
| 2 | 8 |  |  |  |  |  | 1girl, bowtie, long_sleeves, school_uniform, solo, white_shirt, grey_pantyhose, looking_at_viewer, open_mouth, white_background, simple_background, :d, machinery, sleeveless_dress, turret, blush, teeth |
| 3 | 15 |  |  |  |  |  | 1girl, bowtie, grey_pantyhose, school_uniform, solo, white_shirt, halterneck, full_body, lace-up_boots, long_sleeves, single_hair_bun, smile, standing, looking_at_viewer, open_mouth, white_background, chibi, purple_dress, simple_background |
| 4 | 5 |  |  |  |  |  | blush, looking_at_viewer, navel, nipples, nude, pussy, small_breasts, 1girl, solo, uncensored, cleft_of_venus, open_mouth, :d, blue_eyes, collarbone, hair_ribbon, loli, purple_eyes, standing |
| 5 | 7 |  |  |  |  |  | 1girl, alternate_costume, blue_hair, full_body, looking_at_viewer, multicolored_hair, solo, simple_background, smile, white_socks, twitter_username, white_background, bag, holding, long_sleeves, standing, white_shirt, black_footwear, dress, flower, mary_janes, open_mouth |
| 6 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, nipples, panty_pull, polka_dot_panties, small_breasts, solo, green_panties, navel, pussy, smile, pantyhose_pull, bow_panties, bra_lift, dakimakura_(medium), grey_pantyhose, on_back, open_shirt |
| 7 | 11 |  |  |  |  |  | 1girl, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, wrist_cuffs, purple_leotard, solo, strapless_leotard, grey_pantyhose, small_breasts, white_background, rabbit_tail, simple_background, aqua_bowtie, covered_navel, fishnet_pantyhose, highleg_leotard, smile, thighband_pantyhose, adapted_costume, full_body, purple_footwear, cowboy_shot, dated, hair_bun, looking_at_viewer, rudder_footwear |
| 8 | 9 |  |  |  |  |  | alternate_costume, kimono, looking_at_viewer, 1girl, floral_print, hair_flower, solo, wide_sleeves, obi, blue_hair, blush, happy_new_year, long_sleeves, multicolored_hair, open_mouth, twitter_username, :d, upper_body |
| 9 | 11 |  |  |  |  |  | 1girl, solo, looking_at_viewer, single_hair_bun, smile, simple_background, white_background, collarbone, covered_navel, highleg_swimsuit, small_breasts, alternate_costume, competition_swimsuit, flat_chest, standing, barefoot, cowboy_shot, open_mouth, ass_visible_through_thighs, full_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bowtie | dress | grey_pantyhose | long_sleeves | looking_at_viewer | school_uniform | solo | white_shirt | halterneck | smile | white_background | multicolored_hair | simple_background | single_hair_bun | upper_body | blush | one-hour_drawing_challenge | open_mouth | twitter_username | :d | machinery | sleeveless_dress | turret | teeth | full_body | lace-up_boots | standing | chibi | purple_dress | navel | nipples | nude | pussy | small_breasts | uncensored | cleft_of_venus | blue_eyes | collarbone | hair_ribbon | loli | purple_eyes | alternate_costume | blue_hair | white_socks | bag | holding | black_footwear | flower | mary_janes | panty_pull | polka_dot_panties | green_panties | pantyhose_pull | bow_panties | bra_lift | dakimakura_(medium) | on_back | open_shirt | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | wrist_cuffs | purple_leotard | strapless_leotard | rabbit_tail | aqua_bowtie | covered_navel | fishnet_pantyhose | highleg_leotard | thighband_pantyhose | adapted_costume | purple_footwear | cowboy_shot | dated | hair_bun | rudder_footwear | kimono | floral_print | hair_flower | wide_sleeves | obi | happy_new_year | highleg_swimsuit | competition_swimsuit | flat_chest | barefoot | ass_visible_through_thighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------|:-----------------|:---------------|:--------------------|:-----------------|:-------|:--------------|:-------------|:--------|:-------------------|:--------------------|:--------------------|:------------------|:-------------|:--------|:-----------------------------|:-------------|:-------------------|:-----|:------------|:-------------------|:---------|:--------|:------------|:----------------|:-----------|:--------|:---------------|:--------|:----------|:-------|:--------|:----------------|:-------------|:-----------------|:------------|:-------------|:--------------|:-------|:--------------|:--------------------|:------------|:--------------|:------|:----------|:-----------------|:---------|:-------------|:-------------|:--------------------|:----------------|:-----------------|:--------------|:-----------|:----------------------|:----------|:-------------|:------------------|:-------------------|:----------------|:--------------|:--------------|:-----------------|:--------------------|:--------------|:--------------|:----------------|:--------------------|:------------------|:----------------------|:------------------|:------------------|:--------------|:--------|:-----------|:------------------|:---------|:---------------|:--------------|:---------------|:------|:-----------------|:-------------------|:-----------------------|:-------------|:-----------|:-----------------------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | | X | X | X | X | X | X | | | X | | X | | | X | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 15 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | X | X | | X | X | | | | X | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | | | X | | X | | | | | | | | | X | | X | | X | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | X | | X | X | | X | X | | X | X | X | X | | | | | X | X | | | | | | X | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | | | X | | X | | X | | | X | | | | | | X | | | | | | | | | | | | | | X | X | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 11 |  |  |  |  |  | X | | | X | | X | | X | | | X | X | | X | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 8 | 9 |  |  |  |  |  | X | | | | X | X | | X | | | | | X | | | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | |
| 9 | 11 |  |  |  |  |  | X | | | | | X | | X | | | X | X | | X | X | | | | X | | | | | | | X | | X | | | | | | | X | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | | | | | | | | X | X | X | X | X |
|
arieg/fma_small_images | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '000002'
'1': '000005'
'2': '000010'
'3': '000140'
'4': '000141'
'5': 000148
'6': 000182
'7': 000190
'8': 000193
'9': 000194
'10': 000197
'11': '000200'
'12': '000203'
'13': '000204'
'14': '000207'
'15': '000210'
'16': '000211'
'17': '000212'
'18': '000213'
'19': '000255'
'20': '000256'
'21': 000368
'22': '000424'
'23': 000459
'24': '000534'
'25': '000540'
'26': '000546'
'27': '000574'
'28': '000602'
'29': '000615'
'30': '000620'
'31': '000621'
'32': '000625'
'33': '000666'
'34': '000667'
'35': '000676'
'36': 000690
'37': 000694
'38': 000695
'39': '000704'
'40': '000705'
'41': '000706'
'42': '000707'
'43': 000708
'44': 000709
'45': '000714'
'46': '000715'
'47': '000716'
'48': 000718
'49': '000777'
'50': 000814
'51': 000821
'52': 000822
'53': 000825
'54': 000853
'55': 000890
'56': 000892
'57': 000897
'58': 000993
'59': 000995
'60': 000997
'61': 000998
'62': 001039
'63': '001040'
'64': '001066'
'65': 001069
'66': '001073'
'67': '001075'
'68': 001082
'69': 001083
'70': 001087
'71': '001102'
'72': 001193
'73': 001195
'74': 001196
'75': 001197
'76': 001249
'77': 001259
'78': '001270'
'79': '001276'
'80': '001277'
'81': 001278
'82': '001417'
'83': '001427'
'84': '001443'
'85': 001482
'86': '001510'
'87': '001544'
'88': '001642'
'89': '001644'
'90': 001649
'91': '001661'
'92': '001663'
'93': '001666'
'94': '001673'
'95': 001680
'96': 001681
'97': 001682
'98': 001683
'99': 001684
'100': 001685
'101': 001686
'102': 001687
'103': 001688
'104': 001689
'105': '001701'
'106': '001702'
'107': '001703'
'108': '001704'
'109': '001706'
'110': '001720'
'111': '001732'
'112': '001733'
'113': '001735'
'114': '001736'
'115': 001883
'116': 001891
'117': 001893
'118': 001924
'119': 001925
'120': 001929
'121': 001930
'122': '002012'
'123': 002096
'124': 002097
'125': 002099
'126': '003263'
'127': '003264'
'128': '003265'
'129': '003266'
'130': '003270'
'131': '003271'
'132': '003272'
'133': '003273'
'134': '003274'
'135': 003492
'136': '003532'
'137': '003533'
'138': '003534'
'139': '003535'
'140': '003537'
'141': 003538
'142': '003573'
'143': 003598
'144': '003624'
'145': '003707'
'146': 003708
'147': '003720'
'148': '003721'
'149': '003722'
'150': '003724'
'151': '003725'
'152': '003761'
'153': '003762'
'154': '003763'
'155': '003765'
'156': '003766'
'157': '003775'
'158': '003776'
'159': '003777'
'160': 003778
'161': 003779
'162': 003832
'163': 003833
'164': 003840
'165': 003880
'166': 003895
'167': 003896
'168': 003904
'169': 003905
'170': 003906
'171': 003908
'172': 003909
'173': 003910
'174': 003911
'175': 003912
'176': 003913
'177': 003920
'178': 003921
'179': 003950
'180': '004013'
'181': '004017'
'182': '004022'
'183': '004037'
'184': '004066'
'185': '004067'
'186': 004068
'187': 004069
'188': '004070'
'189': '004071'
'190': '004072'
'191': '004073'
'192': '004074'
'193': '004075'
'194': '004076'
'195': '004077'
'196': 004078
'197': 004079
'198': 004080
'199': 004091
'200': 004092
'201': 004093
'202': 004094
'203': 004095
'204': 004096
'205': 004097
'206': 004098
'207': 004099
'208': '004100'
'209': '004101'
'210': '004102'
'211': '004103'
'212': 004108
'213': '004232'
'214': '004233'
'215': '004234'
'216': '004235'
'217': '004236'
'218': 004239
'219': '004450'
'220': '004507'
'221': 004508
'222': 004509
'223': '004510'
'224': '004511'
'225': 004519
'226': '004520'
'227': '004521'
'228': '004522'
'229': 004682
'230': 004684
'231': 004685
'232': 004688
'233': '004777'
'234': 004778
'235': 004779
'236': 004780
'237': 004781
'238': 004782
'239': 004784
'240': 004785
'241': 004786
'242': 004787
'243': 004788
'244': 004799
'245': 004835
'246': 004836
'247': 004838
'248': 004846
'249': 004848
'250': 004849
'251': '005006'
'252': '005156'
'253': '005157'
'254': 005158
'255': 005159
'256': 005169
'257': '005170'
'258': '005171'
'259': 005191
'260': '005264'
'261': 005268
'262': '005376'
'263': 005381
'264': '005521'
'265': 005879
'266': 005936
'267': 005940
'268': 006329
'269': '006330'
'270': '006331'
'271': '006332'
'272': '006333'
'273': '006342'
'274': '006354'
'275': '006357'
'276': 006358
'277': '006360'
'278': '006363'
'279': '006366'
'280': '006367'
'281': 006368
'282': '006370'
'283': '006372'
'284': '006373'
'285': '006376'
'286': 006379
'287': 006380
'288': 006381
'289': 006382
'290': 006383
'291': 006385
'292': 006387
'293': 006389
'294': 006390
'295': 006393
'296': 006394
'297': 006396
'298': '006406'
'299': '006407'
'300': 006439
'301': '006440'
'302': '006442'
'303': '006443'
'304': 006448
'305': 006459
'306': '006461'
'307': '006463'
'308': '006467'
'309': 006469
'310': '006517'
'311': 006519
'312': '006603'
'313': '006605'
'314': '006606'
'315': '006607'
'316': 006608
'317': 006609
'318': '006610'
'319': '006611'
'320': '006674'
'321': '006675'
'322': '006677'
'323': 006679
'324': 006680
'325': 006684
'326': '006762'
'327': '006776'
'328': 006778
'329': 006779
'330': 006782
'331': 006783
'332': 006788
'333': 006802
'334': 006803
'335': 006854
'336': 006855
'337': 006856
'338': 006857
'339': '007011'
'340': '007373'
'341': '007374'
'342': '007375'
'343': '007376'
'344': '007377'
'345': 007378
'346': 007379
'347': 007381
'348': 007383
'349': 007385
'350': 007386
'351': 007388
'352': 007391
'353': 007393
'354': 007481
'355': 007482
'356': 007483
'357': 007487
'358': 007488
'359': 007489
'360': 007490
'361': 007491
'362': 007492
'363': 007495
'364': '007526'
'365': '007527'
'366': 007528
'367': 007529
'368': 007548
'369': '007554'
'370': 007709
'371': '007710'
'372': '007711'
'373': '007712'
'374': '007713'
'375': 007872
'376': 008056
'377': 008208
'378': 008256
'379': 008259
'380': 008261
'381': 008345
'382': 008357
'383': 008363
'384': 008372
'385': 008416
'386': 009152
'387': 009155
'388': 009307
'389': 009476
'390': 009477
'391': 009491
'392': 009505
'393': 009511
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'395': 009513
'396': 009550
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'399': 009557
'400': 009559
'401': 009560
'402': 009678
'403': 009721
'404': 009846
'405': 009887
'406': 009888
'407': 009918
'408': 009962
'409': 010186
'410': 010192
'411': '010250'
'412': '010374'
'413': '010375'
'414': '010376'
'415': '010377'
'416': 010381
'417': 010382
'418': 010383
'419': 010384
'420': 010385
'421': 010386
'422': 010387
'423': 010388
'424': 010389
'425': '010435'
'426': 010438
'427': 010439
'428': '010440'
'429': '010441'
'430': '010442'
'431': '010443'
'432': '010444'
'433': '010447'
'434': 010458
'435': 010480
'436': 010481
'437': 010485
'438': '010521'
'439': '010527'
'440': '010535'
'441': '010541'
'442': '010575'
'443': '010577'
'444': 010668
'445': 010669
'446': '010670'
'447': '010671'
'448': '010672'
'449': '010673'
'450': '010674'
'451': '010675'
'452': '010676'
'453': '010677'
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'455': 010679
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'458': 010693
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'462': 010697
'463': 010698
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'468': 010808
'469': 010809
'470': 010810
'471': 010983
'472': 010992
'473': 010993
'474': 011019
'475': '011020'
'476': 011059
'477': 011198
'478': 011199
'479': '011200'
'480': '011204'
'481': '011206'
'482': '011234'
'483': '011237'
'484': 011239
'485': '011242'
'486': '011261'
'487': '011262'
'488': '011264'
'489': 011268
'490': 011298
'491': 011299
'492': '011306'
'493': '011333'
'494': '011334'
'495': '011503'
'496': '011504'
'497': '011505'
'498': 011508
'499': '011544'
'500': 011638
'501': '011671'
'502': '011672'
'503': '011673'
'504': '011674'
'505': '011675'
'506': '011677'
'507': 011679
'508': 011681
'509': 011682
'510': 011683
'511': '011763'
'512': '011764'
'513': '011765'
'514': '011766'
'515': '011767'
'516': 011768
'517': 011769
'518': '011770'
'519': '011771'
'520': '011772'
'521': '011773'
'522': '011774'
'523': '011775'
'524': '011776'
'525': '011777'
'526': 011778
'527': 011779
'528': 011780
'529': 011781
'530': 011782
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'536': 011788
'537': 011789
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'541': 011793
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'548': 011862
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'555': 011920
'556': 011921
'557': 011922
'558': 011933
'559': 011937
'560': 011942
'561': 011946
'562': 011947
'563': 011951
'564': '012045'
'565': '012046'
'566': '012047'
'567': 012048
'568': 012049
'569': '012050'
'570': '012051'
'571': '012052'
'572': '012053'
'573': 012058
'574': 012059
'575': '012060'
'576': '012061'
'577': '012062'
'578': '012064'
'579': '012065'
'580': '012066'
'581': '012067'
'582': 012109
'583': '012146'
'584': '012147'
'585': '012173'
'586': '012174'
'587': 012179
'588': 012188
'589': 012189
'590': '012346'
'591': 012348
'592': 012349
'593': '012350'
'594': '012351'
'595': '012352'
'596': '012353'
'597': '012355'
'598': '012376'
'599': 012387
'600': 012390
'601': 012394
'602': 012481
'603': 012482
'604': 012484
'605': 012485
'606': 012486
'607': 012487
'608': 012488
'609': 012489
'610': 012490
'611': 012508
'612': '012513'
'613': '012514'
'614': 012518
'615': '012521'
'616': '012526'
'617': '012527'
'618': '012530'
'619': '012531'
'620': '012532'
'621': '012537'
'622': '012551'
'623': '012552'
'624': '012654'
'625': 012690
'626': 012691
'627': 012692
'628': '012737'
'629': 012985
'630': 012986
'631': 013191
'632': 013197
'633': 013199
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splits:
- name: train
num_bytes: 440849403.831
num_examples: 7997
download_size: 432687499
dataset_size: 440849403.831
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
colettemb/288-demo | ---
license: pddl
---
|
BangumiBase/fullmetalalchemist | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Fullmetal Alchemist
This is the image base of bangumi Fullmetal Alchemist, we detected 44 characters, 5107 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1190 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 164 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 20 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 61 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 56 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 80 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 384 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 427 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 179 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 73 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 93 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 33 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 84 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 113 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 95 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 129 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 318 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 187 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 26 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 48 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 78 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 54 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 53 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 97 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 142 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 217 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 12 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 246 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 58 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 32 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 19 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 15 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 39 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 23 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 9 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 14 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 23 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 11 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 12 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 13 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 18 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 40 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 14 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 108 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
AndyLiu0104/Soldering-Data-Tiny-Complete-Sentence | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 18013335.875
num_examples: 10481
download_size: 11576663
dataset_size: 18013335.875
---
# Dataset Card for "Soldering-Data-Tiny-Complete-Sentence"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Amanaccessassist/distileplay | ---
dataset_info:
features:
- name: input
dtype: string
- name: label
dtype:
class_label:
names:
'0': Negative
'1': Positive
'2': Neutral
splits:
- name: train
num_bytes: 32764330.300624426
num_examples: 368145
- name: test
num_bytes: 14041906.699375574
num_examples: 157777
download_size: 26351556
dataset_size: 46806237.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
huuuyeah/MeetingBank_Audio | ---
license: cc-by-nc-sa-4.0
---
## Overview
MeetingBank, a benchmark dataset created from the city councils of 6 major U.S. cities to supplement existing datasets. It contains 1,366 meetings with over 3,579 hours of video, as well as transcripts, PDF documents of meeting minutes, agenda, and other metadata. On average, a council meeting is 2.6 hours long and its transcript contains over 28k tokens, making it a valuable testbed for meeting summarizers and for extracting structure from meeting videos. The datasets contains 6,892 segment-level summarization instances for training and evaluating of performance.
## Acknowledgement
Please cite the following paper in work that makes use of this dataset:
[MeetingBank: A Benchmark Dataset for Meeting Summarization](https://arxiv.org/abs/2305.17529)\
Yebowen Hu, Tim Ganter, Hanieh Deilamsalehy, Franck Dernoncourt, Hassan Foroosh, Fei Liu\
In main conference of Association for Computational Linguistics (ACL'23), Toronto, Canada.
## Bibtex
```
@inproceedings{hu-etal-2023-meetingbank,
title = "MeetingBank: A Benchmark Dataset for Meeting Summarization",
author = "Yebowen Hu and Tim Ganter and Hanieh Deilamsalehy and Franck Dernoncourt and Hassan Foroosh and Fei Liu",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)",
month = July,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
}
```
## Resources
MeetingBank dataset will be hosted at Zenodo. The audio files of each meeting will be hosted individually on Huggingface. All resources will includes meeting audio, transcripts, meetingbank main JSON file, summaries from 6 systems and human annotations.
**Summary, Segments Transcripts and VideoList**: [zenodo](https://zenodo.org/record/7989108)
**Meeting Audios**: [HuggingFace](https://huggingface.co/datasets/huuuyeah/MeetingBank)
Some scripts can be found in github repo [MeetingBank_Utils](https://github.com/YebowenHu/MeetingBank-utils)
|
arize-ai/fashion_mnist_quality_drift | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|imdb
task_categories:
- image-classification
task_ids:
- multi-class-classification
pretty_name: sentiment-classification-reviews-with-drift
---
# Dataset Card for `reviews_with_drift`
## 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
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[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 [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. |
CVasNLPExperiments/VQAv2_testdev_no_image_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_
num_bytes: 94166
num_examples: 1000
download_size: 30018
dataset_size: 94166
---
# Dataset Card for "VQAv2_testdev_no_image_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
frodoclicks/train | ---
license: mit
---
|
WforGodot/add345_1 | ---
license: openrail
---
|
AdapterOcean/code_instructions_standardized_cluster_9_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 35793557
num_examples: 37842
download_size: 16026961
dataset_size: 35793557
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "code_instructions_standardized_cluster_9_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
passing2961/dialogcc | ---
license: cc-by-nc-sa-4.0
language:
- en
pretty_name: DialogCC
size_categories:
- 1K<n<10K
multilinguality:
- monolingual
annotation_creators:
- machine-generated
tags:
- multi-modal dialogue
source_datasets:
- BlendedSkillTalk
- DailyDialog
- Persona-Chat
- Wizard-of-Wikipedia
- EmpatheticDialogues
- CC3M
task_ids:
- conversational
task_categories:
- text-to-image
- image-to-text
splits:
- name: train
num_examples: 68402
- name: valid
num_examples: 7644
- name: test
num_examples: 7324
dataset_size: 83,370
---
# Dataset Card for DialogCC
## Dataset Description
- **Repository:** [Code](https://github.com/passing2961/dialogcc)
- **Paper:** [DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset](https://arxiv.org/abs/2212.04119)
- **Point of Contact:** [Young-Jun Lee](mailto:yj2961@kaist.ac.kr)
## Dataset Summary
DialogCC is a publicly available high-quality and diverse multi-modal dialogue dataset that contains various images per dialogue and utterance, respectively.
## Languages
English
## Dataset Structure
field | type | description
--- | --- | ---
`dialogue_id` | str | the identifier for the dialogue, containing the original text-only dialogue type (e.g., bst) and index
`dialogue` | list of dict | the dialogue where each dict entry includes {utterance_idx, utterance, speaker, rationale, shared_image, description}
`split` | str | the split information: {train, valid, test}
For the original text-only dialogue dataset, we have five types: "bst" (BlendedSkillTalk), "empathy" (EmpatheticDialogues), "daily" (DailyDialog), "wow" (Wizard-of-Wikipedia), and "persona" (Persona-Chat).
In the "dialogue" field, the "shared_image" field is a list of dict. Each dict entry comprises two key pieces of information: "image_url" and "caption", both of which are sourced from the CC3M dataset.
**Note:** We prompt GPT-4 to generate appropriate image-sharing moments within dialogues, including the utterance, the speaker, the rationale behind sharing,
and a description of the image. Due to the nature of the generation process, GPT-4 may produce different descriptions, speakers, or rationales at the same image-sharing turn. Consequently, the same dialogue_id can appear across different instances within the dataset, representing these variations.
## Dataset Creation
To create DialogCC, we propose a fully automatic framework for creating a multi-modal dialogue dataset that involves three main steps: (1) Collect source dialogue datasets (i.e., EmpatheticDialogues, Persona-Chat, DailyDialog, Wizard-of-Wikipedia, Blended Skill Talk) and source image-caption pair dataset (i.e., CC3M), (2) align most appropriate images to the dialogue by leveraging GPT-4 and CLIP, and (3) filter inappropriate images based on CLIP similarity for image-image consistency. For more details, please refer to our [paper](https://arxiv.org/abs/2212.04119).
### Further Details, Social Impacts, and Limitations
Please refer to our [paper](https://arxiv.org/abs/2212.04119).
## Additional Information
For a brief summary of our paper, please see this [project page](https://dialogcc.github.io).
## Limitations
Please refer to the Limitation section in our [paper](https://arxiv.org/abs/2212.04119).
### Recommendations
Despite our efforts to create a high-quality and diverse multi-modal dialogue dataset, it still contains harmful content, such as social bias. Moreover, since DialogCC incorporates dialogues from the DailyDialog dataset, which is licensed under CC BY-NC-SA 4.0, DialogCC is shared under the license CC-BY-NC-SA 4.0. Therefore, we strongly recommend using our dataset for academic and research purposes.
### Citation
Please cite our work if you find the resources in this repository useful:
```
TBD
```
|
Mediform/sharegpt-french | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 32698024
num_examples: 5616
download_size: 14734838
dataset_size: 32698024
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zolak/twitter_dataset_50_1713146806 | ---
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: 410994
num_examples: 1010
download_size: 220233
dataset_size: 410994
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zolak/twitter_dataset_78_1713211298 | ---
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: 1521573
num_examples: 3760
download_size: 759876
dataset_size: 1521573
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mmathys/openai-moderation-api-evaluation | ---
license: mit
task_categories:
- text-classification
language: en
pretty_name: OpenAI Moderation API Validation Dataset
---
# Evaluation dataset for the paper "A Holistic Approach to Undesired Content Detection"
The evaluation dataset `data/samples-1680.jsonl.gz` is the test set used in [this paper](https://arxiv.org/abs/2208.03274).
Each line contains information about one sample in a JSON object and each sample is labeled according to our taxonomy. The category label is a binary flag, but if it does not include in the JSON, it means we do not know the label.
| Category | Label | Definition |
| -------- | ----- | ---------- |
| sexual | `S` | Content meant to arouse sexual excitement, such as the description of sexual activity, or that promotes sexual services (excluding sex education and wellness). |
| hate | `H` | Content that expresses, incites, or promotes hate based on race, gender, ethnicity, religion, nationality, sexual orientation, disability status, or caste. |
| violence | `V` | Content that promotes or glorifies violence or celebrates the suffering or humiliation of others. |
| harassment | `HR` | Content that may be used to torment or annoy individuals in real life, or make harassment more likely to occur. |
| self-harm | `SH` | Content that promotes, encourages, or depicts acts of self-harm, such as suicide, cutting, and eating disorders. |
| sexual/minors | `S3` | Sexual content that includes an individual who is under 18 years old. |
| hate/threatening | `H2` | Hateful content that also includes violence or serious harm towards the targeted group. |
| violence/graphic | `V2` | Violent content that depicts death, violence, or serious physical injury in extreme graphic detail. |
Parsed from the GitHub repo: https://github.com/openai/moderation-api-release |
MatsuoDochiai/Golinz | ---
license: openrail
---
|
AdapterOcean/math_dataset_standardized_cluster_3 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 180711025
num_examples: 18655
download_size: 50145228
dataset_size: 180711025
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "math_dataset_standardized_cluster_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chathuranga-jayanath/context-5-predict-token-for-fine-tune | ---
dataset_info:
features:
- name: id
dtype: int64
- name: filepath
dtype: string
- name: start_bug_line
dtype: int64
- name: end_bug_line
dtype: int64
- name: bug
dtype: string
- name: fix
dtype: string
- name: ctx
dtype: string
splits:
- name: train
num_bytes: 179586
num_examples: 305
- name: validation
num_bytes: 21916
num_examples: 37
- name: test
num_bytes: 22956
num_examples: 37
download_size: 60857
dataset_size: 224458
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
RUCAIBox/Question-Generation | ---
language:
- en
multilinguality:
- monolingual
task_categories:
- text2text-generation
task_ids: []
tags:
- question-generation
---
This is the question generation datasets collected by TextBox, including:
- SQuAD (squadqg)
- CoQA (coqaqg)
- NewsQA (newsqa)
- HotpotQA (hotpotqa)
- MS MARCO (marco)
- MSQG (msqg)
- NarrativeQA (nqa)
- QuAC (quac).
The detail and leaderboard of each dataset can be found in [TextBox page](https://github.com/RUCAIBox/TextBox#dataset). |
kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-46000 | ---
dataset_info:
features:
- name: tables
sequence: string
- name: table_names
sequence: string
- name: query
dtype: string
- name: answer
dtype: string
- name: source
dtype: string
- name: target
dtype: string
- name: source_latex
dtype: string
- name: target_latex
dtype: string
- name: source_html
dtype: string
- name: target_html
dtype: string
- name: source_markdown
dtype: string
- name: target_markdown
dtype: string
splits:
- name: train
num_bytes: 5137284719
num_examples: 1000
download_size: 1094992564
dataset_size: 5137284719
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_bardsai__jaskier-7b-dpo-v5.6 | ---
pretty_name: Evaluation run of bardsai/jaskier-7b-dpo-v5.6
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6)\
\ 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_bardsai__jaskier-7b-dpo-v5.6\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-17T15:44:22.548008](https://huggingface.co/datasets/open-llm-leaderboard/details_bardsai__jaskier-7b-dpo-v5.6/blob/main/results_2024-02-17T15-44-22.548008.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\": 0.6501937924638518,\n\
\ \"acc_stderr\": 0.032025249091365754,\n \"acc_norm\": 0.6494469200952948,\n\
\ \"acc_norm_stderr\": 0.03269529478578274,\n \"mc1\": 0.6303549571603427,\n\
\ \"mc1_stderr\": 0.01689818070697388,\n \"mc2\": 0.7781424860062839,\n\
\ \"mc2_stderr\": 0.013751565023330138\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7090443686006825,\n \"acc_stderr\": 0.01327307786590759,\n\
\ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869147\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7137024497112129,\n\
\ \"acc_stderr\": 0.004511063351278702,\n \"acc_norm\": 0.8899621589324835,\n\
\ \"acc_norm_stderr\": 0.00312297363203947\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-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\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322663,\n\
\ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322663\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\
: 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542126,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542126\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\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\": 0.4649122807017544,\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\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778394,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778394\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542126,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542126\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\": 0.5073891625615764,\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\
: 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\
\ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.02403548967633508,\n \
\ \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.02403548967633508\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066485,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066485\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\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\"\
: 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\
acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\
acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931055,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931055\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n \
\ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993469,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993469\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n\
\ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4402234636871508,\n\
\ \"acc_stderr\": 0.016602564615049942,\n \"acc_norm\": 0.4402234636871508,\n\
\ \"acc_norm_stderr\": 0.016602564615049942\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.02575586592263295,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.02575586592263295\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4758800521512386,\n\
\ \"acc_stderr\": 0.012755368722863937,\n \"acc_norm\": 0.4758800521512386,\n\
\ \"acc_norm_stderr\": 0.012755368722863937\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \
\ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\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\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\
\ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.025870646766169136\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\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6303549571603427,\n\
\ \"mc1_stderr\": 0.01689818070697388,\n \"mc2\": 0.7781424860062839,\n\
\ \"mc2_stderr\": 0.013751565023330138\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8453038674033149,\n \"acc_stderr\": 0.010163172650433535\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6967399545109931,\n \
\ \"acc_stderr\": 0.012661502663418697\n }\n}\n```"
repo_url: https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6
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_17T15_44_22.548008
path:
- '**/details_harness|arc:challenge|25_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|gsm8k|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hellaswag|10_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T15-44-22.548008.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T15-44-22.548008.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-17T15-44-22.548008.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_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-17T15-44-22.548008.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- '**/details_harness|winogrande|5_2024-02-17T15-44-22.548008.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-17T15-44-22.548008.parquet'
- config_name: results
data_files:
- split: 2024_02_17T15_44_22.548008
path:
- results_2024-02-17T15-44-22.548008.parquet
- split: latest
path:
- results_2024-02-17T15-44-22.548008.parquet
---
# Dataset Card for Evaluation run of bardsai/jaskier-7b-dpo-v5.6
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [bardsai/jaskier-7b-dpo-v5.6](https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6) 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_bardsai__jaskier-7b-dpo-v5.6",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-17T15:44:22.548008](https://huggingface.co/datasets/open-llm-leaderboard/details_bardsai__jaskier-7b-dpo-v5.6/blob/main/results_2024-02-17T15-44-22.548008.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": 0.6501937924638518,
"acc_stderr": 0.032025249091365754,
"acc_norm": 0.6494469200952948,
"acc_norm_stderr": 0.03269529478578274,
"mc1": 0.6303549571603427,
"mc1_stderr": 0.01689818070697388,
"mc2": 0.7781424860062839,
"mc2_stderr": 0.013751565023330138
},
"harness|arc:challenge|25": {
"acc": 0.7090443686006825,
"acc_stderr": 0.01327307786590759,
"acc_norm": 0.7303754266211604,
"acc_norm_stderr": 0.012968040686869147
},
"harness|hellaswag|10": {
"acc": 0.7137024497112129,
"acc_stderr": 0.004511063351278702,
"acc_norm": 0.8899621589324835,
"acc_norm_stderr": 0.00312297363203947
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"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": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6943396226415094,
"acc_stderr": 0.028353298073322663,
"acc_norm": 0.6943396226415094,
"acc_norm_stderr": 0.028353298073322663
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542126,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542126
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"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": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.025379524910778394,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.025379524910778394
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542126,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542126
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7806451612903226,
"acc_stderr": 0.023540799358723295,
"acc_norm": 0.7806451612903226,
"acc_norm_stderr": 0.023540799358723295
},
"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": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.797979797979798,
"acc_stderr": 0.02860620428922987,
"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.02860620428922987
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9119170984455959,
"acc_stderr": 0.02045374660160103,
"acc_norm": 0.9119170984455959,
"acc_norm_stderr": 0.02045374660160103
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.658974358974359,
"acc_stderr": 0.02403548967633508,
"acc_norm": 0.658974358974359,
"acc_norm_stderr": 0.02403548967633508
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.028317533496066485,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.028317533496066485
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
"acc_stderr": 0.03943966699183629,
"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.03943966699183629
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8422018348623853,
"acc_stderr": 0.01563002297009244,
"acc_norm": 0.8422018348623853,
"acc_norm_stderr": 0.01563002297009244
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.03407632093854051,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.03407632093854051
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931055,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931055
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8059071729957806,
"acc_stderr": 0.025744902532290916,
"acc_norm": 0.8059071729957806,
"acc_norm_stderr": 0.025744902532290916
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.034981493854624714,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.034981493854624714
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070416
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7914110429447853,
"acc_stderr": 0.031921934489347235,
"acc_norm": 0.7914110429447853,
"acc_norm_stderr": 0.031921934489347235
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04697113923010212,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04697113923010212
},
"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": 0.8803418803418803,
"acc_stderr": 0.021262719400406964,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406964
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.013625556907993469,
"acc_norm": 0.8237547892720306,
"acc_norm_stderr": 0.013625556907993469
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7312138728323699,
"acc_stderr": 0.023868003262500104,
"acc_norm": 0.7312138728323699,
"acc_norm_stderr": 0.023868003262500104
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4402234636871508,
"acc_stderr": 0.016602564615049942,
"acc_norm": 0.4402234636871508,
"acc_norm_stderr": 0.016602564615049942
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7189542483660131,
"acc_stderr": 0.025738854797818733,
"acc_norm": 0.7189542483660131,
"acc_norm_stderr": 0.025738854797818733
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7106109324758842,
"acc_stderr": 0.02575586592263295,
"acc_norm": 0.7106109324758842,
"acc_norm_stderr": 0.02575586592263295
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7314814814814815,
"acc_stderr": 0.024659685185967284,
"acc_norm": 0.7314814814814815,
"acc_norm_stderr": 0.024659685185967284
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
"acc_stderr": 0.02980873964223777,
"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4758800521512386,
"acc_stderr": 0.012755368722863937,
"acc_norm": 0.4758800521512386,
"acc_norm_stderr": 0.012755368722863937
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6875,
"acc_stderr": 0.02815637344037142,
"acc_norm": 0.6875,
"acc_norm_stderr": 0.02815637344037142
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.018926082916083383,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.018926082916083383
},
"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": 0.7428571428571429,
"acc_stderr": 0.02797982353874455,
"acc_norm": 0.7428571428571429,
"acc_norm_stderr": 0.02797982353874455
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.025870646766169136,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.025870646766169136
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.6303549571603427,
"mc1_stderr": 0.01689818070697388,
"mc2": 0.7781424860062839,
"mc2_stderr": 0.013751565023330138
},
"harness|winogrande|5": {
"acc": 0.8453038674033149,
"acc_stderr": 0.010163172650433535
},
"harness|gsm8k|5": {
"acc": 0.6967399545109931,
"acc_stderr": 0.012661502663418697
}
}
```
## 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. -->
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## Dataset Card Contact
[More Information Needed] |
openlifescienceai/mmlu_clinical_knowledge | ---
dataset_info:
features:
- name: subject_name
dtype: string
- name: data
struct:
- name: Correct Answer
dtype: string
- name: Correct Option
dtype: string
- name: Options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: Question
dtype: string
- name: id
dtype: string
splits:
- name: test
num_bytes: 88710
num_examples: 265
- name: validation
num_bytes: 9355
num_examples: 29
- name: dev
num_bytes: 1662
num_examples: 5
download_size: 89460
dataset_size: 99727
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
- split: dev
path: data/dev-*
---
|
xPXXX/test_ragas | ---
license: mit
---
|
MPrabhu/textbookQA | ---
dataset_info:
features:
- name: level
dtype: string
- name: question
dtype: string
splits:
- name: train
num_bytes: 143352
num_examples: 1000
download_size: 61476
dataset_size: 143352
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jhopela/dolly_train | ---
license: openrail
---
|
Liareizz/MANUGAVASSI | ---
license: openrail
---
|
CyberHarem/kisaragi_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kisaragi/如月/如月 (Azur Lane)
This is the dataset of kisaragi/如月/如月 (Azur Lane), containing 433 images and their tags.
The core tags of this character are `animal_ears, long_hair, pink_hair, cat_ears, ribbon, cat_tail, tail, hat, bow, pink_eyes, animal_ear_fluff, tail_ornament, school_hat, cat_girl, bangs, hair_between_eyes, yellow_headwear, one_side_up, very_long_hair, purple_eyes, ears_through_headwear, hair_ribbon, red_ribbon`, 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 | 433 | 447.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kisaragi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 433 | 279.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kisaragi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1032 | 615.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kisaragi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 433 | 404.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kisaragi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1032 | 834.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kisaragi_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kisaragi_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, blue_shirt, blush, kindergarten_uniform, looking_at_viewer, solo, long_sleeves, yellow_bowtie, parted_lips, simple_background, upper_body, :o, white_background, white_sailor_collar |
| 1 | 7 |  |  |  |  |  | 1girl, jingle_bell, kindergarten_uniform, simple_background, solo, white_background, blue_shirt, tail_ribbon, white_thighhighs, yellow_skirt, looking_at_viewer, chibi |
| 2 | 6 |  |  |  |  |  | 1girl, blue_shirt, blush, kindergarten_uniform, simple_background, solo, yellow_skirt, looking_at_viewer, white_background, own_hands_together, pleated_skirt |
| 3 | 6 |  |  |  |  |  | 1girl, blue_shirt, blush, full_body, jingle_bell, kindergarten_uniform, long_sleeves, looking_at_viewer, parted_lips, pleated_skirt, red_bow, solo, tail_bell, tail_bow, white_sailor_collar, yellow_skirt, bowtie, chibi, red_eyes, twitter_username, white_thighhighs, yellow_bow, :o, own_hands_together, black_footwear, blue_background, standing_on_one_leg |
| 4 | 20 |  |  |  |  |  | 1girl, black_headwear, black_skirt, hair_bow, retrofit_(azur_lane), short_sleeves, solo, suspender_skirt, white_shirt, beret, black_bow, blush, looking_at_viewer, two_side_up, white_pantyhose, collared_shirt, jingle_bell, pleated_skirt, dress_shirt, tail_bell, tail_bow, neck_ribbon, pink_bow, pink_ribbon, striped_bow, tail_ribbon, parted_lips, black_footwear, full_body, white_background, anchor, open_mouth, shoes |
| 5 | 8 |  |  |  |  |  | 1girl, black_headwear, black_skirt, jingle_bell, short_sleeves, solo, suspender_skirt, tail_ribbon, white_shirt, two_side_up, white_pantyhose, simple_background, blush, hair_bow, retrofit_(azur_lane), white_background, school_uniform, legs, looking_at_viewer |
| 6 | 8 |  |  |  |  |  | 1boy, 1girl, blush, hetero, loli, open_mouth, flat_chest, nipples, penis, spread_legs, navel, sex, solo_focus, tears, vaginal, nude, thighhighs, bar_censor, blue_shirt, heart-shaped_pupils, kindergarten_uniform, missionary, moaning, mosaic_censoring, on_back, bell, cum_in_pussy, torso_grab |
| 7 | 24 |  |  |  |  |  | 1girl, blush, hair_bow, jingle_bell, obi, solo, wide_sleeves, hair_flower, looking_at_viewer, white_pantyhose, pink_kimono, red_bow, long_sleeves, floral_print, short_kimono, frills, fur_collar, pink_skirt, print_kimono, parted_lips, dog, white_background, animal_on_head, pink_flower |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_shirt | blush | kindergarten_uniform | looking_at_viewer | solo | long_sleeves | yellow_bowtie | parted_lips | simple_background | upper_body | :o | white_background | white_sailor_collar | jingle_bell | tail_ribbon | white_thighhighs | yellow_skirt | chibi | own_hands_together | pleated_skirt | full_body | red_bow | tail_bell | tail_bow | bowtie | red_eyes | twitter_username | yellow_bow | black_footwear | blue_background | standing_on_one_leg | black_headwear | black_skirt | hair_bow | retrofit_(azur_lane) | short_sleeves | suspender_skirt | white_shirt | beret | black_bow | two_side_up | white_pantyhose | collared_shirt | dress_shirt | neck_ribbon | pink_bow | pink_ribbon | striped_bow | anchor | open_mouth | shoes | school_uniform | legs | 1boy | hetero | loli | flat_chest | nipples | penis | spread_legs | navel | sex | solo_focus | tears | vaginal | nude | thighhighs | bar_censor | heart-shaped_pupils | missionary | moaning | mosaic_censoring | on_back | bell | cum_in_pussy | torso_grab | obi | wide_sleeves | hair_flower | pink_kimono | floral_print | short_kimono | frills | fur_collar | pink_skirt | print_kimono | dog | animal_on_head | pink_flower |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------|:-----------------------|:--------------------|:-------|:---------------|:----------------|:--------------|:--------------------|:-------------|:-----|:-------------------|:----------------------|:--------------|:--------------|:-------------------|:---------------|:--------|:---------------------|:----------------|:------------|:----------|:------------|:-----------|:---------|:-----------|:-------------------|:-------------|:-----------------|:------------------|:----------------------|:-----------------|:--------------|:-----------|:-----------------------|:----------------|:------------------|:--------------|:--------|:------------|:--------------|:------------------|:-----------------|:--------------|:--------------|:-----------|:--------------|:--------------|:---------|:-------------|:--------|:-----------------|:-------|:-------|:---------|:-------|:-------------|:----------|:--------|:--------------|:--------|:------|:-------------|:--------|:----------|:-------|:-------------|:-------------|:----------------------|:-------------|:----------|:-------------------|:----------|:-------|:---------------|:-------------|:------|:---------------|:--------------|:--------------|:---------------|:---------------|:---------|:-------------|:-------------|:---------------|:------|:-----------------|:--------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | | X | X | X | | | | X | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | X | X | | | | X | | | X | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | | X | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 20 |  |  |  |  |  | X | | X | | X | X | | | X | | | | X | | X | X | | | | | X | X | | X | X | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | X | | X | X | | | | X | | | X | | X | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | X | X | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 7 | 24 |  |  |  |  |  | X | | X | | X | X | X | | X | | | | X | | X | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_88 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1337055356.0
num_examples: 260533
download_size: 1369319733
dataset_size: 1337055356.0
---
# Dataset Card for "chunk_88"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jiandong/crimson-cve-mapping | ---
license: unknown
dataset_info:
features:
- name: cve_id
dtype: string
- name: cve_mapping
struct:
- name: cve_id
dtype: string
- name: explaination
dtype: string
- name: exploit_techniques
list:
- name: exploit_method
dtype: string
- name: exploitation_technique
struct:
- name: id
dtype: string
- name: name
dtype: string
- name: functionality
struct:
- name: gained_functionality
sequence: string
- name: primary_impact
list:
- name: id
dtype: string
- name: name
dtype: string
- name: secondary_impact
list:
- name: id
dtype: string
- name: name
dtype: string
- name: vulnerability_type
struct:
- name: exploitation_techniques
list:
- name: id
dtype: string
- name: name
dtype: string
- name: primary_impact
list:
- name: id
dtype: string
- name: name
dtype: string
- name: secondary_impact
list:
- name: id
dtype: string
- name: name
dtype: string
- name: type
dtype: string
- name: description
dtype: string
- name: cvss_version
dtype: string
- name: cvss_severity
dtype: string
- name: cvss_base_score
dtype: float64
- name: related_attcks
list:
- name: id
dtype: string
- name: name
dtype: string
- name: gt_attcks
list:
- name: id
dtype: string
- name: name
dtype: string
- name: attck_patterns
list:
- name: brief
struct:
- name: long_text
dtype: string
- name: short_text
dtype: string
- name: description
dtype: string
- name: metadata
struct:
- name: external_id
dtype: string
- name: id
dtype: string
- name: kill_chain_phases
list:
- name: kill_chain_name
dtype: string
- name: phase_name
dtype: string
- name: name
dtype: string
- name: x_mitre_domains
sequence: string
- name: x_mitre_is_subtechnique
dtype: bool
- name: x_mitre_platforms
sequence: string
- name: x_mitre_tactic_type
sequence: string
- name: relationships
list:
- name: id
dtype: string
- name: relationship_type
dtype: string
- name: source_ref
dtype: string
- name: target_ref
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- name: related_attck_patterns
list:
- name: brief
struct:
- name: long_text
dtype: string
- name: short_text
dtype: string
- name: description
dtype: string
- name: metadata
struct:
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dtype: string
- name: id
dtype: string
- name: kill_chain_phases
list:
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dtype: string
- name: phase_name
dtype: string
- name: name
dtype: string
- name: x_mitre_domains
sequence: string
- name: x_mitre_is_subtechnique
dtype: bool
- name: x_mitre_platforms
sequence: string
- name: x_mitre_tactic_type
sequence: string
- name: relationships
list:
- name: id
dtype: string
- name: relationship_type
dtype: string
- name: source_ref
dtype: string
- name: target_ref
dtype: string
- name: count_attck_patterns
dtype: int64
- name: count_related_attck_patterns
dtype: int64
splits:
- name: train
num_bytes: 31954455.207373273
num_examples: 1215
- name: test
num_bytes: 7995188.792626728
num_examples: 304
download_size: 2567859
dataset_size: 39949644.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
SKSowe/Models_Downloads_and_Likes_Metrics | ---
license: apache-2.0
language:
- ff
- wo
pretty_name: DBIS
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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]
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## Dataset Card Contact
[More Information Needed] |
Writo/realestate_data | ---
license: mit
---
|
KAUE2006/PatatiPauloTeixeira | ---
license: openrail
---
|
autoevaluate/autoeval-staging-eval-project-adversarial_qa-1cd241d3-12195624 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- adversarial_qa
eval_info:
task: extractive_question_answering
model: deepset/roberta-large-squad2
metrics: []
dataset_name: adversarial_qa
dataset_config: adversarialQA
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/roberta-large-squad2
* Dataset: adversarial_qa
* Config: adversarialQA
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ceyda](https://huggingface.co/ceyda) for evaluating this model. |
multimodalart/lora-ease-helper | ---
license: mit
---
|
pfcheng123/test2 | ---
license: other
---
|
Hack90/ncbi_genbank_part_23 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
- name: name
dtype: string
- name: description
dtype: string
- name: features
dtype: int64
- name: seq_length
dtype: int64
splits:
- name: train
num_bytes: 32376257548
num_examples: 660870
download_size: 14556362694
dataset_size: 32376257548
---
# Dataset Card for "ncbi_genbank_part_23"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
harshinde/Covid-19_country_vaccinations | ---
license: mit
language:
- en
--- |
Alphonsce/Fef_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 5469372.0
num_examples: 21
download_size: 5470869
dataset_size: 5469372.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# That's photos of my dog :) |
Copninich/PongSawaML | ---
license: apache-2.0
language:
- th
tags:
- history
pretty_name: s
task_categories:
- summarization
size_categories:
- 1K<n<10K
---
# PongSawaML
PongsawaM(L) is a Thai annal Ayutthaya, Thonburi, and Rattakosin (Rama I). This annal writed by Dan Beach Bradley an American Protestant missionary
|
davanstrien/raw-tldr-dataset | ---
dataset_info:
features:
- name: datasetId
dtype: string
- name: author
dtype: string
- name: last_modified
dtype: timestamp[us, tz=UTC]
- name: downloads
dtype: int64
- name: likes
dtype: int64
- name: tags
sequence: string
- name: task_categories
sequence: string
- name: createdAt
dtype: timestamp[us, tz=UTC]
- name: card
dtype: string
- name: parsed_card
dtype: string
- name: length
dtype: int64
- name: input
dtype: string
- name: generation_model
sequence: string
- name: generation_prompt
sequence: string
- name: raw_generation_responses
sequence: string
- name: generations
sequence: string
splits:
- name: train
num_bytes: 19291771
num_examples: 500
download_size: 7858010
dataset_size: 19291771
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_qqp_clause_final_though_but | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 914
num_examples: 5
- name: test
num_bytes: 10119
num_examples: 42
- name: train
num_bytes: 9114
num_examples: 37
download_size: 23042
dataset_size: 20147
---
# Dataset Card for "MULTI_VALUE_qqp_clause_final_though_but"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
iwasjohnlennon/JayAraeEssexArchive | ---
task_categories:
- text-classification
language:
- en
tags:
- medical
- music
- biology
- chemistry
- art
- climate
size_categories:
- 100K<n<1M
---
300-400 hours of video turned into text + his twitter tweets using whisper large-v2 model for ai transcription. Jay Essex has made 3 books and about 1200 videos which only about 800 can be found online unless someone has a back up storage for past youtube videos as his old youtube channel got removed before it was fully backed up by a fan. This goes indepth on a variety of topics. Alot of it has never been shared before here. Some include DNA ICUC (Evolution), Source energy, aliens, extraterrestrials, spiritual awakening, psychic development, creations history, earths history, creations future, earths future, who was god, talk about angels, what types of spirit/souls are there, how to awaken metaphysically, tools that help the psychic abilitys develop, talk about aliens like the annunaki and more, facts about dragons and unicorns spirit, crystals and stones, divination tools, spirit guides, and a whole lot more.
For list of problems with this data and how it was made go here https://www.youtube.com/watch?v=TBUDd3EVX6A
Here are even more topics he covers, although some topics might only be found in his books and atm i havent included the books with this dataset. The New Universal Alliance, Drachk, N'Antids, Solar System, Alliance of Planets, Arae, Lilly, source field, akashic records, energy healing, star essenite, earthquake, tectonic plate splits, abuse system, freedom, trump, joe biden, government, military, et, alien hybrid, space travel, time travel, universe, law of attraction (myth), metaphysical, self awareness, energy flowing, flow state, relaxation tips, guided meditations, religions, jesus, qeeg test results, numerology, spirit core, angels, dreams, stone energy, Dreams, Visions, Deja-Vu, Spirit Guides (w/Ear Ringing), Ghost, Demons, Exorcisms, Energetic Imprint Recordings, Dousing Rods, Pendulums, Kinesiology, Pictures, Dimensions, Barriers, Mirrors, Ouija Boards, Darting Black Spots in the Corners of Your Eyes, Sage, Spontaneous Combustion, spirit attack, spirit protection, flow within to flow without outwards, the spiritual foundation, thespiritualfoundation, ghandi reincarnated, reincarnation, past lifes, third eye, pineal gland, nervous system, george washington, Tomoe Gozen, Johann Sebastian Bach, color therapy, android, cyborg, telekinesis, kundlini awakening, gaia |
BigFatDoughtnut/henryk-sienkiewicz-w-pustyni-i-w-puszczy | ---
license: mit
---
|
male-2/evaluation_align_v1__store_baehanjin_work_ml-training_v3_merged-public | ---
dataset_info:
features:
- name: Aspect
dtype: string
- name: Sub-Aspect
dtype: string
- name: Query
dtype: string
- name: type
dtype: string
- name: Dialogue
dtype: string
- name: Response
dtype: string
splits:
- name: train
num_bytes: 475
num_examples: 1
download_size: 5647
dataset_size: 475
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "evaluation_align_v1__store_baehanjin_work_ml-training_v3_merged-public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
avitri/eng-fra | ---
license: mit
---
|
classla/ParlaSpeech-HR | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: text_normalised
dtype: string
- name: words
list:
- name: char_e
dtype: int64
- name: char_s
dtype: int64
- name: time_e
dtype: float64
- name: time_s
dtype: float64
- name: audio_length
dtype: float64
- name: date
dtype: string
- name: speaker_name
dtype: string
- name: speaker_gender
dtype: string
- name: speaker_birth
dtype: string
- name: speaker_party
dtype: string
- name: party_orientation
dtype: string
- name: party_status
dtype: string
splits:
- name: train
num_bytes: 162874686121.866
num_examples: 867581
download_size: 179092718936
dataset_size: 162874686121.866
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# The Croatian Parliamentary Spoken Dataset ParlaSpeech-HR 2.0
http://hdl.handle.net/11356/1914
The ParlaSpeech-HR dataset is built from the transcripts of parliamentary proceedings available in the Croatian part of the ParlaMint corpus, and the parliamentary recordings available from the Croatian Parliament's YouTube channel.
The corpus consists of audio segments that correspond to specific sentences in the transcripts. The transcript contains word-level alignments to the recordings, each instance consisting of character and millisecond start and end offsets, allowing for simple further segmentation of long sentences into shorter segments for ASR and other memory-sensitive applications. Sequences longer than 30 seconds have already been removed from this dataset, which should allow for a simple usage on most modern GPUs.
Each segment has an identifier reference to the ParlaMint 4.0 corpus (http://hdl.handle.net/11356/1859) via the utterance ID and character offsets.
While in the original dataset all the speaker information from the ParlaMint corpus is available via the `speaker_info` attribute, in the HuggingFace version only a subset of metadata is available, namely: the date, the name of the speaker, their gender, year of birth, party affiliation at that point in time, status of the party at that point in time (coalition or opposition), and party orientation (left, right, centre etc.).
Different to the original dataset, this version has also a `text_normalised` attribute, which contains the text with parliamentary comments (`[[Applause]]` and similar) removed.
If you use the dataset, please cite the following paper:
```
@inproceedings{ljubesic-etal-2022-parlaspeech,
title = "{P}arla{S}peech-{HR} - a Freely Available {ASR} Dataset for {C}roatian Bootstrapped from the {P}arla{M}int Corpus",
author = "Ljube{\v{s}}i{\'c}, Nikola and
Kor{\v{z}}inek, Danijel and
Rupnik, Peter and
Jazbec, Ivo-Pavao",
editor = "Fi{\v{s}}er, Darja and
Eskevich, Maria and
Lenardi{\v{c}}, Jakob and
de Jong, Franciska",
booktitle = "Proceedings of the Workshop ParlaCLARIN III within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.parlaclarin-1.16",
pages = "111--116",
}
```
|
AdapterOcean/med_alpaca_standardized_cluster_7_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 13037706
num_examples: 22584
download_size: 6735945
dataset_size: 13037706
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_7_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
augtoma/medmcqa | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: cop
dtype:
class_label:
names:
'0': a
'1': b
'2': c
'3': d
- name: choice_type
dtype: string
- name: exp
dtype: string
- name: subject_name
dtype: string
- name: topic_name
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer_idx
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 136988451
num_examples: 182822
- name: test
num_bytes: 2350095
num_examples: 4183
download_size: 90978864
dataset_size: 139338546
---
# Dataset Card for "medmcqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Bepitic/DM-Description-Action | ---
license: gpl
---
|
YaTharThShaRma999/autotrain-data-flant5finetune | ---
task_categories:
- summarization
---
# AutoTrain Dataset for project: flant5finetune
## Dataset Description
This dataset has been automatically processed by AutoTrain for project flant5finetune.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Describe this image of a serene lake and turn it into a starry night scene.",
"target": "Tools: Image captioning, Image editing\nInput: {Image}, turn it into a starry night scene\n"
},
{
"text": "Generate a melodic rock track for an energetic outdoor adventure video.",
"target": "Tools: Music generation\nInput: melodic rock track for an energetic outdoor adventure video\n"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 23 |
| valid | 6 |
|
tyzhu/lmind_hotpot_train5000_eval5000_v1_ic_qa | ---
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: train_ic_qa
path: data/train_ic_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: eval_ic_qa
path: data/eval_ic_qa-*
- split: all_docs
path: data/all_docs-*
- split: all_docs_eval
path: data/all_docs_eval-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
splits:
- name: train_qa
num_bytes: 864508
num_examples: 5000
- name: train_recite_qa
num_bytes: 5350190
num_examples: 5000
- name: train_ic_qa
num_bytes: 5345190
num_examples: 5000
- name: eval_qa
num_bytes: 813536
num_examples: 5000
- name: eval_recite_qa
num_bytes: 5394796
num_examples: 5000
- name: eval_ic_qa
num_bytes: 5345190
num_examples: 5000
- name: all_docs
num_bytes: 8524332
num_examples: 18224
- name: all_docs_eval
num_bytes: 8523131
num_examples: 18224
- name: train
num_bytes: 5345190
num_examples: 5000
- name: validation
num_bytes: 5345190
num_examples: 5000
download_size: 30643792
dataset_size: 50851253
---
# Dataset Card for "lmind_hotpot_train5000_eval5000_v1_ic_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-one-sec-cv12-each-chunk-uniq/chunk_10 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1037767380.0
num_examples: 202215
download_size: 1059033634
dataset_size: 1037767380.0
---
# Dataset Card for "chunk_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/talulah_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Talulah/タルラ/塔露拉 (Arknights)
This is the dataset of Talulah/タルラ/塔露拉 (Arknights), containing 218 images and their tags.
The core tags of this character are `horns, grey_hair, dragon_horns, hair_intakes, short_hair, breasts, dragon_girl, hair_between_eyes`, 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 | 218 | 392.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/talulah_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 218 | 326.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/talulah_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 544 | 628.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/talulah_arknights/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/talulah_arknights',
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 | 20 |  |  |  |  |  | 1girl, solo, long_sleeves, black_ascot, looking_at_viewer, black_dress, upper_body, frills, white_shirt, armband, closed_mouth, simple_background, white_background, puffy_sleeves, medium_breasts |
| 1 | 11 |  |  |  |  |  | 1girl, black_dress, long_sleeves, solo, dragon_tail, looking_at_viewer, standing, holding_sword, fire, feet_out_of_frame, sky, ascot, black_footwear, outdoors, skirt, armband, building, full_body, high_heel_boots, pantyhose, shirt |
| 2 | 9 |  |  |  |  |  | black_dress, deer_antlers, deer_ears, deer_girl, long_sleeves, shirt, long_hair, smile, 2girls, closed_eyes, open_mouth, simple_background, solo_focus, white_background, epaulettes, pinafore_dress, tail, 1girl, black_neckerchief, holding |
| 3 | 14 |  |  |  |  |  | looking_at_viewer, bare_shoulders, 1girl, dragon_tail, navel, outdoors, solo, day, stomach, thighs, black_bikini, blue_sky, medium_breasts, sunglasses, water, beach, long_hair, sitting, cleavage, cloud, grey_eyes, open_mouth, smile, alternate_costume, bare_arms, bare_legs, eyewear_on_head, large_breasts, ocean |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | long_sleeves | black_ascot | looking_at_viewer | black_dress | upper_body | frills | white_shirt | armband | closed_mouth | simple_background | white_background | puffy_sleeves | medium_breasts | dragon_tail | standing | holding_sword | fire | feet_out_of_frame | sky | ascot | black_footwear | outdoors | skirt | building | full_body | high_heel_boots | pantyhose | shirt | deer_antlers | deer_ears | deer_girl | long_hair | smile | 2girls | closed_eyes | open_mouth | solo_focus | epaulettes | pinafore_dress | tail | black_neckerchief | holding | bare_shoulders | navel | day | stomach | thighs | black_bikini | blue_sky | sunglasses | water | beach | sitting | cleavage | cloud | grey_eyes | alternate_costume | bare_arms | bare_legs | eyewear_on_head | large_breasts | ocean |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:--------------|:--------------------|:--------------|:-------------|:---------|:--------------|:----------|:---------------|:--------------------|:-------------------|:----------------|:-----------------|:--------------|:-----------|:----------------|:-------|:--------------------|:------|:--------|:-----------------|:-----------|:--------|:-----------|:------------|:------------------|:------------|:--------|:---------------|:------------|:------------|:------------|:--------|:---------|:--------------|:-------------|:-------------|:-------------|:-----------------|:-------|:--------------------|:----------|:-----------------|:--------|:------|:----------|:---------|:---------------|:-----------|:-------------|:--------|:--------|:----------|:-----------|:--------|:------------|:--------------------|:------------|:------------|:------------------|:----------------|:--------|
| 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | X | | X | X | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | X | | | X | | | | | | X | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | X | X | | | X | | | | | | | | | | X | X | | | | | | | | X | | | | | | | | | | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
jxm/mpqa | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 263258
num_examples: 8603
- name: test
num_bytes: 62502
num_examples: 2000
- name: dev
num_bytes: 7835
num_examples: 256
download_size: 0
dataset_size: 333595
---
# Dataset Card for "mpqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
louisbrulenaudet/cgi | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- tax
- llm
- fiscal
- cgi
- Code Général des Impôts
source_datasets:
- original
pretty_name: Code Général des Impôts (CGI)
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code Général des Impôts, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for tax practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code Général des Impôts, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/cgi}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
autoevaluate/autoeval-eval-acronym_identification-default-ef327c-37654145039 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- acronym_identification
eval_info:
task: entity_extraction
model: lewtun/autotrain-acronym-identification-7324788
metrics: ['angelina-wang/directional_bias_amplification']
dataset_name: acronym_identification
dataset_config: default
dataset_split: train
col_mapping:
tokens: tokens
tags: labels
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@qingxuwenli](https://huggingface.co/qingxuwenli) for evaluating this model. |
marcelhuber/downprojection_images | ---
task_categories:
- image-classification
language:
- en
tags:
- medical
pretty_name: downprojection_images
size_categories:
- 1K<n<10K
---
# Dataset Structure
This dataset contains images categorized into different classes for medical image analysis. The dataset is organized as follows:
- `CNV`: Contains 250 images and 250 w-vector files.
- `DME`: Contains 250 images and 250 w-vector files.
- `DRUSEN`: Contains 250 images and 250 w-vector files.
- `NORMAL`: Contains 250 images and 250 w-vector files.
## Usage
This dataset can be used for tasks such as classification, image recognition, and medical analysis. The provided class subdirectories indicate the different categories for the images.
The purpose of this dataset is to be used in an interactive dash-map where the latent codes of these images have been downprojected using either PCA or t-SNE. Each individual point in the map corresponds to an original image, which is displayed when hovered over. Here is the reposity: https://github.com/marceljhuber/Dash-Downprojection-Viewer |
DTU54DL/common-voice-test3k | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Acronym Identification Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- token-classification-other-acronym-identification
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction
---
# Dataset Card for [Dataset Name]
## 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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
DeliberatorArchiver/gi_cutscn_new | ---
license: cc-by-nc-nd-4.0
language:
- zh
- en
- ja
- ko
viewer: false
---
# gi_cutscn_new
This repository contains cut-scene video files from a certain anime game (a.k.a. GI).
  
## Disclaimer
**This resource is released for educational or research purposes only. Copyrights and other rights to this resource belong to their respective copyright holders.**
## About
All cut-scene video files are encoded using HLS streaming technology.
### Details
The original files were extracted directly from the game.
The original file contains one video track and four audio tracks (Chinese, English, Japanese, and Korean). The video track is encoded in VP9 and the audio track is encoded in CRI HCA. All video and audio tracks are packed in CRI USM.
### Details of encoded video files
See [Gist](https://gist.github.com/daydreamer-json/8e0f2bf2025db209a9727ad4f2dd983a) for the parameters used during encoding.
|Type|Level|Audio|Resolution|Codec|Color|Max Bitrate|Avg Bitrate|
|---|---|---|---|---|---|---|---|
|Video|L-5|L-2|1920 x 1080|VP9|YUV 4:2:0 8bit BT.601|Copy|Copy|
|Video|L-4|L-1|1920 x 1080|HEVC|YUV 4:2:0 8bit BT.709|12800 kbps|9600 kbps|
|Video|L-3|L-1|1280 x 720|HEVC|YUV 4:2:0 8bit BT.709|6400 kbps|4800 kbps|
|Video|L-2|L-0|854 x 480|HEVC|YUV 4:2:0 8bit BT.709|3600 kbps|2400 kbps|
|Video|L-1|L-0|640 x 360|HEVC|YUV 4:2:0 8bit BT.709|2400 kbps|1600 kbps|
|Video|L-0|L-0|426 x 240|HEVC|YUV 4:2:0 8bit BT.709|1600 kbps|800 kbps|
|Type|Level|Resolution|Codec|Max Bitrate|Avg Bitrate|
|---|---|---|---|---|---|
|Audio|L-2|16bit 48kHz 2ch|FLAC|1536 kbps|Lossless|
|Audio|L-1|16bit 48kHz 2ch|AAC|? kbps|256 kbps|
|Audio|L-0|16bit 48kHz 2ch|AAC|? kbps|128 kbps|
## How to watch
To watch HLS (HTTP Live Streaming) media manifest files, you will need a player that supports HLS (MPEG-TS and fMP4) demux and can decode various video and audio formats.
### For iOS / iPadOS / macOS:
You can open it from the **"Vidstack Player"** link.
### For Android / Windows / Linux:
The best thing to do is to use a player that can play HLS, such as mpv player, mpv.net, or VLC. Use the **"Raw"** link to watch.
Another way is to use hls.js to play HLS media files in browsers that support the Media Source Extensions API, such as Chrome, Firefox, and Edge. You can open it from the **"Vidstack Player"** link.
## Cut-scene Video Links
### See [Links page](lists.md) |
Lostkyd/InstrucDataPDF | ---
dataset_info:
features:
- name: Instruction
dtype: string
- name: Input
dtype: string
- name: Output
dtype: string
splits:
- name: train
num_bytes: 335269
num_examples: 87
download_size: 74238
dataset_size: 335269
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
bigcode/santacoder-token-usage | ---
dataset_info:
features:
- name: token
dtype: int64
- name: Java
dtype: int64
- name: JavaScript
dtype: int64
- name: Python
dtype: int64
splits:
- name: train
num_bytes: 1571808
num_examples: 49119
download_size: 1165252
dataset_size: 1571808
---
# Dataset Card for "santacoder-token-usage"
Token usage count per language when tokenizing the `"bigcode/stack-dedup-alt-comments"` dataset with the `santacoder` tokenizer. There are less tokens than in the tokenizer because of vocabulary mismatch between the datasets used to train the tokenizer and the ones that ended up being used to train the model.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JovialValley/broadclass_totalMapped3 | ---
dataset_info:
features:
- name: input_values
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 109539072
num_examples: 390
- name: test
num_bytes: 27914744
num_examples: 97
download_size: 138277700
dataset_size: 137453816
---
# Dataset Card for "broadclass_totalMapped3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mlenjoyneer/RuTextSegWiki | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- ru
size_categories:
- 10K<n<100K
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
---
# Dataset Card
## 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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
### Dataset Summary
Dataset for automatic text segmentation of Russian wiki. Text corpora based on May 2023 Wikipedia dump. Markup was generated automatically based on 2 methods: taking texts with ready division into paragraphs and random joining parts of different texts.
### Supported Tasks and Leaderboards
Dataset designed for text segmentation task.
### Languages
The dataset is in Russian.
### Usage
```python
from datasets import load_dataset
dataset = load_dataset('mlenjoyneer/RuTextSegWiki')
```
### Other datasets
mlenjoyneer/RuTextSegNews - similar dataset based on news corpora
## Dataset Structure
### Data Instances
For each instance, there is a list of strings for text sentences, a list of ints for labels (1 is new topic starting and 0 is previous topic continuation) and a string for sample generation method (base or random_joining).
### Data Splits
| Dataset Split | Number of Instances in Split |
|:---------|:---------|
| Train | 20000 |
| Test | 4000 |
## Additional Information
### Licensing Information
In progress
### Citation Information
```bibtex
In progress
```
|
philschmid/chip2_oasst1_en_code | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 4934232
num_examples: 4687
download_size: 1866641
dataset_size: 4934232
---
# Dataset Card for "chip2_oasst1_en_code"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_maldv__electric-sheep-7b-alpha | ---
pretty_name: Evaluation run of maldv/electric-sheep-7b-alpha
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [maldv/electric-sheep-7b-alpha](https://huggingface.co/maldv/electric-sheep-7b-alpha)\
\ 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_maldv__electric-sheep-7b-alpha\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-16T15:55:01.323956](https://huggingface.co/datasets/open-llm-leaderboard/details_maldv__electric-sheep-7b-alpha/blob/main/results_2024-03-16T15-55-01.323956.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\": 0.5053488597835195,\n\
\ \"acc_stderr\": 0.0341231358277047,\n \"acc_norm\": 0.5096581303380655,\n\
\ \"acc_norm_stderr\": 0.03483649987748136,\n \"mc1\": 0.32068543451652387,\n\
\ \"mc1_stderr\": 0.0163391703732809,\n \"mc2\": 0.48261245911723205,\n\
\ \"mc2_stderr\": 0.015170591571160566\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5051194539249146,\n \"acc_stderr\": 0.014610624890309157,\n\
\ \"acc_norm\": 0.5486348122866894,\n \"acc_norm_stderr\": 0.014542104569955267\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5676160127464649,\n\
\ \"acc_stderr\": 0.004943945069611453,\n \"acc_norm\": 0.7642899820752838,\n\
\ \"acc_norm_stderr\": 0.004235743182042561\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.04065771002562605,\n\
\ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.04065771002562605\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.47,\n\
\ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5660377358490566,\n \"acc_stderr\": 0.03050329201334259,\n\
\ \"acc_norm\": 0.5660377358490566,\n \"acc_norm_stderr\": 0.03050329201334259\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5416666666666666,\n\
\ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n\
\ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\
: 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.47398843930635837,\n\
\ \"acc_stderr\": 0.03807301726504513,\n \"acc_norm\": 0.47398843930635837,\n\
\ \"acc_norm_stderr\": 0.03807301726504513\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.04440521906179328,\n\
\ \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.04440521906179328\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.48936170212765956,\n \"acc_stderr\": 0.03267862331014063,\n\
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.03267862331014063\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\
\ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\
\ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.35978835978835977,\n \"acc_stderr\": 0.02471807594412928,\n \"\
acc_norm\": 0.35978835978835977,\n \"acc_norm_stderr\": 0.02471807594412928\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.040061680838488774,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.040061680838488774\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6193548387096774,\n\
\ \"acc_stderr\": 0.02762171783290703,\n \"acc_norm\": 0.6193548387096774,\n\
\ \"acc_norm_stderr\": 0.02762171783290703\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3891625615763547,\n \"acc_stderr\": 0.03430462416103873,\n\
\ \"acc_norm\": 0.3891625615763547,\n \"acc_norm_stderr\": 0.03430462416103873\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6787878787878788,\n \"acc_stderr\": 0.03646204963253812,\n\
\ \"acc_norm\": 0.6787878787878788,\n \"acc_norm_stderr\": 0.03646204963253812\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6262626262626263,\n \"acc_stderr\": 0.03446897738659333,\n \"\
acc_norm\": 0.6262626262626263,\n \"acc_norm_stderr\": 0.03446897738659333\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7409326424870466,\n \"acc_stderr\": 0.031618779179354115,\n\
\ \"acc_norm\": 0.7409326424870466,\n \"acc_norm_stderr\": 0.031618779179354115\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.44871794871794873,\n \"acc_stderr\": 0.02521731518484648,\n\
\ \"acc_norm\": 0.44871794871794873,\n \"acc_norm_stderr\": 0.02521731518484648\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.27037037037037037,\n \"acc_stderr\": 0.027080372815145668,\n \
\ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.027080372815145668\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.49159663865546216,\n \"acc_stderr\": 0.032473902765696686,\n\
\ \"acc_norm\": 0.49159663865546216,\n \"acc_norm_stderr\": 0.032473902765696686\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.23178807947019867,\n \"acc_stderr\": 0.03445406271987054,\n \"\
acc_norm\": 0.23178807947019867,\n \"acc_norm_stderr\": 0.03445406271987054\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.6587155963302752,\n \"acc_stderr\": 0.020328612816592446,\n \"\
acc_norm\": 0.6587155963302752,\n \"acc_norm_stderr\": 0.020328612816592446\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.39351851851851855,\n \"acc_stderr\": 0.03331747876370312,\n \"\
acc_norm\": 0.39351851851851855,\n \"acc_norm_stderr\": 0.03331747876370312\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6568627450980392,\n \"acc_stderr\": 0.033321399446680854,\n \"\
acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.033321399446680854\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6455696202531646,\n \"acc_stderr\": 0.03113730429718582,\n \
\ \"acc_norm\": 0.6455696202531646,\n \"acc_norm_stderr\": 0.03113730429718582\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6188340807174888,\n\
\ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n\
\ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5572519083969466,\n \"acc_stderr\": 0.0435644720266507,\n\
\ \"acc_norm\": 0.5572519083969466,\n \"acc_norm_stderr\": 0.0435644720266507\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6528925619834711,\n \"acc_stderr\": 0.04345724570292535,\n \"\
acc_norm\": 0.6528925619834711,\n \"acc_norm_stderr\": 0.04345724570292535\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.04668408033024931,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.04668408033024931\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6319018404907976,\n \"acc_stderr\": 0.03789213935838396,\n\
\ \"acc_norm\": 0.6319018404907976,\n \"acc_norm_stderr\": 0.03789213935838396\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\
\ \"acc_stderr\": 0.044642857142857116,\n \"acc_norm\": 0.33035714285714285,\n\
\ \"acc_norm_stderr\": 0.044642857142857116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.046897659372781335,\n\
\ \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.046897659372781335\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7735042735042735,\n\
\ \"acc_stderr\": 0.027421007295392916,\n \"acc_norm\": 0.7735042735042735,\n\
\ \"acc_norm_stderr\": 0.027421007295392916\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.6845466155810983,\n\
\ \"acc_stderr\": 0.01661750173876339,\n \"acc_norm\": 0.6845466155810983,\n\
\ \"acc_norm_stderr\": 0.01661750173876339\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5549132947976878,\n \"acc_stderr\": 0.02675625512966377,\n\
\ \"acc_norm\": 0.5549132947976878,\n \"acc_norm_stderr\": 0.02675625512966377\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2122905027932961,\n\
\ \"acc_stderr\": 0.013676644685831718,\n \"acc_norm\": 0.2122905027932961,\n\
\ \"acc_norm_stderr\": 0.013676644685831718\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5098039215686274,\n \"acc_stderr\": 0.028624412550167958,\n\
\ \"acc_norm\": 0.5098039215686274,\n \"acc_norm_stderr\": 0.028624412550167958\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5787781350482315,\n\
\ \"acc_stderr\": 0.028043399858210628,\n \"acc_norm\": 0.5787781350482315,\n\
\ \"acc_norm_stderr\": 0.028043399858210628\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5709876543209876,\n \"acc_stderr\": 0.027538925613470863,\n\
\ \"acc_norm\": 0.5709876543209876,\n \"acc_norm_stderr\": 0.027538925613470863\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3546099290780142,\n \"acc_stderr\": 0.028538650028878634,\n \
\ \"acc_norm\": 0.3546099290780142,\n \"acc_norm_stderr\": 0.028538650028878634\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3539765319426336,\n\
\ \"acc_stderr\": 0.01221350473173164,\n \"acc_norm\": 0.3539765319426336,\n\
\ \"acc_norm_stderr\": 0.01221350473173164\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121596,\n\
\ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121596\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5196078431372549,\n \"acc_stderr\": 0.020212274976302954,\n \
\ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.020212274976302954\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6090909090909091,\n\
\ \"acc_stderr\": 0.046737523336702384,\n \"acc_norm\": 0.6090909090909091,\n\
\ \"acc_norm_stderr\": 0.046737523336702384\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5387755102040817,\n \"acc_stderr\": 0.031912820526692774,\n\
\ \"acc_norm\": 0.5387755102040817,\n \"acc_norm_stderr\": 0.031912820526692774\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6965174129353234,\n\
\ \"acc_stderr\": 0.03251006816458618,\n \"acc_norm\": 0.6965174129353234,\n\
\ \"acc_norm_stderr\": 0.03251006816458618\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n\
\ \"acc_stderr\": 0.03889951252827217,\n \"acc_norm\": 0.4819277108433735,\n\
\ \"acc_norm_stderr\": 0.03889951252827217\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.036155076303109365,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.036155076303109365\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32068543451652387,\n\
\ \"mc1_stderr\": 0.0163391703732809,\n \"mc2\": 0.48261245911723205,\n\
\ \"mc2_stderr\": 0.015170591571160566\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7032359905288083,\n \"acc_stderr\": 0.012839239695202032\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2934040940106141,\n \
\ \"acc_stderr\": 0.012541830815461487\n }\n}\n```"
repo_url: https://huggingface.co/maldv/electric-sheep-7b-alpha
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_16T15_55_01.323956
path:
- '**/details_harness|arc:challenge|25_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|gsm8k|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hellaswag|10_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-16T15-55-01.323956.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-16T15-55-01.323956.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- '**/details_harness|winogrande|5_2024-03-16T15-55-01.323956.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-16T15-55-01.323956.parquet'
- config_name: results
data_files:
- split: 2024_03_16T15_55_01.323956
path:
- results_2024-03-16T15-55-01.323956.parquet
- split: latest
path:
- results_2024-03-16T15-55-01.323956.parquet
---
# Dataset Card for Evaluation run of maldv/electric-sheep-7b-alpha
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [maldv/electric-sheep-7b-alpha](https://huggingface.co/maldv/electric-sheep-7b-alpha) 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_maldv__electric-sheep-7b-alpha",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-16T15:55:01.323956](https://huggingface.co/datasets/open-llm-leaderboard/details_maldv__electric-sheep-7b-alpha/blob/main/results_2024-03-16T15-55-01.323956.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": 0.5053488597835195,
"acc_stderr": 0.0341231358277047,
"acc_norm": 0.5096581303380655,
"acc_norm_stderr": 0.03483649987748136,
"mc1": 0.32068543451652387,
"mc1_stderr": 0.0163391703732809,
"mc2": 0.48261245911723205,
"mc2_stderr": 0.015170591571160566
},
"harness|arc:challenge|25": {
"acc": 0.5051194539249146,
"acc_stderr": 0.014610624890309157,
"acc_norm": 0.5486348122866894,
"acc_norm_stderr": 0.014542104569955267
},
"harness|hellaswag|10": {
"acc": 0.5676160127464649,
"acc_stderr": 0.004943945069611453,
"acc_norm": 0.7642899820752838,
"acc_norm_stderr": 0.004235743182042561
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5197368421052632,
"acc_stderr": 0.04065771002562605,
"acc_norm": 0.5197368421052632,
"acc_norm_stderr": 0.04065771002562605
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5660377358490566,
"acc_stderr": 0.03050329201334259,
"acc_norm": 0.5660377358490566,
"acc_norm_stderr": 0.03050329201334259
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5416666666666666,
"acc_stderr": 0.04166666666666665,
"acc_norm": 0.5416666666666666,
"acc_norm_stderr": 0.04166666666666665
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.47398843930635837,
"acc_stderr": 0.03807301726504513,
"acc_norm": 0.47398843930635837,
"acc_norm_stderr": 0.03807301726504513
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.27450980392156865,
"acc_stderr": 0.04440521906179328,
"acc_norm": 0.27450980392156865,
"acc_norm_stderr": 0.04440521906179328
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252609,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252609
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.48936170212765956,
"acc_stderr": 0.03267862331014063,
"acc_norm": 0.48936170212765956,
"acc_norm_stderr": 0.03267862331014063
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.37719298245614036,
"acc_stderr": 0.04559522141958216,
"acc_norm": 0.37719298245614036,
"acc_norm_stderr": 0.04559522141958216
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.45517241379310347,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.45517241379310347,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.35978835978835977,
"acc_stderr": 0.02471807594412928,
"acc_norm": 0.35978835978835977,
"acc_norm_stderr": 0.02471807594412928
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.040061680838488774,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.040061680838488774
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6193548387096774,
"acc_stderr": 0.02762171783290703,
"acc_norm": 0.6193548387096774,
"acc_norm_stderr": 0.02762171783290703
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3891625615763547,
"acc_stderr": 0.03430462416103873,
"acc_norm": 0.3891625615763547,
"acc_norm_stderr": 0.03430462416103873
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6787878787878788,
"acc_stderr": 0.03646204963253812,
"acc_norm": 0.6787878787878788,
"acc_norm_stderr": 0.03646204963253812
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6262626262626263,
"acc_stderr": 0.03446897738659333,
"acc_norm": 0.6262626262626263,
"acc_norm_stderr": 0.03446897738659333
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7409326424870466,
"acc_stderr": 0.031618779179354115,
"acc_norm": 0.7409326424870466,
"acc_norm_stderr": 0.031618779179354115
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.44871794871794873,
"acc_stderr": 0.02521731518484648,
"acc_norm": 0.44871794871794873,
"acc_norm_stderr": 0.02521731518484648
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.27037037037037037,
"acc_stderr": 0.027080372815145668,
"acc_norm": 0.27037037037037037,
"acc_norm_stderr": 0.027080372815145668
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.49159663865546216,
"acc_stderr": 0.032473902765696686,
"acc_norm": 0.49159663865546216,
"acc_norm_stderr": 0.032473902765696686
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.23178807947019867,
"acc_stderr": 0.03445406271987054,
"acc_norm": 0.23178807947019867,
"acc_norm_stderr": 0.03445406271987054
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.6587155963302752,
"acc_stderr": 0.020328612816592446,
"acc_norm": 0.6587155963302752,
"acc_norm_stderr": 0.020328612816592446
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.39351851851851855,
"acc_stderr": 0.03331747876370312,
"acc_norm": 0.39351851851851855,
"acc_norm_stderr": 0.03331747876370312
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6568627450980392,
"acc_stderr": 0.033321399446680854,
"acc_norm": 0.6568627450980392,
"acc_norm_stderr": 0.033321399446680854
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.6455696202531646,
"acc_stderr": 0.03113730429718582,
"acc_norm": 0.6455696202531646,
"acc_norm_stderr": 0.03113730429718582
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6188340807174888,
"acc_stderr": 0.03259625118416827,
"acc_norm": 0.6188340807174888,
"acc_norm_stderr": 0.03259625118416827
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5572519083969466,
"acc_stderr": 0.0435644720266507,
"acc_norm": 0.5572519083969466,
"acc_norm_stderr": 0.0435644720266507
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6528925619834711,
"acc_stderr": 0.04345724570292535,
"acc_norm": 0.6528925619834711,
"acc_norm_stderr": 0.04345724570292535
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.04668408033024931,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.04668408033024931
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6319018404907976,
"acc_stderr": 0.03789213935838396,
"acc_norm": 0.6319018404907976,
"acc_norm_stderr": 0.03789213935838396
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.33035714285714285,
"acc_stderr": 0.044642857142857116,
"acc_norm": 0.33035714285714285,
"acc_norm_stderr": 0.044642857142857116
},
"harness|hendrycksTest-management|5": {
"acc": 0.6601941747572816,
"acc_stderr": 0.046897659372781335,
"acc_norm": 0.6601941747572816,
"acc_norm_stderr": 0.046897659372781335
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7735042735042735,
"acc_stderr": 0.027421007295392916,
"acc_norm": 0.7735042735042735,
"acc_norm_stderr": 0.027421007295392916
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6845466155810983,
"acc_stderr": 0.01661750173876339,
"acc_norm": 0.6845466155810983,
"acc_norm_stderr": 0.01661750173876339
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5549132947976878,
"acc_stderr": 0.02675625512966377,
"acc_norm": 0.5549132947976878,
"acc_norm_stderr": 0.02675625512966377
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2122905027932961,
"acc_stderr": 0.013676644685831718,
"acc_norm": 0.2122905027932961,
"acc_norm_stderr": 0.013676644685831718
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5098039215686274,
"acc_stderr": 0.028624412550167958,
"acc_norm": 0.5098039215686274,
"acc_norm_stderr": 0.028624412550167958
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5787781350482315,
"acc_stderr": 0.028043399858210628,
"acc_norm": 0.5787781350482315,
"acc_norm_stderr": 0.028043399858210628
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5709876543209876,
"acc_stderr": 0.027538925613470863,
"acc_norm": 0.5709876543209876,
"acc_norm_stderr": 0.027538925613470863
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3546099290780142,
"acc_stderr": 0.028538650028878634,
"acc_norm": 0.3546099290780142,
"acc_norm_stderr": 0.028538650028878634
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3539765319426336,
"acc_stderr": 0.01221350473173164,
"acc_norm": 0.3539765319426336,
"acc_norm_stderr": 0.01221350473173164
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4485294117647059,
"acc_stderr": 0.030211479609121596,
"acc_norm": 0.4485294117647059,
"acc_norm_stderr": 0.030211479609121596
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.020212274976302954,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.020212274976302954
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6090909090909091,
"acc_stderr": 0.046737523336702384,
"acc_norm": 0.6090909090909091,
"acc_norm_stderr": 0.046737523336702384
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5387755102040817,
"acc_stderr": 0.031912820526692774,
"acc_norm": 0.5387755102040817,
"acc_norm_stderr": 0.031912820526692774
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6965174129353234,
"acc_stderr": 0.03251006816458618,
"acc_norm": 0.6965174129353234,
"acc_norm_stderr": 0.03251006816458618
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4819277108433735,
"acc_stderr": 0.03889951252827217,
"acc_norm": 0.4819277108433735,
"acc_norm_stderr": 0.03889951252827217
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.036155076303109365,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.036155076303109365
},
"harness|truthfulqa:mc|0": {
"mc1": 0.32068543451652387,
"mc1_stderr": 0.0163391703732809,
"mc2": 0.48261245911723205,
"mc2_stderr": 0.015170591571160566
},
"harness|winogrande|5": {
"acc": 0.7032359905288083,
"acc_stderr": 0.012839239695202032
},
"harness|gsm8k|5": {
"acc": 0.2934040940106141,
"acc_stderr": 0.012541830815461487
}
}
```
## 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
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[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. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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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
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#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### 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]
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## Glossary [optional]
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## Dataset Card Contact
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CyberHarem/sense_sousounofrieren | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Sense/ゼンゼ (Sousou no Frieren)
This is the dataset of Sense/ゼンゼ (Sousou no Frieren), containing 85 images and their tags.
The core tags of this character are `long_hair, brown_hair, bow, white_bow, hair_between_eyes, very_long_hair, brown_eyes, ahoge`, 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 | 85 | 49.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sense_sousounofrieren/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 85 | 49.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sense_sousounofrieren/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 127 | 77.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sense_sousounofrieren/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/sense_sousounofrieren',
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 | 27 |  |  |  |  |  | 1girl, expressionless, solo, closed_mouth, looking_at_viewer, upper_body, white_bowtie, capelet |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | expressionless | solo | closed_mouth | looking_at_viewer | upper_body | white_bowtie | capelet |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------|:---------------|:--------------------|:-------------|:---------------|:----------|
| 0 | 27 |  |  |  |  |  | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_chargoddard__ypotryll-22b-epoch2-qlora | ---
pretty_name: Evaluation run of chargoddard/ypotryll-22b-epoch2-qlora
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/ypotryll-22b-epoch2-qlora](https://huggingface.co/chargoddard/ypotryll-22b-epoch2-qlora)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_chargoddard__ypotryll-22b-epoch2-qlora\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-26T17:07:11.654928](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__ypotryll-22b-epoch2-qlora/blob/main/results_2023-09-26T17-07-11.654928.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\": 0.39198825503355705,\n\
\ \"em_stderr\": 0.004999564353850857,\n \"f1\": 0.452352139261747,\n\
\ \"f1_stderr\": 0.004826380442768646,\n \"acc\": 0.4085244316417271,\n\
\ \"acc_stderr\": 0.00908196050272276\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.39198825503355705,\n \"em_stderr\": 0.004999564353850857,\n\
\ \"f1\": 0.452352139261747,\n \"f1_stderr\": 0.004826380442768646\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.053828658074298714,\n \
\ \"acc_stderr\": 0.006216328640238116\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7632202052091555,\n \"acc_stderr\": 0.0119475923652074\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/ypotryll-22b-epoch2-qlora
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|arc:challenge|25_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|arc:challenge|25_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_26T17_07_11.654928
path:
- '**/details_harness|drop|3_2023-09-26T17-07-11.654928.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-26T17-07-11.654928.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_26T17_07_11.654928
path:
- '**/details_harness|gsm8k|5_2023-09-26T17-07-11.654928.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-26T17-07-11.654928.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hellaswag|10_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hellaswag|10_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T22:24:06.867434.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T22:33:04.843641.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T22:24:06.867434.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_machine_learning_5
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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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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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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- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_philosophy_5
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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- config_name: harness_hendrycksTest_professional_accounting_5
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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- config_name: harness_hendrycksTest_professional_psychology_5
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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- config_name: harness_hendrycksTest_sociology_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T22_24_06.867434
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T22:24:06.867434.parquet'
- split: 2023_08_18T22_33_04.843641
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T22:33:04.843641.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T22:33:04.843641.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_26T17_07_11.654928
path:
- '**/details_harness|winogrande|5_2023-09-26T17-07-11.654928.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-26T17-07-11.654928.parquet'
- config_name: results
data_files:
- split: 2023_09_26T17_07_11.654928
path:
- results_2023-09-26T17-07-11.654928.parquet
- split: latest
path:
- results_2023-09-26T17-07-11.654928.parquet
---
# Dataset Card for Evaluation run of chargoddard/ypotryll-22b-epoch2-qlora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/ypotryll-22b-epoch2-qlora
- **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 [chargoddard/ypotryll-22b-epoch2-qlora](https://huggingface.co/chargoddard/ypotryll-22b-epoch2-qlora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_chargoddard__ypotryll-22b-epoch2-qlora",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-26T17:07:11.654928](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__ypotryll-22b-epoch2-qlora/blob/main/results_2023-09-26T17-07-11.654928.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": 0.39198825503355705,
"em_stderr": 0.004999564353850857,
"f1": 0.452352139261747,
"f1_stderr": 0.004826380442768646,
"acc": 0.4085244316417271,
"acc_stderr": 0.00908196050272276
},
"harness|drop|3": {
"em": 0.39198825503355705,
"em_stderr": 0.004999564353850857,
"f1": 0.452352139261747,
"f1_stderr": 0.004826380442768646
},
"harness|gsm8k|5": {
"acc": 0.053828658074298714,
"acc_stderr": 0.006216328640238116
},
"harness|winogrande|5": {
"acc": 0.7632202052091555,
"acc_stderr": 0.0119475923652074
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
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